{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Project: Answering Business Questions using SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction: "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we will use SQL to answer business questions related to a modified version of a database called Chinook. The Chinook database contains information about a fictional digital music shop - kind of like a mini-iTunes store.\n",
    "\n",
    "The Chinook database contains information about the artists, songs, and albums from the music shop, as well as information on the shop's employees, customers, and the customers purchases. This information is contained in eleven tables. \n"
   ]
  },
  {
   "attachments": {
    "chinwook%20tables.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a schema diagram for the Chinook database:\n",
    "\n",
    "![chinwook%20tables.PNG](attachment:chinwook%20tables.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connecting the Jupyter Notebook to the database file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%load_ext sql\n",
    "%sql sqlite:///chinook.db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Returning information on the tables and views in the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "        <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>album</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>artist</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>customer</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>employee</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>genre</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>invoice</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>invoice_line</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>media_type</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>playlist</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>playlist_track</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>track</td>\n",
       "        <td>table</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('album', 'table'),\n",
       " ('artist', 'table'),\n",
       " ('customer', 'table'),\n",
       " ('employee', 'table'),\n",
       " ('genre', 'table'),\n",
       " ('invoice', 'table'),\n",
       " ('invoice_line', 'table'),\n",
       " ('media_type', 'table'),\n",
       " ('playlist', 'table'),\n",
       " ('playlist_track', 'table'),\n",
       " ('track', 'table')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT name, type\n",
    "  FROM sqlite_master\n",
    "WHERE type IN (\"table\",\"view\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* return the first five album names from the 'album' table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>album_id</th>\n",
       "        <th>title</th>\n",
       "        <th>artist_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>For Those About To Rock We Salute You</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>Balls to the Wall</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>Restless and Wild</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>Let There Be Rock</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>5</td>\n",
       "        <td>Big Ones</td>\n",
       "        <td>3</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'For Those About To Rock We Salute You', 1),\n",
       " (2, 'Balls to the Wall', 2),\n",
       " (3, 'Restless and Wild', 2),\n",
       " (4, 'Let There Be Rock', 1),\n",
       " (5, 'Big Ones', 3)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "\n",
    "SELECT *\n",
    "  FROM album\n",
    "LIMIT 5"
   ]
  },
  {
   "attachments": {
    "table%201.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°1:\n",
    "\n",
    "The Chinook record store has just signed a deal with a new record label, and you've been tasked with selecting the first three albums that will be added to the store, from a list of four. All four albums are by artists that don't have any tracks in the store right now - we have the artist names, and the genre of music they produce:![table%201.PNG](attachment:table%201.PNG)\n",
    "\n",
    "The record label specializes in artists from the USA, and they have given Chinook some money to advertise the new albums in the USA, so we're interested in finding out which genres sell the best in the USA.\n",
    "\n",
    "so we need to write a query to find out which genres sell the most tracks in the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>genre</th>\n",
       "        <th>number of tracks sold in the USA</th>\n",
       "        <th>percentage of tracks sold in the USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Rock</td>\n",
       "        <td>561</td>\n",
       "        <td>53.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative &amp; Punk</td>\n",
       "        <td>130</td>\n",
       "        <td>12.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Metal</td>\n",
       "        <td>124</td>\n",
       "        <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>R&amp;B/Soul</td>\n",
       "        <td>53</td>\n",
       "        <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Blues</td>\n",
       "        <td>36</td>\n",
       "        <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative</td>\n",
       "        <td>35</td>\n",
       "        <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Pop</td>\n",
       "        <td>22</td>\n",
       "        <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Latin</td>\n",
       "        <td>22</td>\n",
       "        <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Hip Hop/Rap</td>\n",
       "        <td>20</td>\n",
       "        <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Jazz</td>\n",
       "        <td>14</td>\n",
       "        <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Easy Listening</td>\n",
       "        <td>13</td>\n",
       "        <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Reggae</td>\n",
       "        <td>6</td>\n",
       "        <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Electronica/Dance</td>\n",
       "        <td>5</td>\n",
       "        <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Classical</td>\n",
       "        <td>4</td>\n",
       "        <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Heavy Metal</td>\n",
       "        <td>3</td>\n",
       "        <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Soundtrack</td>\n",
       "        <td>2</td>\n",
       "        <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>TV Shows</td>\n",
       "        <td>1</td>\n",
       "        <td>0.1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Rock', 561, 53.38),\n",
       " ('Alternative & Punk', 130, 12.37),\n",
       " ('Metal', 124, 11.8),\n",
       " ('R&B/Soul', 53, 5.04),\n",
       " ('Blues', 36, 3.43),\n",
       " ('Alternative', 35, 3.33),\n",
       " ('Pop', 22, 2.09),\n",
       " ('Latin', 22, 2.09),\n",
       " ('Hip Hop/Rap', 20, 1.9),\n",
       " ('Jazz', 14, 1.33),\n",
       " ('Easy Listening', 13, 1.24),\n",
       " ('Reggae', 6, 0.57),\n",
       " ('Electronica/Dance', 5, 0.48),\n",
       " ('Classical', 4, 0.38),\n",
       " ('Heavy Metal', 3, 0.29),\n",
       " ('Soundtrack', 2, 0.19),\n",
       " ('TV Shows', 1, 0.1)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH tempo AS\n",
    "         (SELECT g.name genre, country \n",
    "            FROM customer c\n",
    "              JOIN invoice i on i.customer_id = c.customer_id\n",
    "              JOIN invoice_line il on il.invoice_id = i.invoice_id\n",
    "              JOIN track t on t.track_id = il.track_id\n",
    "              JOIN genre g on g.genre_id = t.genre_id \n",
    "           WHERE c.country = 'USA')\n",
    "    \n",
    "SELECT genre, COUNT(*) 'number of tracks sold in the USA', \n",
    "       ROUND(CAST(COUNT(*) AS float) * 100 / (SELECT COUNT(*) FROM tempo),2) 'percentage of tracks sold in the USA' \n",
    "FROM tempo\n",
    "GROUP BY genre\n",
    "ORDER BY 3 DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* About 54% of US customers prefer rock music!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>genre</th>\n",
       "        <th>number of tracks sold in the USA</th>\n",
       "        <th>percentage of tracks sold in the USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative &amp; Punk</td>\n",
       "        <td>130</td>\n",
       "        <td>12.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Blues</td>\n",
       "        <td>36</td>\n",
       "        <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Pop</td>\n",
       "        <td>22</td>\n",
       "        <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Hip Hop/Rap</td>\n",
       "        <td>20</td>\n",
       "        <td>1.9</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Alternative & Punk', 130, 12.37),\n",
       " ('Blues', 36, 3.43),\n",
       " ('Pop', 22, 2.09),\n",
       " ('Hip Hop/Rap', 20, 1.9)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH tempo AS\n",
    "         (SELECT g.name genre, country \n",
    "          FROM customer c\n",
    "          JOIN invoice i on i.customer_id = c.customer_id\n",
    "          JOIN invoice_line il on il.invoice_id = i.invoice_id\n",
    "          JOIN track t on t.track_id = il.track_id\n",
    "          JOIN genre g on g.genre_id = t.genre_id \n",
    "          WHERE c.country = 'USA')\n",
    "    \n",
    "SELECT genre, COUNT(*) 'number of tracks sold in the USA', \n",
    "       ROUND(CAST(COUNT(*) AS float) * 100 / (SELECT COUNT(*) FROM tempo),2) \n",
    "       'percentage of tracks sold in the USA' \n",
    "\n",
    "FROM tempo\n",
    "GROUP BY genre\n",
    "HAVING genre like '%blues%' or \n",
    "    genre like '%punk%' or \n",
    "    genre like '%pop%' or \n",
    "    genre like '%hip%'\n",
    "ORDER BY 3 desc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Based on the sales of tracks of their genres in the US, we only recommend artists who produce `Punk`, `Blues` and `Pop music`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°2:\n",
    "\n",
    "Each customer for the Chinook store gets assigned to a sales support agent within the company when they first make a purchase. We have been asked to analyze the purchases of customers belonging to each employee to see if any sales support agent is performing either better or worse than the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "        <th>birthdate</th>\n",
       "        <th>hire_date</th>\n",
       "        <th>country</th>\n",
       "        <th>total_purchases_of_customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Jane Peacock</td>\n",
       "        <td>1973-08-29 00:00:00</td>\n",
       "        <td>2017-04-01 00:00:00</td>\n",
       "        <td>Canada</td>\n",
       "        <td>1731.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Margaret Park</td>\n",
       "        <td>1947-09-19 00:00:00</td>\n",
       "        <td>2017-05-03 00:00:00</td>\n",
       "        <td>Canada</td>\n",
       "        <td>1584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Steve Johnson</td>\n",
       "        <td>1965-03-03 00:00:00</td>\n",
       "        <td>2017-10-17 00:00:00</td>\n",
       "        <td>Canada</td>\n",
       "        <td>1393.92</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Jane Peacock', '1973-08-29 00:00:00', '2017-04-01 00:00:00', 'Canada', 1731.51),\n",
       " ('Margaret Park', '1947-09-19 00:00:00', '2017-05-03 00:00:00', 'Canada', 1584.0),\n",
       " ('Steve Johnson', '1965-03-03 00:00:00', '2017-10-17 00:00:00', 'Canada', 1393.92)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH tempo AS\n",
    "         (SELECT e.first_name||' '||e.last_name name,birthdate,hire_date, e.country country,i.total\n",
    "            FROM employee e\n",
    "              JOIN customer c on e.employee_id = c.support_rep_id\n",
    "              JOIN invoice i on i.customer_id = c.customer_id\n",
    "           WHERE e.title = 'Sales Support Agent')\n",
    "    \n",
    "SELECT name,birthdate,hire_date,country,ROUND(SUM(total),2) total_purchases_of_customers\n",
    "  FROM tempo\n",
    "GROUP BY 1,2,3,4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* Only 3 sales support agents work for the Chinook record store.\n",
    "* all sales support agents are from Canada.\n",
    "* all sales support agents were over 40 when hired.\n",
    "* all sales support agents were hired in 2017.\n",
    "* Purchases of customers belonging to each sales support agent are positively correlated with their years of experience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°3:\n",
    "\n",
    "We have been asked to analyze the sales data for customers from each different country on the:\n",
    "\n",
    "* total number of customers\n",
    "* total value of sales\n",
    "* average value of sales per customer\n",
    "* average order value\n",
    "\n",
    "Because there are a number of countries with only one customer, we were asked to group these customers as \"Other\" in our analysis.\n",
    "\n",
    "The function of the temporary tables used in the sql query:\n",
    "* `temporary table t`: joining **invoice** and **customer** tables and selecting country, customr_id and total sales.\n",
    "* `temporary table t1` : group **t** by country and selecting country, the total number of customers for each country, the number of orders for each country, the total value of sales for each country and highlighting countries that have only one customer as \"Other\".\n",
    "* `temporary table t2` : calculating the total number of customers, the total value of sales, the average value of sales per customer and the average order value for each group of countries in **t1**.\n",
    "* `temporary table t3` : sort **t2** by the total sales from highest to lowest, with the \"Other\" group at the very bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>country</th>\n",
       "        <th>total number of customers</th>\n",
       "        <th>total value of sales</th>\n",
       "        <th>average order value</th>\n",
       "        <th>average value of sales per customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>USA</td>\n",
       "        <td>13</td>\n",
       "        <td>1040.4899999999998</td>\n",
       "        <td>7.942671755725189</td>\n",
       "        <td>80.0376923076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Canada</td>\n",
       "        <td>8</td>\n",
       "        <td>535.5900000000001</td>\n",
       "        <td>7.047236842105265</td>\n",
       "        <td>66.94875000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Brazil</td>\n",
       "        <td>5</td>\n",
       "        <td>427.68000000000006</td>\n",
       "        <td>7.0111475409836075</td>\n",
       "        <td>85.53600000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>France</td>\n",
       "        <td>5</td>\n",
       "        <td>389.0699999999999</td>\n",
       "        <td>7.781399999999998</td>\n",
       "        <td>77.81399999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Germany</td>\n",
       "        <td>4</td>\n",
       "        <td>334.62</td>\n",
       "        <td>8.161463414634147</td>\n",
       "        <td>83.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Czech Republic</td>\n",
       "        <td>2</td>\n",
       "        <td>273.24000000000007</td>\n",
       "        <td>9.108000000000002</td>\n",
       "        <td>136.62000000000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>United Kingdom</td>\n",
       "        <td>3</td>\n",
       "        <td>245.52</td>\n",
       "        <td>8.768571428571429</td>\n",
       "        <td>81.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Portugal</td>\n",
       "        <td>2</td>\n",
       "        <td>185.13000000000002</td>\n",
       "        <td>6.383793103448276</td>\n",
       "        <td>92.56500000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>India</td>\n",
       "        <td>2</td>\n",
       "        <td>183.14999999999998</td>\n",
       "        <td>8.72142857142857</td>\n",
       "        <td>91.57499999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Other</td>\n",
       "        <td>15</td>\n",
       "        <td>1094.9399999999998</td>\n",
       "        <td>7.4485714285714275</td>\n",
       "        <td>72.996</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('USA', 13, 1040.4899999999998, 7.942671755725189, 80.0376923076923),\n",
       " ('Canada', 8, 535.5900000000001, 7.047236842105265, 66.94875000000002),\n",
       " ('Brazil', 5, 427.68000000000006, 7.0111475409836075, 85.53600000000002),\n",
       " ('France', 5, 389.0699999999999, 7.781399999999998, 77.81399999999998),\n",
       " ('Germany', 4, 334.62, 8.161463414634147, 83.655),\n",
       " ('Czech Republic', 2, 273.24000000000007, 9.108000000000002, 136.62000000000003),\n",
       " ('United Kingdom', 3, 245.52, 8.768571428571429, 81.84),\n",
       " ('Portugal', 2, 185.13000000000002, 6.383793103448276, 92.56500000000001),\n",
       " ('India', 2, 183.14999999999998, 8.72142857142857, 91.57499999999999),\n",
       " ('Other', 15, 1094.9399999999998, 7.4485714285714275, 72.996)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH t AS \n",
    "         (SELECT c.country country,c.customer_id customer_id, i.total total\n",
    "            FROM customer c\n",
    "             JOIN invoice i on c.customer_id = i.customer_id),\n",
    "    \n",
    "     t1 AS\n",
    "         (SELECT country ,COUNT(DISTINCT(customer_id)) 'total number of customers',\n",
    "                 COUNT(customer_id) 'orders value', SUM(total) \"total value of sales\",\n",
    "                 CASE \n",
    "                 WHEN COUNT(DISTINCT(customer_id)) = 1 THEN 'Other'\n",
    "                 ELSE country\n",
    "                 END other_country\n",
    "           FROM t\n",
    "          GROUP BY country),\n",
    "            \n",
    "    t2 AS\n",
    "         (SELECT other_country country,\n",
    "              SUM(t1.\"total number of customers\") \"total number of customers\",\n",
    "              SUM(t1.\"total value of sales\") \"total value of sales\",\n",
    "              SUM(t1.\"total value of sales\") / SUM(t1.\"total number of customers\") \"average value of sales per customer\",\n",
    "              SUM(t1.\"total value of sales\") / SUM(t1.\"orders value\") \"average order value\"\n",
    "            FROM t1\n",
    "           GROUP BY other_country\n",
    "           ORDER BY \"total value of sales\" desc),\n",
    "     \n",
    "     t3 AS \n",
    "          (SELECT country,  \"total number of customers\", \"total value of sales\",\"average order value\",\n",
    "                 \"average value of sales per customer\"\n",
    "             FROM (SELECT t2.*,CASE\n",
    "                               WHEN country = 'Other' THEN 1\n",
    "                               ELSE 0\n",
    "                               END sort\n",
    "                     FROM t2\n",
    "                   ORDER BY sort asc))\n",
    "    \n",
    "SELECT *\n",
    "FROM t3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The highest total sales value is in the United States while the lowest is in Portugal and India."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°4:\n",
    "\n",
    "Management are currently considering changing their purchasing strategy to save money. The strategy they are considering is to purchase only the most popular tracks from each album from record companies, instead of purchasing every track from an album.\n",
    "\n",
    "We have been asked to find out what percentage of purchases are individual tracks vs whole albums, so that management can use this data to understand the effect this decision might have on overall revenue.\n",
    "\n",
    "We will write a query that categorizes each invoice as either an album purchase or not, and calculates the following summary statistics:\n",
    "* Number of invoices\n",
    "* Percentage of invoices\n",
    "\n",
    "The definition used here of what constitutes an album purchase is a purchase that contains a full album and nothing more, just as long as that album has more than two tracks.\n",
    "\n",
    "The function of the temporary tables used in the sql query:\n",
    "* `temporary table t`: joining **invoice** and **track** tables, grouping the resulting table by invoice_id and album_id and selecting invoice_id, album_id and the number of purchased tracks for each pair of invoice_id and album_id.\n",
    "* `temporary table t1`: joining **album** and **track** tables, grouping the resulting table by album_id and selecting album_id and the number of tracks in each album.\n",
    "* `temporary table t2`: joining **t** and **t1**, selecting invoice_id, album_id, the number of purchased tracks for each pair of invoice_id and album_id, the number of tracks in each album and highlighting purchases where the number of tracks in album is bigger than 2 and equal to the number of purchased tracks as 'album_purchase'.\n",
    "* `temporary table t3`: grouping **t2** by invoice_id and selecting invoice_id and album_purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>album_purchase</th>\n",
       "        <th>number_of_invoice</th>\n",
       "        <th>percentage %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>no</td>\n",
       "        <td>503</td>\n",
       "        <td>81.92182410423453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>yes</td>\n",
       "        <td>111</td>\n",
       "        <td>18.078175895765472</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('no', 503, 81.92182410423453), ('yes', 111, 18.078175895765472)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH t AS\n",
    "     (SELECT i.invoice_id,t.album_id,COUNT(t.track_id) tracks_bought\n",
    "        FROM invoice i\n",
    "          JOIN invoice_line il on i.invoice_id = il.invoice_id\n",
    "          JOIN track t on t.track_id = il.track_id\n",
    "       GROUP BY i.invoice_id,t.album_id),\n",
    "    \n",
    "    t1 AS\n",
    "      (SELECT a.album_id, COUNT(t.track_id) tracks_in_album\n",
    "         FROM track t\n",
    "           JOIN album a on a.album_id = t.album_id\n",
    "        GROUP BY 1),\n",
    "        \n",
    "    t2 AS\n",
    "       (SELECT t.*,t1.tracks_in_album,case\n",
    "                                      WHEN tracks_in_album = tracks_bought AND tracks_in_album >= 2 THEN 'yes'\n",
    "                                      ELSE 'no'\n",
    "                                      END album_purchase\n",
    "          FROM t\n",
    "            JOIN t1 on t.album_id = t1.album_id),\n",
    "    \n",
    "    t3 AS\n",
    "       (SELECT invoice_id,t2.album_purchase\n",
    "          FROM t2\n",
    "        GROUP BY invoice_id)\n",
    "\n",
    "SELECT album_purchase,COUNT(*) number_of_invoice, \n",
    "       CAST(COUNT(*) AS float) *100 / (SELECT COUNT(DISTINCT(invoice_id))FROM t) 'percentage %'\n",
    "FROM t3\n",
    "GROUP BY 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 81% of customers tend to buy single tracks more than purchasing the full album, so as a strategy to save money it would be profitable to purchase only the most popular tracks from each album from record companies rather than buying a full album."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Business question N°5: Which artist is used in the most playlists?\n",
    "\n",
    "To answer this question, we first need to write a subquery where we associate each track with its corresponding playlist id and artist and get the unique pairs (artist, playlist id), finally we group the resulting table of the subquery by artist name, we count the number of playlists for each group and we sort the resulting table by number of playlists in descending order to get the most used artist in the playlists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "        <th>appearances_in_playlists</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Eugene Ormandy</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>The King&#x27;s Singers</td>\n",
       "        <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>English Concert &amp; Trevor Pinnock</td>\n",
       "        <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Berliner Philharmoniker &amp; Herbert Von Karajan</td>\n",
       "        <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Academy of St. Martin in the Fields &amp; Sir Neville Marriner</td>\n",
       "        <td>6</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Eugene Ormandy', 7),\n",
       " (\"The King's Singers\", 6),\n",
       " ('English Concert & Trevor Pinnock', 6),\n",
       " ('Berliner Philharmoniker & Herbert Von Karajan', 6),\n",
       " ('Academy of St. Martin in the Fields & Sir Neville Marriner', 6)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT name, COUNT(*) appearances_in_playlists\n",
    "  FROM\n",
    "     (SELECT playlist_id, ar.name\n",
    "       FROM track t \n",
    "        JOIN album al on al.album_id = t.album_id\n",
    "        JOIN artist ar on ar.artist_id = al.artist_id\n",
    "        JOIN playlist_track pl on pl.track_id = t.track_id\n",
    "      GROUP BY 1,2)\n",
    "GROUP BY 1\n",
    "ORDER BY 2 desc\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Eugene Ormandy\" is the most used artist in the playlists."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°6: How many tracks have been purchased vs not purchased?\n",
    "\n",
    "To answer this question, we created two temporary tables, the first was to identify the group of the purchased tracks and the second was to label each track from the table `track` as purchased or not, finally we grouped the resulting table by label and we counted the number of tracks for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>tracks</th>\n",
       "        <th>N°</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>purchased</td>\n",
       "        <td>1806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>not purchased</td>\n",
       "        <td>1697</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('purchased', 1806), ('not purchased', 1697)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH purchased_track AS\n",
    "                 (SELECT DISTINCT(t.track_id) \n",
    "                    FROM track t\n",
    "                      JOIN invoice_line il on t.track_id = il.track_id),\n",
    "\n",
    "            t AS \n",
    "                 (SELECT track_id, CASE\n",
    "                                   WHEN track_id in purchased_track THEN 'purchased'\n",
    "                                   ELSE 'not purchased'\n",
    "                                   END tracks\n",
    "                   FROM track)\n",
    "        \n",
    "SELECT tracks, COUNT(*) N°\n",
    "FROM t\n",
    "GROUP BY tracks\n",
    "ORDER BY 2 DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost half of the tracks have never been purchased, The company need to find a way to advertise those tracks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°7: Is the range of tracks in the store reflective of their sales popularity?\n",
    "\n",
    "Here we can simply calculate the number of titles purchased for each genre of media type since this reflects its popularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>genre</th>\n",
       "        <th>N°_purchased_tracks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Rock</td>\n",
       "        <td>2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Metal</td>\n",
       "        <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative &amp; Punk</td>\n",
       "        <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Latin</td>\n",
       "        <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>R&amp;B/Soul</td>\n",
       "        <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Blues</td>\n",
       "        <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Jazz</td>\n",
       "        <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative</td>\n",
       "        <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Easy Listening</td>\n",
       "        <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Pop</td>\n",
       "        <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Electronica/Dance</td>\n",
       "        <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Classical</td>\n",
       "        <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Reggae</td>\n",
       "        <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Hip Hop/Rap</td>\n",
       "        <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Heavy Metal</td>\n",
       "        <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Soundtrack</td>\n",
       "        <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>TV Shows</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Drama</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Rock', 2635),\n",
       " ('Metal', 619),\n",
       " ('Alternative & Punk', 492),\n",
       " ('Latin', 167),\n",
       " ('R&B/Soul', 159),\n",
       " ('Blues', 124),\n",
       " ('Jazz', 121),\n",
       " ('Alternative', 117),\n",
       " ('Easy Listening', 74),\n",
       " ('Pop', 63),\n",
       " ('Electronica/Dance', 55),\n",
       " ('Classical', 47),\n",
       " ('Reggae', 35),\n",
       " ('Hip Hop/Rap', 33),\n",
       " ('Heavy Metal', 8),\n",
       " ('Soundtrack', 5),\n",
       " ('TV Shows', 2),\n",
       " ('Drama', 1)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "  SELECT g.name genre, COUNT(*) N°_purchased_tracks\n",
    "    FROM track t \n",
    "        JOIN genre g on g.genre_id = t.genre_id\n",
    "        JOIN invoice_line il on il.track_id = t.track_id\n",
    "    GROUP BY 1\n",
    "    ORDER BY 2 DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rock music is by far the most popular genre of music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business question N°8: Do protected vs non-protected media types have an effect on popularity?\n",
    "\n",
    "To answer this question we label each purchased track as \"protected\" if its media type name contains the word \"protected\" or \"not protected\" if it is not then we group the resulting table by label and calculating the number of tracks for each group. We recall that a protected media file is a media file with associated content usage rules that prevent a song, for example, from being copied or shared.\n",
    "\n",
    "Let's find out first the different media types used in the Chinook database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>MPEG audio file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Protected AAC audio file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Protected MPEG-4 video file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Purchased AAC audio file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>AAC audio file</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('MPEG audio file',),\n",
       " ('Protected AAC audio file',),\n",
       " ('Protected MPEG-4 video file',),\n",
       " ('Purchased AAC audio file',),\n",
       " ('AAC audio file',)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT DISTINCT(name)\n",
    "  FROM media_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///chinook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>media_type</th>\n",
       "        <th>popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>not protected</td>\n",
       "        <td>4315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>protected</td>\n",
       "        <td>442</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('not protected', 4315), ('protected', 442)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH tempo AS\n",
    "        (SELECT t.track_id,mt.name, CASE\n",
    "                                    WHEN mt.name LIKE '%protected%' THEN 'protected'\n",
    "                                    ELSE 'not protected'\n",
    "                                     END media_type\n",
    "          FROM track t \n",
    "            JOIN media_type mt on mt.media_type_id = t.media_type_id\n",
    "            JOIN invoice_line il on il.track_id = t.track_id)\n",
    "    \n",
    "SELECT media_type, COUNT(track_id) popularity\n",
    "FROM tempo\n",
    "GROUP BY 1\n",
    "ORDER BY 2 DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"not protected\" media type files are more popular than \"protected\" media type files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion \n",
    "\n",
    "Here are the results we got from the analysis:\n",
    "\n",
    "* \"not protected\" media type files are more popular than \"protected\" media type files.\n",
    "* Rock music is by far the most popular genre of music.\n",
    "* Almost half of the tracks have never been purchased.\n",
    "* \"Eugene Ormandy\" is the most used artist in the playlists.\n",
    "* About 81% of customers tend to buy single tracks more than purchasing the full album.\n",
    "* The highest total sales value is in the United States while the lowest is in Portugal and India.\n",
    "* About 54% of US customers prefer rock music!\n",
    "* Only 3 sales support agents work for the Chinook record store, they are Canadian and they were hired in 2017. The youngest was hired at 44!"
   ]
  }
 ],
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