SQL Project - E-book Startup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2dbc7fe",
   "metadata": {},
   "source": [
    "# FINAL PROJECT - SQL\n",
    "\n",
    "by:***Dorothy Kunth***"
   ]
  },
  {
   "attachments": {
    "erd.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8dcbb416",
   "metadata": {},
   "source": [
    "## Overview\n",
    "The coronavirus took the entire world by surprise, changing everyone's daily routine. City dwellers no longer spent their free time outside, going to cafes and malls; more people were at home, reading books. That attracted the attention of startups that rushed to develop new apps for book lovers. \n",
    "\n",
    "You've been given a database of one of the services competing in this market. It contains data on books, publishers, authors, customer ratings and reviews of books. This information will be used to generate a value proposition for a new product.\n",
    "\n",
    "Data Diagram:\n",
    "\n",
    "\n",
    "![erd.png](attachment:erd.png)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79151f28",
   "metadata": {},
   "source": [
    "### Goal\n",
    "Based on the given data, the goal is to create a product value proposition that articulates the product's purpose, features and differentiators while taking into account the customer's problems, needs and wants. This is to ensure that target prospects will read more about the company and understand what they will get out of the products and services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "408390c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries and establish connection\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "\n",
    "db_config = {'user': 'practicum_student',         # username\n",
    "             'pwd': 's65BlTKV3faNIGhmvJVzOqhs', # password\n",
    "             'host': 'rc1b-wcoijxj3yxfsf3fs.mdb.yandexcloud.net',\n",
    "             'port': 6432,              # connection port\n",
    "             'db': 'data-analyst-final-project-db'}          # the name of the database\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",
    "engine = create_engine(connection_string, connect_args={'sslmode':'require'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678df99d",
   "metadata": {},
   "source": [
    "#### Print the first few rows of each table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34a9b84b",
   "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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>1st to Die (Women's Murder Club  #1)</td>\n",
       "      <td>424</td>\n",
       "      <td>2005-05-20</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>258</td>\n",
       "      <td>2nd Chance (Women's Murder Club  #2)</td>\n",
       "      <td>400</td>\n",
       "      <td>2005-05-20</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>260</td>\n",
       "      <td>4th of July (Women's Murder Club  #4)</td>\n",
       "      <td>448</td>\n",
       "      <td>2006-06-01</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>563</td>\n",
       "      <td>A Beautiful Mind</td>\n",
       "      <td>461</td>\n",
       "      <td>2002-02-04</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>445</td>\n",
       "      <td>A Bend in the Road</td>\n",
       "      <td>341</td>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>116</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",
       "5        6        257               1st to Die (Women's Murder Club  #1)   \n",
       "6        7        258               2nd Chance (Women's Murder Club  #2)   \n",
       "7        8        260              4th of July (Women's Murder Club  #4)   \n",
       "8        9        563                                   A Beautiful Mind   \n",
       "9       10        445                                 A Bend in the Road   \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  \n",
       "5        424       2005-05-20           116  \n",
       "6        400       2005-05-20           116  \n",
       "7        448       2006-06-01           318  \n",
       "8        461       2002-02-04           104  \n",
       "9        341       2005-04-01           116  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "query = 'SELECT * FROM books LIMIT 10'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c901e7a2",
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   "outputs": [
    {
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       "   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\n",
       "5          6                            Alan Paton\n",
       "6          7           Albert Camus/Justin O'Brien\n",
       "7          8                         Aldous Huxley\n",
       "8          9    Aldous Huxley/Christopher Hitchens\n",
       "9         10  Aleksandr Solzhenitsyn/H.T. Willetts"
      ]
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       "   rating_id  book_id       username  rating\n",
       "0          1        1     ryanfranco       4\n",
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       "2          3        1   brandtandrea       5\n",
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       "6          7        3    scotttamara       5\n",
       "7          8        3    lesliegibbs       5\n",
       "8          9        4    abbottjames       5\n",
       "9         10        4   valenciaanne       4"
      ]
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    "query = 'SELECT * FROM ratings LIMIT 10'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6407f5b4",
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   "outputs": [
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       "      <td>9</td>\n",
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       "   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",
       "5          6        3    lesliegibbs   \n",
       "6          7        4   valenciaanne   \n",
       "7          8        4    abbottjames   \n",
       "8          9        5        npowers   \n",
       "9         10        5        staylor   \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...  \n",
       "5           Analysis no several cause international.  \n",
       "6  One there cost another. Say type save. With pe...  \n",
       "7  Within enough mother. There at system full rec...  \n",
       "8  Thank now focus realize economy focus fly. Ite...  \n",
       "9  Game push lot reduce where remember. Including...  "
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    "query = 'SELECT * FROM reviews LIMIT 10'\n",
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   "cell_type": "code",
   "execution_count": 7,
   "id": "1498247f",
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   "outputs": [
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       "   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\n",
       "5             6                                  Aladdin\n",
       "6             7                       Aladdin Paperbacks\n",
       "7             8                             Albin Michel\n",
       "8             9                          Alfred A. Knopf\n",
       "9            10  Alfred A. Knopf Books for Young Readers"
      ]
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    "query = 'SELECT * FROM publishers LIMIT 10'\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa43bd10",
   "metadata": {},
   "source": [
    "#### Task 1: Find the number of books released after January 1, 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37068c20",
   "metadata": {},
   "outputs": [
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       "      <td>819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count\n",
       "0    819"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"SELECT COUNT (*) FROM books WHERE publication_date > '2000-01-01'\"\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bdbd73",
   "metadata": {},
   "source": [
    "> There are 819 books released after January 1, 2000. There are 1000 books in the dataset and most of them were released after January 1, 2000."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c446f1",
   "metadata": {},
   "source": [
    "#### Task 2: Find the number of user reviews and the average rating for each book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df14b831",
   "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>2793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count\n",
       "0   2793"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of user reviews\n",
    "query = 'SELECT COUNT(review_id) FROM reviews'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e99f4c8d",
   "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>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>4.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg\n",
       "0    4.500000\n",
       "1    4.500000\n",
       "2    4.250000\n",
       "3    4.000000\n",
       "4    4.285714\n",
       "..        ...\n",
       "995  4.230769\n",
       "996  5.000000\n",
       "997  3.428571\n",
       "998  3.500000\n",
       "999  3.000000\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the average rating for each book\n",
    "query = 'SELECT AVG(rating) FROM ratings GROUP BY book_id'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d7f221e",
   "metadata": {},
   "source": [
    "> There are currently 2793 reviews in the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d313bb",
   "metadata": {},
   "source": [
    "#### Task 3: Identify the publisher that has released the greatest number of books with more than 50 pages (this will help you exclude brochures and similar publications from your analysis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "184f6041",
   "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",
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       "    }\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</th>\n",
       "      <th>num_books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Penguin Books</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       publisher  num_books\n",
       "0  Penguin Books         42"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = 'SELECT publisher, COUNT(book_id) AS num_books \\\n",
    "         FROM publishers AS p \\\n",
    "         JOIN books AS b ON p.publisher_id = b.publisher_id \\\n",
    "         WHERE num_pages > 50 \\\n",
    "         GROUP BY publisher \\\n",
    "         ORDER by num_books DESC LIMIT 1'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe19991e",
   "metadata": {},
   "source": [
    "> The publisher that has released the greatest number of books with more than 50 pages is Penguin Books with 42 book released."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e20bd94",
   "metadata": {},
   "source": [
    "#### Task 4: Identify the author with the highest average book rating (look only at books with at least 50 ratings)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70ba9ccc",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    book_id\n",
       "0        75\n",
       "1       750\n",
       "2       545\n",
       "3       948\n",
       "4       488\n",
       "5       696\n",
       "6       722\n",
       "7       627\n",
       "8       733\n",
       "9       779\n",
       "10      405\n",
       "11      302\n",
       "12      673\n",
       "13      300\n",
       "14      299\n",
       "15      301\n",
       "16      399\n",
       "17       79\n",
       "18      656"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query books with atleast 50 ratings\n",
    "query = 'SELECT book_id FROM ratings GROUP BY book_id HAVING COUNT(ratings) >= 50'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9d1767d",
   "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</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>4.287097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       author       avg\n",
       "0  J.K. Rowling/Mary GrandPré  4.287097"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the author with the highest average book rating (look only at books with at least 50 ratings)\n",
    "query = 'SELECT a.author, AVG(r.rating)\\\n",
    "         FROM authors AS a\\\n",
    "         JOIN books AS b ON a.author_id = b.author_id \\\n",
    "         JOIN ratings AS r ON b.book_id = r.book_id \\\n",
    "         WHERE b.book_id IN (SELECT book_id FROM ratings GROUP BY book_id HAVING COUNT(ratings) >= 50) \\\n",
    "         GROUP BY a.author \\\n",
    "         ORDER BY AVG(r.rating) DESC LIMIT 1' \n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1026c2",
   "metadata": {},
   "source": [
    "> The author with the highest average book rating with minimum 50 ratings is J.K. Rowling/Mary GrandPre with an average rating of 4.287097."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81789d57",
   "metadata": {},
   "source": [
    "#### Task 5: Find the average number of text reviews among users who rated more than 50 books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "785e08b3",
   "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>username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sfitzgerald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jennifermiller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xdavis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>paul88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>martinadam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>richard89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         username\n",
       "0     sfitzgerald\n",
       "1  jennifermiller\n",
       "2          xdavis\n",
       "3          paul88\n",
       "4      martinadam\n",
       "5       richard89"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query users who rated more than 50 books\n",
    "query = 'SELECT username FROM ratings GROUP BY username HAVING COUNT(rating_id) > 50'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d4f8b0fe",
   "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",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>num_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zjohnston</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jnelson</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lewisdesiree</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>patrickhudson</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ulowe</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>shermannatalie</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>dmiller</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>charlesraymond</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>isaiahreyes</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>masonsara</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           username  num_reviews\n",
       "0         zjohnston           14\n",
       "1           jnelson           21\n",
       "2      lewisdesiree           19\n",
       "3     patrickhudson           15\n",
       "4             ulowe           16\n",
       "..              ...          ...\n",
       "155  shermannatalie           24\n",
       "156         dmiller           18\n",
       "157  charlesraymond           16\n",
       "158     isaiahreyes           15\n",
       "159       masonsara           14\n",
       "\n",
       "[160 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query number of text reviews per username\n",
    "query = 'SELECT username, COUNT(text) num_reviews FROM reviews GROUP BY username'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46683a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ave_num_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ave_num_reviews\n",
       "0        24.333333"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the average number of text reviews among users who rated more than 50 books.\n",
    "query = 'SELECT AVG(num_reviews) AS ave_num_reviews \\\n",
    "         FROM (SELECT username, COUNT(text) num_reviews FROM reviews GROUP BY username) AS t1 \\\n",
    "         WHERE username IN (SELECT username FROM ratings GROUP BY username HAVING COUNT(rating_id) > 50)'\n",
    "pd.io.sql.read_sql(query, con = engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3116f052",
   "metadata": {},
   "source": [
    "> The average number of reviews among users who rated more than 50 books is 24.33."
   ]
  }
 ],
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