graduation_project/yandex_ex3_SQL_karpova_new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f34d623",
   "metadata": {},
   "source": [
    "# Анализ базы данных сервиса для чтения книг по подписке"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c9d391",
   "metadata": {},
   "source": [
    "**Заказчик**\n",
    "\n",
    "Коммерческий департамент компании\n",
    "\n",
    "**Цель исследования**\n",
    "\n",
    "Анализ базы данных свежекупленного сервиса для формирования концепции нового продукта.\n",
    "\n",
    "**Входные данные**\n",
    "\n",
    "База данных содержит информацию о книгах, издательствах, авторах, а также пользовательские\n",
    "обзоры книг. \n",
    "\n",
    "* таблица `books` cодержит данные о книгах:   \n",
    "`book_id` — идентификатор книги;   \n",
    "`author_id` — идентификатор автора;   \n",
    "`title` — название книги;   \n",
    "`num_pages` — количество страниц;   \n",
    "`publication_date` — дата публикации книги;   \n",
    "`publisher_id` — идентификатор издателя.   \n",
    "\n",
    "\n",
    "* таблица `authors` cодержит данные об авторах:   \n",
    "`author_id` — идентификатор автора;   \n",
    "`author` — имя автора    \n",
    "\n",
    "\n",
    "* таблица `publishers` cодержит данные об издательствах:    \n",
    "`publisher_id` — идентификатор издательства;   \n",
    "`publisher` — название издательства;   \n",
    "\n",
    "\n",
    "* таблица `ratings` cодержит данные о пользовательских оценках книг:   \n",
    "`rating_id` — идентификатор оценки;   \n",
    "`book_id` — идентификатор книги;   \n",
    "`username` — имя пользователя, оставившего оценку;   \n",
    "`rating` — оценка книги.   \n",
    "\n",
    "\n",
    "* таблица `reviews` cодержит данные о пользовательских обзорах на книги:   \n",
    "`review_id` — идентификатор обзора;   \n",
    "`book_id` — идентификатор книги;   \n",
    "`username` — имя пользователя, написавшего обзор;   \n",
    "`text`— текст обзора.   "
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "3970679e",
   "metadata": {},
   "source": [
    "![%D0%B8%D0%B7%D0%BE%D0%B1%D1%80%D0%B0%D0%B6%D0%B5%D0%BD%D0%B8%D0%B5.png](attachment:%D0%B8%D0%B7%D0%BE%D0%B1%D1%80%D0%B0%D0%B6%D0%B5%D0%BD%D0%B8%D0%B5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8915122",
   "metadata": {},
   "source": [
    "## Подключение к БД"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "717d7bd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: psycopg2-binary in c:\\users\\ksuta\\anaconda3\\envs\\praktikum_env_win_new\\lib\\site-packages (2.8.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install psycopg2-binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd51b1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем бибилиотеки\n",
    "\n",
    "import pandas as pd\n",
    "import warnings; warnings.filterwarnings(action='once')\n",
    "\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94139b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# устанавливаем параметры\n",
    "\n",
    "db_config = {\n",
    "    'user': '-censored-', # имя пользователя\n",
    "    'pwd': '-censored-', # пароль\n",
    "    'host': '-censored-', 'port': 0000, # порт подключения\n",
    "    'db': '-censored-'} # название базы данных\n",
    "\n",
    "connection_string = 'postgresql://{}:{}@{}:{}/{}'.format(db_config['user'],\n",
    "                                                         db_config['pwd'], \n",
    "                                                         db_config['host'], \n",
    "                                                         db_config['port'], \n",
    "                                                         db_config['db'])\n",
    "\n",
    "# сохраняем коннектор\n",
    "\n",
    "engine = create_engine(connection_string, connect_args={'sslmode':'require'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ddd7fa",
   "metadata": {},
   "source": [
    "## Знакомство с таблицами"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a51b562",
   "metadata": {},
   "source": [
    "### `books`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5de4a040",
   "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>book_id</th>\n",
       "      <th>author_id</th>\n",
       "      <th>title</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>publisher_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>546</td>\n",
       "      <td>'Salem's Lot</td>\n",
       "      <td>594</td>\n",
       "      <td>2005-11-01</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>465</td>\n",
       "      <td>1 000 Places to See Before You Die</td>\n",
       "      <td>992</td>\n",
       "      <td>2003-05-22</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>407</td>\n",
       "      <td>13 Little Blue Envelopes (Little Blue Envelope...</td>\n",
       "      <td>322</td>\n",
       "      <td>2010-12-21</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>82</td>\n",
       "      <td>1491: New Revelations of the Americas Before C...</td>\n",
       "      <td>541</td>\n",
       "      <td>2006-10-10</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>125</td>\n",
       "      <td>1776</td>\n",
       "      <td>386</td>\n",
       "      <td>2006-07-04</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  author_id                                              title  \\\n",
       "0        1        546                                       'Salem's Lot   \n",
       "1        2        465                 1 000 Places to See Before You Die   \n",
       "2        3        407  13 Little Blue Envelopes (Little Blue Envelope...   \n",
       "3        4         82  1491: New Revelations of the Americas Before C...   \n",
       "4        5        125                                               1776   \n",
       "\n",
       "   num_pages publication_date  publisher_id  \n",
       "0        594       2005-11-01            93  \n",
       "1        992       2003-05-22           336  \n",
       "2        322       2010-12-21           135  \n",
       "3        541       2006-10-10           309  \n",
       "4        386       2006-07-04           268  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT *\n",
    "FROM books\n",
    "LIMIT 5\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66ff7c3",
   "metadata": {},
   "source": [
    "### `authors`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c7130f8",
   "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>author_id</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>A.S. Byatt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Aesop/Laura Harris/Laura Gibbs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Agatha Christie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Alan Brennert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Alan Moore/David   Lloyd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   author_id                          author\n",
       "0          1                      A.S. Byatt\n",
       "1          2  Aesop/Laura Harris/Laura Gibbs\n",
       "2          3                 Agatha Christie\n",
       "3          4                   Alan Brennert\n",
       "4          5        Alan Moore/David   Lloyd"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT *\n",
    "FROM authors\n",
    "LIMIT 5\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8ea999",
   "metadata": {},
   "source": [
    "### `publishers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a89d2ea0",
   "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>publisher_id</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Ace Book</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Ace Books</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Ace Hardcover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Addison Wesley Publishing Company</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publisher_id                          publisher\n",
       "0             1                                Ace\n",
       "1             2                           Ace Book\n",
       "2             3                          Ace Books\n",
       "3             4                      Ace Hardcover\n",
       "4             5  Addison Wesley Publishing Company"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT *\n",
    "FROM publishers\n",
    "LIMIT 5\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fed0d26",
   "metadata": {},
   "source": [
    "### `ratings`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d0743ea",
   "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>rating_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>username</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ryanfranco</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>grantpatricia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>brandtandrea</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>lorichen</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>mariokeller</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating_id  book_id       username  rating\n",
       "0          1        1     ryanfranco       4\n",
       "1          2        1  grantpatricia       2\n",
       "2          3        1   brandtandrea       5\n",
       "3          4        2       lorichen       3\n",
       "4          5        2    mariokeller       2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT *\n",
    "FROM ratings\n",
    "LIMIT 5\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b39f61f",
   "metadata": {},
   "source": [
    "### `reviews`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e1a7fb4",
   "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>review_id</th>\n",
       "      <th>book_id</th>\n",
       "      <th>username</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>brandtandrea</td>\n",
       "      <td>Mention society tell send professor analysis. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>ryanfranco</td>\n",
       "      <td>Foot glass pretty audience hit themselves. Amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>lorichen</td>\n",
       "      <td>Listen treat keep worry. Miss husband tax but ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>johnsonamanda</td>\n",
       "      <td>Finally month interesting blue could nature cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>scotttamara</td>\n",
       "      <td>Nation purpose heavy give wait song will. List...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id  book_id       username  \\\n",
       "0          1        1   brandtandrea   \n",
       "1          2        1     ryanfranco   \n",
       "2          3        2       lorichen   \n",
       "3          4        3  johnsonamanda   \n",
       "4          5        3    scotttamara   \n",
       "\n",
       "                                                text  \n",
       "0  Mention society tell send professor analysis. ...  \n",
       "1  Foot glass pretty audience hit themselves. Amo...  \n",
       "2  Listen treat keep worry. Miss husband tax but ...  \n",
       "3  Finally month interesting blue could nature cu...  \n",
       "4  Nation purpose heavy give wait song will. List...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT *\n",
    "FROM reviews\n",
    "LIMIT 5\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a4d2e1",
   "metadata": {},
   "source": [
    "## Задания"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16fb76d",
   "metadata": {},
   "source": [
    "### Посчитайте, сколько книг вышло после 1 января 2000 года;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f36095eb",
   "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",
       "      <td>0</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count\n",
       "0    819"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT COUNT(book_id)\n",
    "FROM books\n",
    "WHERE publication_date > '2000-01-01'\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64cf4105",
   "metadata": {},
   "source": [
    "После 1 января 2000 года в каталог сервиса было добавлено **819** книг и брошюр."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcfba19d",
   "metadata": {},
   "source": [
    "### Для каждой книги посчитайте количество обзоров и среднюю оценку;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "318c4d7d",
   "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>book_id</th>\n",
       "      <th>title</th>\n",
       "      <th>cnt_reviews</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>948</td>\n",
       "      <td>Twilight (Twilight  #1)</td>\n",
       "      <td>7</td>\n",
       "      <td>3.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>963</td>\n",
       "      <td>Water for Elephants</td>\n",
       "      <td>6</td>\n",
       "      <td>3.977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>734</td>\n",
       "      <td>The Glass Castle</td>\n",
       "      <td>6</td>\n",
       "      <td>4.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>302</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>6</td>\n",
       "      <td>4.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>695</td>\n",
       "      <td>The Curious Incident of the Dog in the Night-Time</td>\n",
       "      <td>6</td>\n",
       "      <td>4.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>83</td>\n",
       "      <td>Anne Rice's The Vampire Lestat: A Graphic Novel</td>\n",
       "      <td>0</td>\n",
       "      <td>3.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>996</td>\n",
       "      <td>808</td>\n",
       "      <td>The Natural Way to Draw</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>997</td>\n",
       "      <td>672</td>\n",
       "      <td>The Cat in the Hat and Other Dr. Seuss Favorites</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>998</td>\n",
       "      <td>221</td>\n",
       "      <td>Essential Tales and Poems</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>999</td>\n",
       "      <td>191</td>\n",
       "      <td>Disney's Beauty and the Beast (A Little Golden...</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     book_id                                              title  cnt_reviews  \\\n",
       "0        948                            Twilight (Twilight  #1)            7   \n",
       "1        963                                Water for Elephants            6   \n",
       "2        734                                   The Glass Castle            6   \n",
       "3        302  Harry Potter and the Prisoner of Azkaban (Harr...            6   \n",
       "4        695  The Curious Incident of the Dog in the Night-Time            6   \n",
       "..       ...                                                ...          ...   \n",
       "995       83    Anne Rice's The Vampire Lestat: A Graphic Novel            0   \n",
       "996      808                            The Natural Way to Draw            0   \n",
       "997      672   The Cat in the Hat and Other Dr. Seuss Favorites            0   \n",
       "998      221                          Essential Tales and Poems            0   \n",
       "999      191  Disney's Beauty and the Beast (A Little Golden...            0   \n",
       "\n",
       "     avg_rating  \n",
       "0         3.663  \n",
       "1         3.977  \n",
       "2         4.207  \n",
       "3         4.415  \n",
       "4         4.081  \n",
       "..          ...  \n",
       "995       3.667  \n",
       "996       3.000  \n",
       "997       5.000  \n",
       "998       4.000  \n",
       "999       4.000  \n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT b.book_id, \n",
    "       b.title, \n",
    "       COUNT(DISTINCT rv.review_id) AS cnt_reviews, \n",
    "       ROUND(AVG(r.rating),3) AS avg_rating\n",
    "FROM books AS b\n",
    "\n",
    "LEFT JOIN reviews AS rv\n",
    "ON b.book_id = rv.book_id\n",
    "\n",
    "LEFT JOIN  ratings AS r\n",
    "ON b.book_id = r.book_id\n",
    "\n",
    "GROUP BY b.book_id, b.title\n",
    "ORDER BY cnt_reviews DESC\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efde08d1",
   "metadata": {},
   "source": [
    "Всего в каталоге 1000 книг. Количество обзоров варьируется от 7 до полного их осутствия. Средний рейтинг каждой книги представлен в результате запроса выше. Можно отметить, что количество обзоров на книгу не коррелирует с средним рейтингом, т.к. речь идет исключительно о вкусовых предпочтениях пользователей."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c6abcf",
   "metadata": {},
   "source": [
    "### Определите издательство, которое выпустило наибольшее число книг толще 50 страниц — так вы исключите из анализа брошюры;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8650b4b1",
   "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>publisher_id</th>\n",
       "      <th>publisher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>Penguin Books</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publisher_id      publisher\n",
       "0           212  Penguin Books"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "WITH \n",
    "-- исключаем брошюры --\n",
    "tab AS (\n",
    "SELECT publisher_id,\n",
    "       book_id\n",
    "FROM books\n",
    "WHERE num_pages > 50\n",
    ")\n",
    "\n",
    "SELECT tab.publisher_id,\n",
    "       p.publisher\n",
    "FROM tab \n",
    "\n",
    "INNER JOIN publishers AS p\n",
    "ON tab.publisher_id = p.publisher_id\n",
    "\n",
    "GROUP BY tab.publisher_id, p.publisher\n",
    "ORDER BY COUNT(tab.book_id) DESC\n",
    "\n",
    "LIMIT 1\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9eb9341",
   "metadata": {},
   "source": [
    "Наиболее широко представлены книги (внимание: из анализа исключены брошюры с 50-ю  и меньше страницами) издательства **Penguin Books** (британское издательство, основанное в 1935 году в Лондоне сэром Алленом Лейном и его братьями Ричардом и Джоном). Главной заслугой издательства считают демократизацию книжного рынка, превратившую книгу из предмета роскоши в удовольствие, доступное массам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc32d141",
   "metadata": {},
   "source": [
    "### Определите автора с самой высокой средней оценкой книг — учитывайте только книги с 50 и более оценками;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bf71de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author_id</th>\n",
       "      <th>author</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.287097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   author_id                      author  avg_rating\n",
       "0        236  J.K. Rowling/Mary GrandPré    4.287097"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "WITH \n",
    "\n",
    "--отбираем книги с количеством оценок более 50ти--\n",
    "tab AS (\n",
    "SELECT book_id\n",
    "FROM ratings\n",
    "GROUP BY book_id\n",
    "HAVING COUNT(rating_id) > 50)\n",
    "\n",
    "\n",
    "SELECT a.author_id, \n",
    "       a.author, \n",
    "       AVG(r.rating) AS avg_rating\n",
    "FROM books AS b\n",
    "\n",
    "INNER JOIN tab\n",
    "ON b.book_id = tab.book_id\n",
    "\n",
    "INNER JOIN authors AS a\n",
    "ON b.author_id = a.author_id\n",
    "\n",
    "INNER JOIN ratings AS r\n",
    "ON tab.book_id = r.book_id\n",
    "\n",
    "GROUP BY a.author_id, a.author\n",
    "ORDER BY AVG(r.rating) DESC\n",
    "\n",
    "LIMIT 1\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf28e1f0",
   "metadata": {},
   "source": [
    "Самые популярный авторы с средним рейтингом в **4.29** баллов из 5 - это тандем **Джоан Роулинг** (британской писательницы, автора популярной серии книг о Гарри Поттере) и американского иллюстратора **Мари Грандпре** (делала иллюстрации к обложкам и главам книг). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f35c5cb",
   "metadata": {},
   "source": [
    "### Посчитайте среднее количество обзоров от пользователей, которые поставили больше 50 оценок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "693ac871",
   "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>avg_rv_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_rv_count\n",
       "0            24"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "\n",
    "SELECT COUNT(DISTINCT rv.text)/ COUNT(DISTINCT rv.username) AS avg_rv_count\n",
    "FROM ratings AS r\n",
    "\n",
    "INNER JOIN reviews AS rv\n",
    "ON r.username = rv.username\n",
    "\n",
    "WHERE r.username in ( \n",
    "                    SELECT username\n",
    "                    FROM ratings\n",
    "                    GROUP BY username\n",
    "                    HAVING COUNT(rating_id) > 50\n",
    "                    )\n",
    "\n",
    "'''\n",
    "\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab249ba8",
   "metadata": {},
   "source": [
    "В среднем,  активные пользователи (активными считаются пользователи, поставившие болеее 50-ти оценок книгам и брошюрам) написали обзоры на **24** книги."
   ]
  }
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