{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ef9b5e81-cbc1-4f29-8310-ebfc812bbeb0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "from DATA225utils import make_connection, dataframe_query" ] }, { "cell_type": "code", "execution_count": 2, "id": "a6631a5e-512e-45bd-9849-a7539d4c4ab2", "metadata": { "tags": [] }, "outputs": [], "source": [ "conn = make_connection(config_file='checkpoint3.ini')\n", "pd.set_option('display.max_rows', None)" ] }, { "cell_type": "code", "execution_count": 3, "id": "f8ea089f-6d2a-4f50-a3b5-72ce310ba63b", "metadata": { "tags": [] }, "outputs": [], "source": [ "def show_results(sql):\n", " _, df = dataframe_query(conn, sql)\n", " display(df)" ] }, { "cell_type": "markdown", "id": "51bebcbe-9142-4011-a431-4d3ccb450e7c", "metadata": {}, "source": [ "## What's there?\n", "#### By the way, the scores are meaningless values randomly generated for this exam." ] }, { "cell_type": "code", "execution_count": 4, "id": "024c269b-1f6c-4e2e-bd12-54c5aa069fc2", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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cpu_modelos_nameos_versionrelease_datebenchmark_specrating_score
0i7Windows 1021H22021-11-16FLOAT-14.6
1i7Windows 1021H22021-11-16FLOAT-23.8
2i7Windows 1021H22021-11-16INT-13.9
3i7Windows 1021H22021-11-16INT-24.5
4i7Windows 1021H22021-11-16STRING-14.7
5i7Windows 1021H22021-11-16STRING-24.8
6i7Windows 1022H22022-10-18FLOAT-13.1
7i7Windows 1022H22022-10-18FLOAT-23.6
8i7Windows 1022H22022-10-18INT-14.0
9i7Windows 1022H22022-10-18INT-23.1
10i7Windows 1022H22022-10-18STRING-14.9
11i7Windows 1022H22022-10-18STRING-23.3
12i7Windows 1122H22022-09-20FLOAT-14.9
13i7Windows 1122H22022-09-20FLOAT-24.7
14i7Windows 1122H22022-09-20INT-13.7
15i7Windows 1122H22022-09-20INT-23.7
16i7Windows 1122H22022-09-20STRING-14.4
17i7Windows 1122H22022-09-20STRING-24.1
18i7Windows 1123H22023-10-31FLOAT-14.6
19i7Windows 1123H22023-10-31FLOAT-24.0
20i7Windows 1123H22023-10-31INT-14.6
21i7Windows 1123H22023-10-31INT-23.9
22i7Windows 1123H22023-10-31STRING-13.4
23i7Windows 1123H22023-10-31STRING-23.5
24i9Windows 1021H22021-11-16FLOAT-13.6
25i9Windows 1021H22021-11-16FLOAT-23.0
26i9Windows 1021H22021-11-16INT-13.5
27i9Windows 1021H22021-11-16INT-23.1
28i9Windows 1021H22021-11-16STRING-13.6
29i9Windows 1021H22021-11-16STRING-24.5
30i9Windows 1022H22022-10-18FLOAT-14.8
31i9Windows 1022H22022-10-18FLOAT-23.8
32i9Windows 1022H22022-10-18INT-13.9
33i9Windows 1022H22022-10-18INT-23.4
34i9Windows 1022H22022-10-18STRING-14.5
35i9Windows 1022H22022-10-18STRING-23.4
36i9Windows 1122H22022-09-20FLOAT-14.5
37i9Windows 1122H22022-09-20FLOAT-23.7
38i9Windows 1122H22022-09-20INT-13.8
39i9Windows 1122H22022-09-20INT-24.8
40i9Windows 1122H22022-09-20STRING-14.9
41i9Windows 1122H22022-09-20STRING-24.1
42i9Windows 1123H22023-10-31FLOAT-14.6
43i9Windows 1123H22023-10-31FLOAT-23.3
44i9Windows 1123H22023-10-31INT-14.1
45i9Windows 1123H22023-10-31INT-23.3
46i9Windows 1123H22023-10-31STRING-14.4
47i9Windows 1123H22023-10-31STRING-24.3
48M2macOS132022-10-24FLOAT-13.1
49M2macOS132022-10-24FLOAT-24.6
50M2macOS132022-10-24INT-14.7
51M2macOS132022-10-24INT-23.7
52M2macOS132022-10-24STRING-13.7
53M2macOS132022-10-24STRING-23.1
54M2macOS142023-09-26FLOAT-13.8
55M2macOS142023-09-26FLOAT-23.5
56M2macOS142023-09-26INT-13.8
57M2macOS142023-09-26INT-24.8
58M2macOS142023-09-26STRING-13.4
59M2macOS142023-09-26STRING-23.8
60M2OS X10.12014-10-16FLOAT-14.4
61M2OS X10.12014-10-16FLOAT-24.6
62M2OS X10.12014-10-16INT-13.9
63M2OS X10.12014-10-16INT-23.2
64M2OS X10.12014-10-16STRING-13.2
65M2OS X10.12014-10-16STRING-23.1
66M2OS X10.112015-09-30FLOAT-13.1
67M2OS X10.112015-09-30FLOAT-23.3
68M2OS X10.112015-09-30INT-14.6
69M2OS X10.112015-09-30INT-24.6
70M2OS X10.112015-09-30STRING-14.8
71M2OS X10.112015-09-30STRING-23.8
72M3macOS132022-10-24FLOAT-13.0
73M3macOS132022-10-24FLOAT-23.6
74M3macOS132022-10-24INT-13.8
75M3macOS132022-10-24INT-24.7
76M3macOS132022-10-24STRING-13.1
77M3macOS132022-10-24STRING-23.3
78M3macOS142023-09-26FLOAT-14.3
79M3macOS142023-09-26FLOAT-24.4
80M3macOS142023-09-26INT-13.2
81M3macOS142023-09-26INT-23.8
82M3macOS142023-09-26STRING-14.5
83M3macOS142023-09-26STRING-24.2
84M3OS X10.12014-10-16FLOAT-14.8
85M3OS X10.12014-10-16FLOAT-23.5
86M3OS X10.12014-10-16INT-14.0
87M3OS X10.12014-10-16INT-23.2
88M3OS X10.12014-10-16STRING-13.5
89M3OS X10.12014-10-16STRING-24.3
90M3OS X10.112015-09-30FLOAT-14.1
91M3OS X10.112015-09-30FLOAT-24.8
92M3OS X10.112015-09-30INT-13.9
93M3OS X10.112015-09-30INT-24.5
94M3OS X10.112015-09-30STRING-14.3
95M3OS X10.112015-09-30STRING-24.6
\n", "
" ], "text/plain": [ " cpu_model os_name os_version release_date benchmark_spec rating_score\n", "0 i7 Windows 10 21H2 2021-11-16 FLOAT-1 4.6\n", "1 i7 Windows 10 21H2 2021-11-16 FLOAT-2 3.8\n", "2 i7 Windows 10 21H2 2021-11-16 INT-1 3.9\n", "3 i7 Windows 10 21H2 2021-11-16 INT-2 4.5\n", "4 i7 Windows 10 21H2 2021-11-16 STRING-1 4.7\n", "5 i7 Windows 10 21H2 2021-11-16 STRING-2 4.8\n", "6 i7 Windows 10 22H2 2022-10-18 FLOAT-1 3.1\n", "7 i7 Windows 10 22H2 2022-10-18 FLOAT-2 3.6\n", "8 i7 Windows 10 22H2 2022-10-18 INT-1 4.0\n", "9 i7 Windows 10 22H2 2022-10-18 INT-2 3.1\n", "10 i7 Windows 10 22H2 2022-10-18 STRING-1 4.9\n", "11 i7 Windows 10 22H2 2022-10-18 STRING-2 3.3\n", "12 i7 Windows 11 22H2 2022-09-20 FLOAT-1 4.9\n", "13 i7 Windows 11 22H2 2022-09-20 FLOAT-2 4.7\n", "14 i7 Windows 11 22H2 2022-09-20 INT-1 3.7\n", "15 i7 Windows 11 22H2 2022-09-20 INT-2 3.7\n", "16 i7 Windows 11 22H2 2022-09-20 STRING-1 4.4\n", "17 i7 Windows 11 22H2 2022-09-20 STRING-2 4.1\n", "18 i7 Windows 11 23H2 2023-10-31 FLOAT-1 4.6\n", "19 i7 Windows 11 23H2 2023-10-31 FLOAT-2 4.0\n", "20 i7 Windows 11 23H2 2023-10-31 INT-1 4.6\n", "21 i7 Windows 11 23H2 2023-10-31 INT-2 3.9\n", "22 i7 Windows 11 23H2 2023-10-31 STRING-1 3.4\n", "23 i7 Windows 11 23H2 2023-10-31 STRING-2 3.5\n", "24 i9 Windows 10 21H2 2021-11-16 FLOAT-1 3.6\n", "25 i9 Windows 10 21H2 2021-11-16 FLOAT-2 3.0\n", "26 i9 Windows 10 21H2 2021-11-16 INT-1 3.5\n", "27 i9 Windows 10 21H2 2021-11-16 INT-2 3.1\n", "28 i9 Windows 10 21H2 2021-11-16 STRING-1 3.6\n", "29 i9 Windows 10 21H2 2021-11-16 STRING-2 4.5\n", "30 i9 Windows 10 22H2 2022-10-18 FLOAT-1 4.8\n", "31 i9 Windows 10 22H2 2022-10-18 FLOAT-2 3.8\n", "32 i9 Windows 10 22H2 2022-10-18 INT-1 3.9\n", "33 i9 Windows 10 22H2 2022-10-18 INT-2 3.4\n", "34 i9 Windows 10 22H2 2022-10-18 STRING-1 4.5\n", "35 i9 Windows 10 22H2 2022-10-18 STRING-2 3.4\n", "36 i9 Windows 11 22H2 2022-09-20 FLOAT-1 4.5\n", "37 i9 Windows 11 22H2 2022-09-20 FLOAT-2 3.7\n", "38 i9 Windows 11 22H2 2022-09-20 INT-1 3.8\n", "39 i9 Windows 11 22H2 2022-09-20 INT-2 4.8\n", "40 i9 Windows 11 22H2 2022-09-20 STRING-1 4.9\n", "41 i9 Windows 11 22H2 2022-09-20 STRING-2 4.1\n", "42 i9 Windows 11 23H2 2023-10-31 FLOAT-1 4.6\n", "43 i9 Windows 11 23H2 2023-10-31 FLOAT-2 3.3\n", "44 i9 Windows 11 23H2 2023-10-31 INT-1 4.1\n", "45 i9 Windows 11 23H2 2023-10-31 INT-2 3.3\n", "46 i9 Windows 11 23H2 2023-10-31 STRING-1 4.4\n", "47 i9 Windows 11 23H2 2023-10-31 STRING-2 4.3\n", "48 M2 macOS 13 2022-10-24 FLOAT-1 3.1\n", "49 M2 macOS 13 2022-10-24 FLOAT-2 4.6\n", "50 M2 macOS 13 2022-10-24 INT-1 4.7\n", "51 M2 macOS 13 2022-10-24 INT-2 3.7\n", "52 M2 macOS 13 2022-10-24 STRING-1 3.7\n", "53 M2 macOS 13 2022-10-24 STRING-2 3.1\n", "54 M2 macOS 14 2023-09-26 FLOAT-1 3.8\n", "55 M2 macOS 14 2023-09-26 FLOAT-2 3.5\n", "56 M2 macOS 14 2023-09-26 INT-1 3.8\n", "57 M2 macOS 14 2023-09-26 INT-2 4.8\n", "58 M2 macOS 14 2023-09-26 STRING-1 3.4\n", "59 M2 macOS 14 2023-09-26 STRING-2 3.8\n", "60 M2 OS X 10.1 2014-10-16 FLOAT-1 4.4\n", "61 M2 OS X 10.1 2014-10-16 FLOAT-2 4.6\n", "62 M2 OS X 10.1 2014-10-16 INT-1 3.9\n", "63 M2 OS X 10.1 2014-10-16 INT-2 3.2\n", "64 M2 OS X 10.1 2014-10-16 STRING-1 3.2\n", "65 M2 OS X 10.1 2014-10-16 STRING-2 3.1\n", "66 M2 OS X 10.11 2015-09-30 FLOAT-1 3.1\n", "67 M2 OS X 10.11 2015-09-30 FLOAT-2 3.3\n", "68 M2 OS X 10.11 2015-09-30 INT-1 4.6\n", "69 M2 OS X 10.11 2015-09-30 INT-2 4.6\n", "70 M2 OS X 10.11 2015-09-30 STRING-1 4.8\n", "71 M2 OS X 10.11 2015-09-30 STRING-2 3.8\n", "72 M3 macOS 13 2022-10-24 FLOAT-1 3.0\n", "73 M3 macOS 13 2022-10-24 FLOAT-2 3.6\n", "74 M3 macOS 13 2022-10-24 INT-1 3.8\n", "75 M3 macOS 13 2022-10-24 INT-2 4.7\n", "76 M3 macOS 13 2022-10-24 STRING-1 3.1\n", "77 M3 macOS 13 2022-10-24 STRING-2 3.3\n", "78 M3 macOS 14 2023-09-26 FLOAT-1 4.3\n", "79 M3 macOS 14 2023-09-26 FLOAT-2 4.4\n", "80 M3 macOS 14 2023-09-26 INT-1 3.2\n", "81 M3 macOS 14 2023-09-26 INT-2 3.8\n", "82 M3 macOS 14 2023-09-26 STRING-1 4.5\n", "83 M3 macOS 14 2023-09-26 STRING-2 4.2\n", "84 M3 OS X 10.1 2014-10-16 FLOAT-1 4.8\n", "85 M3 OS X 10.1 2014-10-16 FLOAT-2 3.5\n", "86 M3 OS X 10.1 2014-10-16 INT-1 4.0\n", "87 M3 OS X 10.1 2014-10-16 INT-2 3.2\n", "88 M3 OS X 10.1 2014-10-16 STRING-1 3.5\n", "89 M3 OS X 10.1 2014-10-16 STRING-2 4.3\n", "90 M3 OS X 10.11 2015-09-30 FLOAT-1 4.1\n", "91 M3 OS X 10.11 2015-09-30 FLOAT-2 4.8\n", "92 M3 OS X 10.11 2015-09-30 INT-1 3.9\n", "93 M3 OS X 10.11 2015-09-30 INT-2 4.5\n", "94 M3 OS X 10.11 2015-09-30 STRING-1 4.3\n", "95 M3 OS X 10.11 2015-09-30 STRING-2 4.6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT cpu.model AS cpu_model, \n", " os.name AS os_name, \n", " os.version AS os_version, \n", " os.release_date,\n", " benchmark.spec AS benchmark_spec, \n", " rating.score AS rating_score\n", " FROM cpu, os, benchmark, rating\n", " WHERE rating.cpu = cpu.skey\n", " AND rating.os = os.skey\n", " AND rating.benchmark = benchmark.skey\n", " ORDER BY cpu.model, os.name, os.version, \n", " benchmark.spec, rating.score\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "061dbaa2-2a5e-403d-b007-0f29482020d6", "metadata": {}, "source": [ "## QUESTION 1: What is the overall average of all the scores?" ] }, { "cell_type": "code", "execution_count": 5, "id": "45367eca-bf25-4eb8-b3c3-7e19c0e487a8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AVG(score)
03.965625
\n", "
" ], "text/plain": [ " AVG(score)\n", "0 3.965625" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT AVG(score)\n", " FROM rating\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "a5e08cc3-4001-445f-802c-f51e1d29012a", "metadata": {}, "source": [ "## QUESTION 2: For each CPU model, what is its overall average?" ] }, { "cell_type": "code", "execution_count": 6, "id": "bb60f511-ea6b-4982-8fd3-082054fd0a28", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
modelAVG(score)
0i74.075000
1i93.954167
2M23.858333
3M33.975000
\n", "
" ], "text/plain": [ " model AVG(score)\n", "0 i7 4.075000\n", "1 i9 3.954167\n", "2 M2 3.858333\n", "3 M3 3.975000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT model, AVG(score)\n", " FROM cpu, rating\n", " WHERE rating.cpu = cpu.skey\n", " GROUP BY model\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "f2937280-ed8e-4349-a4e9-b5bb173c85aa", "metadata": { "tags": [] }, "source": [ "## QUESTION 3: What are the overall average scores for operating systems released in 2022 or later running on CPUs i9 and M3?" ] }, { "cell_type": "code", "execution_count": 7, "id": "c2ca415f-0655-47df-adad-c470d3ee6421", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
modelnameversionrelease_dateAVG(score)
0i9Windows 1022H22022-10-183.966667
1i9Windows 1122H22022-09-204.300000
2i9Windows 1123H22023-10-314.000000
3M3macOS132022-10-243.583333
4M3macOS142023-09-264.066667
\n", "
" ], "text/plain": [ " model name version release_date AVG(score)\n", "0 i9 Windows 10 22H2 2022-10-18 3.966667\n", "1 i9 Windows 11 22H2 2022-09-20 4.300000\n", "2 i9 Windows 11 23H2 2023-10-31 4.000000\n", "3 M3 macOS 13 2022-10-24 3.583333\n", "4 M3 macOS 14 2023-09-26 4.066667" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT model, os.name, version, release_date, AVG(score)\n", " FROM cpu, os, benchmark, rating\n", " WHERE cpu.model IN ('i9', 'M3')\n", " AND os.year >= 2022\n", " AND rating.cpu = cpu.skey\n", " AND rating.os = os.skey\n", " AND rating.benchmark = benchmark.skey\n", " GROUP BY model, os.name, release_date, version\n", " ORDER BY model, os.name, version\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "c8e800af-1582-481e-a660-f2bb9276e4b4", "metadata": { "tags": [] }, "source": [ "## QUESTION 4: What is the average score of each operating system version running on each CPU model? Order the results by operating system name, version, and CPU model." ] }, { "cell_type": "code", "execution_count": 8, "id": "0fe04d1b-5c3f-4786-bfd8-186ff9f1e113", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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nameversionmodelAVG(score)
0macOS13M23.816667
1macOS13M33.583333
2macOS14M23.850000
3macOS14M34.066667
4OS X10.1M23.733333
5OS X10.1M33.883333
6OS X10.11M24.033333
7OS X10.11M34.366667
8Windows 1021H2i74.383333
9Windows 1021H2i93.550000
10Windows 1022H2i73.666667
11Windows 1022H2i93.966667
12Windows 1122H2i74.250000
13Windows 1122H2i94.300000
14Windows 1123H2i74.000000
15Windows 1123H2i94.000000
\n", "
" ], "text/plain": [ " name version model AVG(score)\n", "0 macOS 13 M2 3.816667\n", "1 macOS 13 M3 3.583333\n", "2 macOS 14 M2 3.850000\n", "3 macOS 14 M3 4.066667\n", "4 OS X 10.1 M2 3.733333\n", "5 OS X 10.1 M3 3.883333\n", "6 OS X 10.11 M2 4.033333\n", "7 OS X 10.11 M3 4.366667\n", "8 Windows 10 21H2 i7 4.383333\n", "9 Windows 10 21H2 i9 3.550000\n", "10 Windows 10 22H2 i7 3.666667\n", "11 Windows 10 22H2 i9 3.966667\n", "12 Windows 11 22H2 i7 4.250000\n", "13 Windows 11 22H2 i9 4.300000\n", "14 Windows 11 23H2 i7 4.000000\n", "15 Windows 11 23H2 i9 4.000000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT os.name, os.version, cpu.model, AVG(score)\n", " FROM cpu, os, rating\n", " WHERE rating.cpu = cpu.skey\n", " AND rating.os = os.skey\n", " GROUP BY os.name, os.version, cpu.model\n", " ORDER BY os.name, os.version, cpu.model\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "a34a9328-127f-4ca0-8ee6-49472c7b8023", "metadata": {}, "source": [ "## QUESTION 5: For each operating systems company Apple and Microsoft, what is the overall average of its scores?" ] }, { "cell_type": "code", "execution_count": 9, "id": "5a003c64-84fd-432c-9ffc-0b2070917992", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
companyAVG(score)
0Microsoft4.014583
1Apple3.916667
\n", "
" ], "text/plain": [ " company AVG(score)\n", "0 Microsoft 4.014583\n", "1 Apple 3.916667" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT os.company, AVG(score)\n", " FROM os, rating\n", " WHERE os.company IN ('Apple', 'Microsoft')\n", " AND rating.os = os.skey\n", " GROUP BY os.company\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "24097282-6019-4d1a-8f2a-2ce43429f669", "metadata": {}, "source": [ "## QUESTION 6: In a SINGLE query, generate these averages:\n", "\n", "- ### For each CPU model, the overall average score of each operating system version.\n", "- ### For each CPU model, the overall average score of each operating system company.\n", "- ### The overall average score of each CPU model.\n", "- ### The overall average of all the scores.\n", "#### This is a straightforward rollup." ] }, { "cell_type": "code", "execution_count": 10, "id": "d048a1f2-bcd7-4e3c-85cc-f5abf2035e63", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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modelcompanynameversionAVG(score)
0i7MicrosoftWindows 1021H24.383333
1i7MicrosoftWindows 1022H23.666667
2i7MicrosoftWindows 10None4.025000
3i7MicrosoftWindows 1122H24.250000
4i7MicrosoftWindows 1123H24.000000
5i7MicrosoftWindows 11None4.125000
6i7MicrosoftNoneNone4.075000
7i7NoneNoneNone4.075000
8i9MicrosoftWindows 1021H23.550000
9i9MicrosoftWindows 1022H23.966667
10i9MicrosoftWindows 10None3.758333
11i9MicrosoftWindows 1122H24.300000
12i9MicrosoftWindows 1123H24.000000
13i9MicrosoftWindows 11None4.150000
14i9MicrosoftNoneNone3.954167
15i9NoneNoneNone3.954167
16M2ApplemacOS133.816667
17M2ApplemacOS143.850000
18M2ApplemacOSNone3.833333
19M2AppleOS X10.13.733333
20M2AppleOS X10.114.033333
21M2AppleOS XNone3.883333
22M2AppleNoneNone3.858333
23M2NoneNoneNone3.858333
24M3ApplemacOS133.583333
25M3ApplemacOS144.066667
26M3ApplemacOSNone3.825000
27M3AppleOS X10.13.883333
28M3AppleOS X10.114.366667
29M3AppleOS XNone4.125000
30M3AppleNoneNone3.975000
31M3NoneNoneNone3.975000
32NoneNoneNoneNone3.965625
\n", "
" ], "text/plain": [ " model company name version AVG(score)\n", "0 i7 Microsoft Windows 10 21H2 4.383333\n", "1 i7 Microsoft Windows 10 22H2 3.666667\n", "2 i7 Microsoft Windows 10 None 4.025000\n", "3 i7 Microsoft Windows 11 22H2 4.250000\n", "4 i7 Microsoft Windows 11 23H2 4.000000\n", "5 i7 Microsoft Windows 11 None 4.125000\n", "6 i7 Microsoft None None 4.075000\n", "7 i7 None None None 4.075000\n", "8 i9 Microsoft Windows 10 21H2 3.550000\n", "9 i9 Microsoft Windows 10 22H2 3.966667\n", "10 i9 Microsoft Windows 10 None 3.758333\n", "11 i9 Microsoft Windows 11 22H2 4.300000\n", "12 i9 Microsoft Windows 11 23H2 4.000000\n", "13 i9 Microsoft Windows 11 None 4.150000\n", "14 i9 Microsoft None None 3.954167\n", "15 i9 None None None 3.954167\n", "16 M2 Apple macOS 13 3.816667\n", "17 M2 Apple macOS 14 3.850000\n", "18 M2 Apple macOS None 3.833333\n", "19 M2 Apple OS X 10.1 3.733333\n", "20 M2 Apple OS X 10.11 4.033333\n", "21 M2 Apple OS X None 3.883333\n", "22 M2 Apple None None 3.858333\n", "23 M2 None None None 3.858333\n", "24 M3 Apple macOS 13 3.583333\n", "25 M3 Apple macOS 14 4.066667\n", "26 M3 Apple macOS None 3.825000\n", "27 M3 Apple OS X 10.1 3.883333\n", "28 M3 Apple OS X 10.11 4.366667\n", "29 M3 Apple OS X None 4.125000\n", "30 M3 Apple None None 3.975000\n", "31 M3 None None None 3.975000\n", "32 None None None None 3.965625" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sql = ( \"\"\"\n", " SELECT model, os.company, os.name, version, AVG(score)\n", " FROM cpu, os, benchmark, rating\n", " WHERE rating.cpu = cpu.skey\n", " AND rating.os = os.skey\n", " AND rating.benchmark = benchmark.skey\n", " GROUP BY model, os.company, os.name, version\n", " WITH ROLLUP\n", " \"\"\"\n", " )\n", "\n", "show_results(sql)" ] }, { "cell_type": "markdown", "id": "95620ce0-fb52-4143-8805-7a1a007c1cd8", "metadata": {}, "source": [ "## QUESTION 7: A book publisher wants to analyze its fiction book sales. The analysis will analyze and compare the sales of books in different genres, such as adventure, mystery, and romance. Within each genre, there can be subgenres. For example, mystery can include crime, murder, and espionage. The analysis will also compare sales from the publisher’s authors, whether they’re male, female, young, old, gay, straight, etc. Bookstores can also be analyzed, such as their sizes, locations, chain or independent, physical or online, etc. The publisher will want to look at how the sales of its current books compare with sales in the past.\n", "\n", "## What database you would design for this publisher? What tables would it have, and would they be related to each other? Briefly describe the columns of each table." ] }, { "cell_type": "markdown", "id": "527974ad-4d6e-413a-9ab7-0ae74c8f7852", "metadata": { "tags": [] }, "source": [ "#### The solution I have in mind is a dimensional model implemented by a star schema, such as the one below. Unfortunately, the lockdown browser didn't allow you to make screenshots of diagrams created in ERPPlus. So I can only grade your written descriptions of your solutions." ] }, { "attachments": { "712141c7-551d-412c-922e-92aada5a8c17.png": { "image/png": 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79etnvpixTrQhAiUQiERZpJzf448/7ngO71bcsGHDVvvBpfwfUc/FSaj5uoED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