ETL_python_sql.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ETL (PYTHON + SQL)\n",
    "\n",
    "Поключаемся к ClickHouse через Python c использованием библиотеки pandahouse. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandahouse as ph\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "connection_default = {'host': 'https://clickhouse.lab.karpov.courses',\n",
    "                      'database':'default',\n",
    "                      'user':'student', \n",
    "                      'password':'dpo_python_2020'\n",
    "                     }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка соединения: \n",
    "Число строк в таблице"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count()\n",
       "0    85207"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT count()\n",
    "FROM default.ldn_listings\n",
    "\"\"\"\n",
    "df = ph.read_clickhouse(query, connection=connection_default)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85207"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ДРУГОЙ СПОСОБ\n",
    "int(ph.execute(query, connection_default))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выгрузите из таблицы данные о цене в зависимости от типа жилья. Необходимые столбцы:\n",
    "- price – цена за ночь\n",
    "- room_type – тип сдаваемого жилья (доступные варианты: Entire home/apt, Private room, Hotel room, Shared room)\n",
    "\n",
    "Ограничение поставьте на 1000 строк. Результат должен быть отсортирован по возрастанию id.\n",
    "\n",
    "Сгруппируйте полученный датафрейм по типу жилья и посчитайте 75-й перцентиль цены.\n",
    "В качестве ответа впишите полученное значение 75 перцентиля цены для комнат типа Private room. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>room_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>13913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>15400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>17402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>17506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>25023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price        room_type     id\n",
       "0   65.0     Private room  13913\n",
       "1  100.0  Entire home/apt  15400\n",
       "2  300.0  Entire home/apt  17402\n",
       "3  150.0     Private room  17506\n",
       "4   65.0  Entire home/apt  25023"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT \n",
    "    toFloat32OrNull(replaceRegexpAll(price, '[$,]', '')) AS price,\n",
    "    room_type,\n",
    "    id  \n",
    "FROM ldn_listings\n",
    "ORDER BY id ASC \n",
    "LIMIT 1000\n",
    "\"\"\"\n",
    "price = ph.read_clickhouse(query, connection=connection_default)\n",
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Entire home/apt</th>\n",
       "      <td>160.00</td>\n",
       "      <td>463610.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel room</th>\n",
       "      <td>157.00</td>\n",
       "      <td>73302.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private room</th>\n",
       "      <td>70.00</td>\n",
       "      <td>453442.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shared room</th>\n",
       "      <td>69.25</td>\n",
       "      <td>472261.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  price         id\n",
       "room_type                         \n",
       "Entire home/apt  160.00  463610.50\n",
       "Hotel room       157.00   73302.50\n",
       "Private room      70.00  453442.50\n",
       "Shared room       69.25  472261.75"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.groupby('room_type').quantile(q=0.75) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "К данным о цене и типе комнаты дополнительно выгрузите данные о рейтинге жилья (review_scores_rating). \n",
    "В запросе необходимо будет отфильтровать пустые значения review_scores_rating и сконвертировать эту колонку в тип float32.\n",
    "\n",
    "Давайте построим график рассеивания, который покажет зависимость средней оценки от средней цены по типу жилья.\n",
    "\n",
    "1) сгруппируйте датафрейм по типу жилья и посчитайте среднее для цены и рейтинга \n",
    "\n",
    "2) используйте sns.scatterplot, чтобы построить график рассеивания средней цены (ось X) и рейтинга (ось Y) c разбивкой по типу жилья (параметр hue)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>avg_price</th>\n",
       "      <th>avg_review_scores_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hotel room</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>92.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Private room</td>\n",
       "      <td>54.642436</td>\n",
       "      <td>94.766208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>135.882957</td>\n",
       "      <td>93.568789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         room_type   avg_price  avg_review_scores_rating\n",
       "0       Hotel room  134.000000                 92.500000\n",
       "1     Private room   54.642436                 94.766208\n",
       "2      Shared room   21.000000                 90.000000\n",
       "3  Entire home/apt  135.882957                 93.568789"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  \n",
    "    room_type,\n",
    "    avg(price) as avg_price,\n",
    "    avg(review_scores_rating) as avg_review_scores_rating\n",
    "FROM  \n",
    "    (\n",
    "    SELECT \n",
    "        toFloat32OrNull(replaceRegexpAll(price, '[$,]', '')) AS price,\n",
    "        room_type,\n",
    "        toFloat32OrNull(review_scores_rating) AS review_scores_rating\n",
    "    FROM ldn_listings\n",
    "    WHERE review_scores_rating != 0\n",
    "    ORDER BY id ASC \n",
    "    LIMIT 1000\n",
    "    )\n",
    "GROUP BY room_type\n",
    "\"\"\"\n",
    "mean_scores = ph.read_clickhouse(query, connection=connection_default)\n",
    "mean_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26f0283c88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=mean_scores, x='avg_price', y='avg_review_scores_rating', hue='room_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Помимо аренды жилья, на Airbnb также есть \"Впечатления\" — мероприятия, которые организуют местные жители.\n",
    "Проверим, какие способы верификации аккаунта использовали хозяева, предлагающие различные впечатления (experiences_offered != 'none'). Для каждого уникального пользователя выгрузите только две колонки:\n",
    "\n",
    "- host_id – идентификатор хозяина (уникальный)\n",
    "- host_verifications – какими способами хост подтвердил свой профиль\n",
    "\n",
    "Затем обработайте столбец host_verifications:\n",
    "\n",
    "В ячейках находятся строковые представления списка (например, как результат str([1, 2, 3])). \n",
    "Приведите их к настоящему списку, где в качестве элементов будут храниться использованные способы подтверждения аккаунта: \n",
    "\"['email', 'phone', 'facebook']\" -> ['email', 'phone', 'facebook']\n",
    "\n",
    "\n",
    "Используйте методы explode и value_counts, чтобы посчитать, сколько раз встречается каждый способ верификации\n",
    "\n",
    "Сколько хозяев подтвердили профиль с помощью аккаунта Google?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_verifications</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54730</td>\n",
       "      <td>['email', 'phone', 'facebook', 'reviews', 'off...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60302</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67915</td>\n",
       "      <td>['email', 'phone', 'jumio', 'selfie', 'governm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>167107</td>\n",
       "      <td>['email', 'phone', 'manual_offline', 'jumio', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103583</td>\n",
       "      <td>['email', 'phone', 'reviews', 'offline_governm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>21558962</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>21257846</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>34219613</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>34299581</td>\n",
       "      <td>['email', 'phone', 'google', 'reviews', 'jumio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>26917573</td>\n",
       "      <td>['email', 'phone', 'reviews', 'manual_offline'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1361 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       host_id                                 host_verifications\n",
       "0        54730  ['email', 'phone', 'facebook', 'reviews', 'off...\n",
       "1        60302  ['email', 'phone', 'reviews', 'jumio', 'govern...\n",
       "2        67915  ['email', 'phone', 'jumio', 'selfie', 'governm...\n",
       "3       167107  ['email', 'phone', 'manual_offline', 'jumio', ...\n",
       "4       103583  ['email', 'phone', 'reviews', 'offline_governm...\n",
       "...        ...                                                ...\n",
       "1356  21558962  ['email', 'phone', 'reviews', 'jumio', 'govern...\n",
       "1357  21257846  ['email', 'phone', 'reviews', 'jumio', 'offlin...\n",
       "1358  34219613  ['email', 'phone', 'reviews', 'jumio', 'govern...\n",
       "1359  34299581  ['email', 'phone', 'google', 'reviews', 'jumio...\n",
       "1360  26917573  ['email', 'phone', 'reviews', 'manual_offline'...\n",
       "\n",
       "[1361 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  \n",
    "    DISTINCT host_id,\n",
    "    host_verifications\n",
    "FROM   \n",
    "    ldn_listings\n",
    "WHERE experiences_offered != 'none'\n",
    "\"\"\"\n",
    "\n",
    "verifications = ph.read_clickhouse(query, connection=connection_default)\n",
    "verifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Приведите их к настоящему списку, где в качестве элементов будут храниться использованные способы подтверждения аккаунта: \n",
    "\"['email', 'phone', 'facebook']\" -> ['email', 'phone', 'facebook'] \n",
    "\n",
    "Используйте методы explode и value_counts, чтобы посчитать, сколько раз встречается каждый способ верификации\n",
    "\n",
    "Сколько хозяев подтвердили профиль с помощью аккаунта Google?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'phone'                    1361\n",
       "'email'                    1345\n",
       "'reviews'                  1309\n",
       "'government_id'            1196\n",
       "'jumio'                    1115\n",
       "'offline_government_id'     469\n",
       "'work_email'                279\n",
       "'facebook'                  270\n",
       "'selfie'                    252\n",
       "'identity_manual'           227\n",
       "'manual_offline'             70\n",
       "'google'                     34\n",
       "'kba'                        27\n",
       "'manual_online'              16\n",
       "'sent_id'                     6\n",
       "Name: host_verifications, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verifications.host_verifications = verifications.host_verifications.apply(lambda x: x.strip('[]').split(', '))\n",
    "verifications.host_verifications.explode(ignore_index=True).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь посмотрим, для скольких объявлений и в каких районах хозяева указали впечатления. \n",
    "\n",
    "Сгруппируйте данные по району и виду впечатления и посчитайте количество объявлений. Новый столбец назовите experiences_count.\n",
    "Отсортируйте данные по убыванию experiences_count и выгрузите первые 100 строк. \n",
    "\n",
    "Затем преобразуйте данные с помощью pivot, поместив в индексы название района, столбцы – вид впечатления, а значения – число объявлений с таким впечатлением для каждого района. \n",
    "\n",
    "Визуализируйте результат с помощью sns.heatmap() c палитрой cmap=sns.cubehelix_palette(as_cmap=True). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>experiences_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hackney</td>\n",
       "      <td>family</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>business</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>business</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hackney</td>\n",
       "      <td>social</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hammersmith and Fulham</td>\n",
       "      <td>business</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Redbridge</td>\n",
       "      <td>social</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Barking and Dagenham</td>\n",
       "      <td>social</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Newham</td>\n",
       "      <td>romantic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>business</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>City of London</td>\n",
       "      <td>family</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    neighbourhood_cleansed experiences_offered  experiences_count\n",
       "0                  Hackney              family                 61\n",
       "1            Tower Hamlets            business                 58\n",
       "2              Westminster            business                 50\n",
       "3                  Hackney              social                 49\n",
       "4   Hammersmith and Fulham            business                 48\n",
       "..                     ...                 ...                ...\n",
       "95               Redbridge              social                  2\n",
       "96    Barking and Dagenham              social                  2\n",
       "97                  Newham            romantic                  1\n",
       "98                  Bexley            business                  1\n",
       "99          City of London              family                  1\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  \n",
    "    neighbourhood_cleansed,\n",
    "    experiences_offered,\n",
    "    count(*) AS experiences_count\n",
    "FROM   \n",
    "    ldn_listings\n",
    "WHERE experiences_offered != 'none'\n",
    "GROUP BY \n",
    "    neighbourhood_cleansed,\n",
    "    experiences_offered\n",
    "ORDER BY experiences_count DESC \n",
    "LIMIT 100  \n",
    "\"\"\"\n",
    "\n",
    "\n",
    "experiences = ph.read_clickhouse(query, connection=connection_default)\n",
    "experiences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiences = experiences.pivot(index = 'neighbourhood_cleansed', columns = 'experiences_offered', values = 'experiences_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiences = experiences.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>experiences_offered</th>\n",
       "      <th>business</th>\n",
       "      <th>family</th>\n",
       "      <th>romantic</th>\n",
       "      <th>social</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Barking and Dagenham</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnet</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bexley</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brent</th>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bromley</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "experiences_offered     business  family  romantic  social\n",
       "neighbourhood_cleansed                                    \n",
       "Barking and Dagenham         0.0     0.0       0.0     2.0\n",
       "Barnet                       7.0     7.0       3.0     5.0\n",
       "Bexley                       1.0     0.0       0.0     0.0\n",
       "Brent                        9.0    21.0       5.0     6.0\n",
       "Bromley                      3.0     2.0       0.0     4.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiences.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26e74f3f60>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(experiences, cmap=sns.cubehelix_palette(as_cmap=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выгрузите данные о ценах за ночь для разных типов жилья, для которых также доступен какой-либо вид впечатления. Необходимые для составления запроса столбцы:\n",
    "- room_type – тип сдаваемого жилья (доступные варианты: Entire home/apt, Private room, Hotel room, Shared room)\n",
    "- price – цена за ночь \n",
    "- experiences_offered – вид доступного впечатления (оставить не 'none')\n",
    "\n",
    "Далее постройте два графика, используя distplot из библиотеки seaborn:\n",
    "\n",
    "- На первом отобразите исходные распределения цен для каждого типа жилья\n",
    "- На втором – логарифмированные значения (np.log())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT  \n",
    "    room_type,\n",
    "    toFloat32OrNull(replaceRegexpAll(price, '[$,]', '')) AS price\n",
    "FROM ldn_listings\n",
    "WHERE experiences_offered != 'none'\n",
    "\"\"\"\n",
    "\n",
    "price_room = ph.read_clickhouse(query, connection=connection_default)\n",
    "price_room['log_price'] = np.log(price_room.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f26e6fbc908>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 476.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 476.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x='price', data = price_room, hue = 'room_type')\n",
    "sns.displot(x='log_price', data = price_room, hue = 'room_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выгрузите данные о цене, типе жилья и дате первого отзыва, начиная со 2 января 2010 года. Необходимые столбцы:\n",
    "\n",
    "- room_type – тип сдаваемого жилья (доступные варианты: Entire home/apt, Private room, Hotel room, Shared room)\n",
    "- price – цена за ночь\n",
    "- first_review – дата первого отзыва (отфильтровать по правилу \"строго больше 2010-01-01\")\n",
    "\n",
    "Ограничение поставьте на 1000 строк.\n",
    "\n",
    "\n",
    "Используя sns.lineplot, постройте график динамики средних цен на жилье (ось Y) в зависимости от типа комнаты (цвет линии, параметр 'hue') по годам (ось X). Датафрейм должен быть отсортирован по годам.\n",
    "\n",
    "Удалите последние 6 символов у значений в столбце даты, чтобы получить год, например, с помощью применения lambda-функции .\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>first_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Private room</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2010-08-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2011-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2016-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private room</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2010-04-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private room</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2010-10-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         room_type  price first_review\n",
       "0     Private room   65.0   2010-08-18\n",
       "1  Entire home/apt  300.0   2011-03-21\n",
       "2  Entire home/apt   65.0   2016-03-05\n",
       "3     Private room   29.0   2010-04-14\n",
       "4     Private room  100.0   2010-10-16"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  \n",
    "    room_type,\n",
    "    toFloat32OrNull(replaceRegexpAll(price, '[$,]', '')) AS price,\n",
    "    toDateOrZero(first_review) AS first_review\n",
    "FROM ldn_listings\n",
    "WHERE first_review > '2010-01-01'\n",
    "LIMIT 1000 \n",
    "\"\"\"\n",
    "\n",
    "r = ph.read_clickhouse(query, connection=connection_default)\n",
    "r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r['year_review'] = r.first_review.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>first_review</th>\n",
       "      <th>year_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Private room</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2010-08-18</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2011-03-21</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private room</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2010-04-14</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private room</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2010-10-16</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         room_type  price first_review  year_review\n",
       "0     Private room   65.0   2010-08-18         2010\n",
       "1  Entire home/apt  300.0   2011-03-21         2011\n",
       "2  Entire home/apt   65.0   2016-03-05         2016\n",
       "3     Private room   29.0   2010-04-14         2010\n",
       "4     Private room  100.0   2010-10-16         2010"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = r.groupby(['room_type', 'year_review']).agg({'price' : 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_type</th>\n",
       "      <th>year_review</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">Entire home/apt</th>\n",
       "      <th>2010</th>\n",
       "      <td>103.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>128.792450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>135.467255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>137.887634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>154.583328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>159.380951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>99.733330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>111.666664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>125.066666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>301.666656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hotel room</th>\n",
       "      <th>2010</th>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">Private room</th>\n",
       "      <th>2010</th>\n",
       "      <td>56.428570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>52.607143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>53.269840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>57.036587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>56.842106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>48.058823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>57.307693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>51.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>55.416668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Shared room</th>\n",
       "      <th>2012</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  price\n",
       "room_type       year_review            \n",
       "Entire home/apt 2010         103.500000\n",
       "                2011         128.792450\n",
       "                2012         135.467255\n",
       "                2013         137.887634\n",
       "                2014         154.583328\n",
       "                2015         159.380951\n",
       "                2016          99.733330\n",
       "                2017         111.666664\n",
       "                2018         125.066666\n",
       "                2019         301.666656\n",
       "                2020          50.000000\n",
       "Hotel room      2010          88.000000\n",
       "                2017         180.000000\n",
       "Private room    2010          56.428570\n",
       "                2011          52.607143\n",
       "                2012          53.269840\n",
       "                2013          57.036587\n",
       "                2014          56.842106\n",
       "                2015          48.058823\n",
       "                2016          57.307693\n",
       "                2017          51.142857\n",
       "                2018          55.416668\n",
       "                2019          74.000000\n",
       "                2020          39.000000\n",
       "Shared room     2012          22.000000\n",
       "                2014          20.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f26e6e07668>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Используя библиотеку seaborn и функцию lineplot, постройте график динамики средних цен на жилье (ось Y) \n",
    "# в зависимости от типа комнаты (цвет линии, параметр 'hue') по годам (ось X).\n",
    "sns.lineplot(data=r, x='year_review', y = 'price', hue = 'room_type')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}