Projects/step_4/2.SQL_intermediate_table_relations/Guided Project_Answering Business Questions using SQL.ipynb

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   "source": [
    "# 1. Introduction and Schema Diagram"
   ]
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   "source": [
    "%%capture\n",
    "%load_ext sql\n",
    "%sql sqlite:///chinook.db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<path d=\"M 45.5671 9.37012C 45.5671 10.5094 45.3074 11.4232 44.7878 12.1113C 44.2728 12.7949 43.5756 13.1367 42.696 13.1367C 41.9486 13.1367 41.3448 12.877 40.8845 12.3574L 40.8845 15.8438L 38.9089 15.8438L 38.9089 5.60352L 40.7409 5.60352L 40.8093 6.32812C 41.2878 5.75391 41.9122 5.4668 42.6824 5.4668C 43.5938 5.4668 44.3025 5.80404 44.8083 6.47852C 45.3142 7.15299 45.5671 8.08268 45.5671 9.26758L 45.5671 9.37012ZM 43.5915 9.22656C 43.5915 8.53841 43.4685 8.00749 43.2224 7.63379C 42.9809 7.26009 42.6277 7.07324 42.1628 7.07324C 41.543 7.07324 41.1169 7.31022 40.8845 7.78418L 40.8845 10.8125C 41.126 11.3001 41.5567 11.5439 42.1765 11.5439C 43.1199 11.5439 43.5915 10.7715 43.5915 9.22656ZM 48.8825 13L 46.9001 13L 46.9001 2.5L 48.8825 2.5L 48.8825 13ZM 54.8025 13C 54.7113 12.8223 54.6452 12.6012 54.6042 12.3369C 54.1257 12.8701 53.5036 13.1367 52.738 13.1367C 52.0134 13.1367 51.4118 12.9271 50.9333 12.5078C 50.4594 12.0885 50.2224 11.5599 50.2224 10.9219C 50.2224 10.138 50.5118 9.53646 51.0906 9.11719C 51.6739 8.69792 52.5147 8.486 53.613 8.48145L 54.5222 8.48145L 54.5222 8.05762C 54.5222 7.71582 54.4333 7.44238 54.2556 7.2373C 54.0824 7.03223 53.8067 6.92969 53.4284 6.92969C 53.0958 6.92969 52.8337 7.00944 52.6423 7.16895C 52.4555 7.32845 52.362 7.5472 52.362 7.8252L 50.3865 7.8252C 50.3865 7.39681 50.5186 7.00033 50.7829 6.63574C 51.0473 6.27116 51.421 5.98633 51.904 5.78125C 52.3871 5.57161 52.9294 5.4668 53.531 5.4668C 54.4424 5.4668 55.1648 5.69694 55.698 6.15723C 56.2357 6.61296 56.5046 7.25553 56.5046 8.08496L 56.5046 11.291C 56.5092 11.9928 56.6072 12.5238 56.7986 12.8838L 56.7986 13L 54.8025 13ZM 53.1687 11.626C 53.4603 11.626 53.7292 11.5622 53.9753 11.4346C 54.2214 11.3024 54.4037 11.127 54.5222 10.9082L 54.5222 9.63672L 53.7839 9.63672C 52.795 9.63672 52.2686 9.97852 52.2048 10.6621L 52.198 10.7783C 52.198 11.0244 52.2846 11.2272 52.4577 11.3867C 52.6309 11.5462 52.8679 11.626 53.1687 11.626ZM 60.6745 10.2041L 62.0417 5.60352L 64.1609 5.60352L 61.1872 14.1484L 61.0232 14.5381C 60.5811 15.5042 59.8519 15.9873 58.8357 15.9873C 58.5486 15.9873 58.2569 15.944 57.9607 15.8574L 57.9607 14.3604L 58.2615 14.3672C 58.6351 14.3672 58.9131 14.3102 59.0954 14.1963C 59.2823 14.0824 59.4281 13.8932 59.5329 13.6289L 59.7654 13.0205L 57.1745 5.60352L 59.3005 5.60352L 60.6745 10.2041ZM 67.0251 13L 65.0427 13L 65.0427 2.5L 67.0251 2.5L 67.0251 13ZM 70.7439 13L 68.7615 13L 68.7615 5.60352L 70.7439 5.60352L 70.7439 13ZM 68.6452 3.68945C 68.6452 3.39323 68.7432 3.14941 68.9392 2.95801C 69.1397 2.7666 69.4109 2.6709 69.7527 2.6709C 70.0899 2.6709 70.3588 2.7666 70.5593 2.95801C 70.7598 3.14941 70.8601 3.39323 70.8601 3.68945C 70.8601 3.99023 70.7575 4.23633 70.5525 4.42773C 70.3519 4.61914 70.0853 4.71484 69.7527 4.71484C 69.42 4.71484 69.1511 4.61914 68.946 4.42773C 68.7455 4.23633 68.6452 3.99023 68.6452 3.68945ZM 76.3835 10.9561C 76.3835 10.7145 76.2628 10.5254 76.0212 10.3887C 75.7842 10.2474 75.4014 10.1221 74.8728 10.0127C 73.1137 9.64355 72.2341 8.89616 72.2341 7.77051C 72.2341 7.11426 72.5053 6.56738 73.0476 6.12988C 73.5945 5.68783 74.3077 5.4668 75.1872 5.4668C 76.126 5.4668 76.8757 5.68783 77.4363 6.12988C 78.0014 6.57194 78.2839 7.14616 78.2839 7.85254L 76.3083 7.85254C 76.3083 7.56999 76.2172 7.33757 76.0349 7.15527C 75.8526 6.96842 75.5678 6.875 75.1804 6.875C 74.8477 6.875 74.5902 6.9502 74.4079 7.10059C 74.2256 7.25098 74.1345 7.44238 74.1345 7.6748C 74.1345 7.89355 74.237 8.07129 74.4421 8.20801C 74.6518 8.34017 75.0027 8.45638 75.4949 8.55664C 75.987 8.65234 76.4018 8.76172 76.739 8.88477C 77.7826 9.26758 78.3044 9.93066 78.3044 10.874C 78.3044 11.5485 78.015 12.0954 77.4363 12.5146C 76.8575 12.9294 76.1101 13.1367 75.1941 13.1367C 74.5743 13.1367 74.0228 13.0273 73.5398 12.8086C 73.0613 12.5853 72.6853 12.2822 72.4118 11.8994C 72.1384 11.512 72.0017 11.0951 72.0017 10.6484L 73.8747 10.6484C 73.893 10.9993 74.0228 11.2682 74.2644 11.4551C 74.5059 11.6419 74.8295 11.7354 75.2351 11.7354C 75.6133 11.7354 75.8982 11.6647 76.0896 11.5234C 76.2855 11.3776 76.3835 11.1885 76.3835 10.9561ZM 81.948 3.78516L 81.948 5.60352L 83.2126 5.60352L 83.2126 7.05273L 81.948 7.05273L 81.948 10.7441C 81.948 11.0176 82.0004 11.2135 82.1052 11.332C 82.21 11.4505 82.4105 11.5098 82.7068 11.5098C 82.9255 11.5098 83.1192 11.4938 83.2878 11.4619L 83.2878 12.959C 82.9004 13.0775 82.5017 13.1367 82.0915 13.1367C 80.7061 13.1367 79.9997 12.4372 79.9724 11.0381L 79.9724 7.05273L 78.8923 7.05273L 78.8923 5.60352L 79.9724 5.60352L 79.9724 3.78516L 81.948 3.78516Z\" id=\"path63_fill\"/>\n",
       "<path d=\"M 0 0L 77.6209 0L 77.6209 -2L 0 -2L 0 0Z\" id=\"path64_stroke\"/>\n",
       "</defs>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "SVG(filename='chinook-schema.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Overview of the Data"
   ]
  },
  {
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT\n",
    "    name,\n",
    "    type\n",
    "FROM sqlite_master\n",
    "WHERE type IN (\"table\",\"view\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Selecting Albums to Purchase"
   ]
  },
  {
   "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>tracks_sold</th>\n",
       "        <th>percentage_sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Rock</td>\n",
       "        <td>561</td>\n",
       "        <td>0.5337773549000951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative &amp; Punk</td>\n",
       "        <td>130</td>\n",
       "        <td>0.12369172216936251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Metal</td>\n",
       "        <td>124</td>\n",
       "        <td>0.11798287345385347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>R&amp;B/Soul</td>\n",
       "        <td>53</td>\n",
       "        <td>0.05042816365366318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Blues</td>\n",
       "        <td>36</td>\n",
       "        <td>0.03425309229305423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Alternative</td>\n",
       "        <td>35</td>\n",
       "        <td>0.03330161750713606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Pop</td>\n",
       "        <td>22</td>\n",
       "        <td>0.02093244529019981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Latin</td>\n",
       "        <td>22</td>\n",
       "        <td>0.02093244529019981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Hip Hop/Rap</td>\n",
       "        <td>20</td>\n",
       "        <td>0.019029495718363463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Jazz</td>\n",
       "        <td>14</td>\n",
       "        <td>0.013320647002854425</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Rock', 561, 0.5337773549000951),\n",
       " ('Alternative & Punk', 130, 0.12369172216936251),\n",
       " ('Metal', 124, 0.11798287345385347),\n",
       " ('R&B/Soul', 53, 0.05042816365366318),\n",
       " ('Blues', 36, 0.03425309229305423),\n",
       " ('Alternative', 35, 0.03330161750713606),\n",
       " ('Pop', 22, 0.02093244529019981),\n",
       " ('Latin', 22, 0.02093244529019981),\n",
       " ('Hip Hop/Rap', 20, 0.019029495718363463),\n",
       " ('Jazz', 14, 0.013320647002854425)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH usa_tracks_sold AS\n",
    "   (\n",
    "    SELECT il.* FROM invoice_line il\n",
    "    INNER JOIN invoice i on il.invoice_id = i.invoice_id\n",
    "    INNER JOIN customer c on i.customer_id = c.customer_id\n",
    "    WHERE c.country = \"USA\"\n",
    "   )\n",
    "\n",
    "SELECT\n",
    "    g.name genre,\n",
    "    count(uts.invoice_line_id) tracks_sold,\n",
    "    cast(count(uts.invoice_line_id) AS FLOAT) / (\n",
    "        SELECT COUNT(*) from usa_tracks_sold\n",
    "    ) percentage_sold\n",
    "FROM usa_tracks_sold uts\n",
    "INNER JOIN track t on t.track_id = uts.track_id\n",
    "INNER JOIN genre g on g.genre_id = t.genre_id\n",
    "GROUP BY 1\n",
    "ORDER BY 2 DESC\n",
    "LIMIT 10;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Based on the sales of tracks across different genres in the USA, we should purchase the new albums by the following artists:\n",
    "\n",
    "* Red Tone (Punk)\n",
    "* Slim Jim Bites (Blues)\n",
    "* Meteor and the Girls (Pop)\n",
    "\n",
    "It's worth keeping in mind that combined, these three genres only make up only 17% of total sales, so we should be on the lookout for artists and albums from the 'rock' genre, which accounts for 53% of sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Analyzing Employee Sales Performance"
   ]
  },
  {
   "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>employee</th>\n",
       "        <th>hire_date</th>\n",
       "        <th>total_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Jane Peacock</td>\n",
       "        <td>2017-04-01 00:00:00</td>\n",
       "        <td>1731.5099999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Margaret Park</td>\n",
       "        <td>2017-05-03 00:00:00</td>\n",
       "        <td>1584.0000000000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Steve Johnson</td>\n",
       "        <td>2017-10-17 00:00:00</td>\n",
       "        <td>1393.92</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Jane Peacock', '2017-04-01 00:00:00', 1731.5099999999998),\n",
       " ('Margaret Park', '2017-05-03 00:00:00', 1584.0000000000002),\n",
       " ('Steve Johnson', '2017-10-17 00:00:00', 1393.92)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH customer_support_rep_sales AS\n",
    "    (\n",
    "     SELECT\n",
    "         i.customer_id,\n",
    "         c.support_rep_id,\n",
    "         SUM(i.total) total\n",
    "     FROM invoice i\n",
    "     INNER JOIN customer c ON i.customer_id = c.customer_id\n",
    "     GROUP BY 1,2\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    e.first_name || \" \" || e.last_name employee,\n",
    "    e.hire_date,\n",
    "    SUM(csrs.total) total_sales\n",
    "FROM customer_support_rep_sales csrs\n",
    "INNER JOIN employee e ON e.employee_id = csrs.support_rep_id\n",
    "GROUP BY 1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "While there is a 20% difference in sales between Jane (the top employee) and Steve (the bottom employee), the difference roughly corresponds with the differences in their hiring dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analyzing Sales by Country"
   ]
  },
  {
   "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>country</th>\n",
       "        <th>customers</th>\n",
       "        <th>total_sales</th>\n",
       "        <th>average_order</th>\n",
       "        <th>customer_lifetime_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>USA</td>\n",
       "        <td>13</td>\n",
       "        <td>1040.490000000008</td>\n",
       "        <td>7.942671755725252</td>\n",
       "        <td>80.03769230769292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Canada</td>\n",
       "        <td>8</td>\n",
       "        <td>535.5900000000034</td>\n",
       "        <td>7.047236842105309</td>\n",
       "        <td>66.94875000000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Brazil</td>\n",
       "        <td>5</td>\n",
       "        <td>427.68000000000245</td>\n",
       "        <td>7.011147540983647</td>\n",
       "        <td>85.53600000000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>France</td>\n",
       "        <td>5</td>\n",
       "        <td>389.0700000000021</td>\n",
       "        <td>7.781400000000042</td>\n",
       "        <td>77.81400000000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Germany</td>\n",
       "        <td>4</td>\n",
       "        <td>334.6200000000016</td>\n",
       "        <td>8.161463414634186</td>\n",
       "        <td>83.6550000000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Czech Republic</td>\n",
       "        <td>2</td>\n",
       "        <td>273.24000000000103</td>\n",
       "        <td>9.108000000000034</td>\n",
       "        <td>136.62000000000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>United Kingdom</td>\n",
       "        <td>3</td>\n",
       "        <td>245.52000000000078</td>\n",
       "        <td>8.768571428571457</td>\n",
       "        <td>81.84000000000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Portugal</td>\n",
       "        <td>2</td>\n",
       "        <td>185.13000000000022</td>\n",
       "        <td>6.3837931034482835</td>\n",
       "        <td>92.56500000000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>India</td>\n",
       "        <td>2</td>\n",
       "        <td>183.1500000000002</td>\n",
       "        <td>8.72142857142858</td>\n",
       "        <td>91.5750000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Other</td>\n",
       "        <td>15</td>\n",
       "        <td>1094.9400000000085</td>\n",
       "        <td>7.448571428571486</td>\n",
       "        <td>72.99600000000056</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('USA', 13, 1040.490000000008, 7.942671755725252, 80.03769230769292),\n",
       " ('Canada', 8, 535.5900000000034, 7.047236842105309, 66.94875000000043),\n",
       " ('Brazil', 5, 427.68000000000245, 7.011147540983647, 85.53600000000048),\n",
       " ('France', 5, 389.0700000000021, 7.781400000000042, 77.81400000000042),\n",
       " ('Germany', 4, 334.6200000000016, 8.161463414634186, 83.6550000000004),\n",
       " ('Czech Republic', 2, 273.24000000000103, 9.108000000000034, 136.62000000000052),\n",
       " ('United Kingdom', 3, 245.52000000000078, 8.768571428571457, 81.84000000000026),\n",
       " ('Portugal', 2, 185.13000000000022, 6.3837931034482835, 92.56500000000011),\n",
       " ('India', 2, 183.1500000000002, 8.72142857142858, 91.5750000000001),\n",
       " ('Other', 15, 1094.9400000000085, 7.448571428571486, 72.99600000000056)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH country_or_other AS\n",
    "    (\n",
    "     SELECT\n",
    "       CASE\n",
    "           WHEN (\n",
    "                 SELECT count(*)\n",
    "                 FROM customer\n",
    "                 where country = c.country\n",
    "                ) = 1 THEN \"Other\"\n",
    "           ELSE c.country\n",
    "       END AS country,\n",
    "       c.customer_id,\n",
    "       il.*\n",
    "     FROM invoice_line il\n",
    "     INNER JOIN invoice i ON i.invoice_id = il.invoice_id\n",
    "     INNER JOIN customer c ON c.customer_id = i.customer_id\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    country,\n",
    "    customers,\n",
    "    total_sales,\n",
    "    average_order,\n",
    "    customer_lifetime_value\n",
    "FROM\n",
    "    (\n",
    "    SELECT\n",
    "        country,\n",
    "        count(distinct customer_id) customers,\n",
    "        SUM(unit_price) total_sales,\n",
    "        SUM(unit_price) / count(distinct customer_id) customer_lifetime_value,\n",
    "        SUM(unit_price) / count(distinct invoice_id) average_order,\n",
    "        CASE\n",
    "            WHEN country = \"Other\" THEN 1\n",
    "            ELSE 0\n",
    "        END AS sort\n",
    "    FROM country_or_other\n",
    "    GROUP BY country\n",
    "    ORDER BY sort ASC, total_sales DESC\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the data, there may be opportunity in the following countries:\n",
    "\n",
    "Czech Republic\n",
    "United Kingdom\n",
    "India\n",
    "It's worth keeping in mind that because the amount of data from each of these countries is relatively low. Because of this, we should be cautious spending too much money on new marketing campaigns, as the sample size is not large enough to give us high confidence. A better approach would be to run small campaigns in these countries, collecting and analyzing the new customers to make sure that these trends hold with new customers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Albums vs Individual Tracks"
   ]
  },
  {
   "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>album_purchase</th>\n",
       "        <th>number_of_invoices</th>\n",
       "        <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>no</td>\n",
       "        <td>500</td>\n",
       "        <td>0.8143322475570033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>yes</td>\n",
       "        <td>114</td>\n",
       "        <td>0.18566775244299674</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('no', 500, 0.8143322475570033), ('yes', 114, 0.18566775244299674)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "WITH invoice_first_track AS\n",
    "    (\n",
    "     SELECT\n",
    "         il.invoice_id invoice_id,\n",
    "         MIN(il.track_id) first_track_id\n",
    "     FROM invoice_line il\n",
    "     GROUP BY 1\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    album_purchase,\n",
    "    COUNT(invoice_id) number_of_invoices,\n",
    "    CAST(count(invoice_id) AS FLOAT) / (\n",
    "                                         SELECT COUNT(*) FROM invoice\n",
    "                                      ) percent\n",
    "FROM\n",
    "    (\n",
    "    SELECT\n",
    "        ifs.*,\n",
    "        CASE\n",
    "            WHEN\n",
    "                 (\n",
    "                  SELECT t.track_id FROM track t\n",
    "                  WHERE t.album_id = (\n",
    "                                      SELECT t2.album_id FROM track t2\n",
    "                                      WHERE t2.track_id = ifs.first_track_id\n",
    "                                     ) \n",
    "\n",
    "                  EXCEPT \n",
    "\n",
    "                  SELECT il2.track_id FROM invoice_line il2\n",
    "                  WHERE il2.invoice_id = ifs.invoice_id\n",
    "                 ) IS NULL\n",
    "             AND\n",
    "                 (\n",
    "                  SELECT il2.track_id FROM invoice_line il2\n",
    "                  WHERE il2.invoice_id = ifs.invoice_id\n",
    "\n",
    "                  EXCEPT \n",
    "\n",
    "                  SELECT t.track_id FROM track t\n",
    "                  WHERE t.album_id = (\n",
    "                                      SELECT t2.album_id FROM track t2\n",
    "                                      WHERE t2.track_id = ifs.first_track_id\n",
    "                                     ) \n",
    "                 ) IS NULL\n",
    "             THEN \"yes\"\n",
    "             ELSE \"no\"\n",
    "         END AS \"album_purchase\"\n",
    "     FROM invoice_first_track ifs\n",
    "    )\n",
    "GROUP BY album_purchase;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Album purchases account for 18.6% of purchases. Based on this data, I would recommend against purchasing only select tracks from albums from record companies, since there is potential to lose one fifth of revenue."
   ]
  }
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