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San Jose State University
Department of Applied Data Science

**DATA 225
Database Systems for Analytics**

Spring 2023
Instructor: Ron Mak

**Checkpoint Exam #2**
October 30

Maximum 100 points maximum
You have 100 minutes

***INDIVIDUAL WORK ONLY!***
The Academic Integrity Policy will be strictly enforced.

SOLUTIONS
" ] }, { "attachments": { "0e880a5b-2ad3-4120-900c-a5c6591797e9.png": { "image/png": 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yyCPmWCOx/cmTJ/udd8GCBaVKlSpy4403yo4dOyKxC7aBAAIxJhDHo+Nj7IpxuHlKYPHi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\nQAABBBDIUsBTDcnAgQOld+/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pu8eF73fpvu+jOPOO0wQvAhHvfplllknfzrwm+BQLBFn5pR/IGzNpvrJrv7BW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\nmrWla4SVU045JbMOZFxUx5YKzniY/T/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} }, "cell_type": "markdown", "id": "15d42322-0a09-451f-b7bc-5d846620ced7", "metadata": {}, "source": [ "# PROBLEM 1\n", "#### [10 points] It is possible to download data from a database and use Python to generate a horizontal bar chart. For example, here is a chart of the percentages of Titanic passengers in each passenger class:\n", "![barchart-Python.png](attachment:0e880a5b-2ad3-4120-900c-a5c6591797e9.png)\n", "#### But downloading may not be an option for a large dataset. Instead, you can use SQL to generate a crude horizontal bar chart on the server side and only download the chart. The downloaded data might look like this as Python tuples:\n", "![barchart-SQL-1.png](attachment:d936f69b-c7a8-4b1d-a5c3-10fcc6680808.png)" ] }, { "cell_type": "code", "execution_count": 1, "id": "6b75e109-5d68-4562-a105-b2ffde70bafe", "metadata": {}, "outputs": [], "source": [ "from pandas import DataFrame\n", "from DATA225utils import make_connection, dataframe_query" ] }, { "cell_type": "code", "execution_count": 2, "id": "ecd4bf8a-b0b7-4c18-a544-e1bb6a29b1de", "metadata": {}, "outputs": [], "source": [ "conn = make_connection(config_file = 'titanic.ini')\n", "cursor = conn.cursor()" ] }, { "cell_type": "code", "execution_count": 3, "id": "d40e008b-4749-4948-938e-56b10a408408", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('1st', 323, Decimal('24.6753'), '*************************')\n", "('2nd', 277, Decimal('21.1612'), '*********************')\n", "('3rd', 709, Decimal('54.1635'), '******************************************************')\n" ] } ], "source": [ "cursor.execute('SET @total = (SELECT COUNT(*) FROM passengers)')\n", "\n", "sql = ( \"\"\"\n", " SELECT @class := class, COUNT(*), \n", " @pct := (SELECT 100*COUNT(*)/@total\n", " FROM passengers\n", " WHERE class = @class),\n", " REPEAT('*', @pct)\n", " FROM passengers\n", " GROUP BY class\n", " ORDER BY class\n", " \"\"\"\n", " )\n", "\n", "cursor.execute(sql)\n", "rows = cursor.fetchall()\n", "\n", "for row in rows:\n", " print(row)" ] }, { "cell_type": "code", "execution_count": 4, "id": "c81642a8-de9c-4b4c-831f-76a2766b0ea0", "metadata": {}, "outputs": [], "source": [ "cursor.close()\n", "conn.close()" ] }, { "attachments": { "2b7002ed-9bd5-4b36-93ef-ee4a67e10f55.png": { "image/png": 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BgoGCgYKBKcNAIexTNqClOwUDBQMFAzcqKCgYGAUG/v3vf4fPf/7z4e53v3u4733vO4oi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\nl1lXRASDPHicwCILlwXRgfN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} }, "cell_type": "markdown", "id": "0799b661-b6e8-4d8e-b92e-d4cc339952e8", "metadata": {}, "source": [ "# PROBLEM 2\n", "#### [30 points] The example SQL code given in class to perform linear regression with one independent variable used local `@` variables. However, the regression examples with two and three independent variables did not use local variables. Instead, they used views to perform the intermediate calculations.\n", "#### The formula to calculate the Pearson correlation coefficient *r* is\n", "![correlation-formula.png](attachment:2b7002ed-9bd5-4b36-93ef-ee4a67e10f55.png)\n", "#### Write SQL code to access the **sizes** tables of the **babies** database and compute the Pearson correlation coefficient between the babies' weights (in kg) and their lengths (in cm). Use the formula above. It doesn't matter which is *x* and which is *y*. **Do not use local variables but use views instead as necessary.** Query and display the value of the calculated coefficient." ] }, { "cell_type": "code", "execution_count": 5, "id": "044a45b8-b800-477f-830c-71d04197f3eb", "metadata": {}, "outputs": [], "source": [ "conn = make_connection(config_file = 'babies.ini')\n", "cursor = conn.cursor()" ] }, { "attachments": { "ac88cc76-e835-454a-ae14-8f1d63c72ca8.png": { "image/png": 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BgoGCgYKBKcNAIexTNqClOwUDBQMFAzcqKCgYGAUG/v3vf4fPf/7z4e53v3u4733vO4oi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\nl1lXRASDPHicwCILlwXRgfN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} }, "cell_type": "markdown", "id": "aead9cec-9d32-4858-9549-f292c7fd7b42", "metadata": {}, "source": [ "![correlation-formula-1.png](attachment:ac88cc76-e835-454a-ae14-8f1d63c72ca8.png)" ] }, { "cell_type": "code", "execution_count": 6, "id": "5e4fab8d-8b2a-472a-9a72-1acbedacc3b0", "metadata": {}, "outputs": [], "source": [ "cursor.execute('DROP VIEW IF EXISTS base1')\n", "\n", "sql = ( \"\"\"\n", " CREATE VIEW base1 AS\n", " SELECT\n", " COUNT(*) AS n,\n", " SUM(weight_kg) AS sum_x,\n", " SUM(length_cm) AS sum_y,\n", " SUM(weight_kg*weight_kg) AS sum_xx,\n", " SUM(length_cm*length_cm) AS sum_yy,\n", " SUM(weight_kg*length_cm) AS sum_xy\n", " FROM sizes;\n", " \"\"\"\n", " )\n", "\n", "cursor.execute(sql);" ] }, { "cell_type": "code", "execution_count": 7, "id": "db151e1d-bcd9-458d-9236-babfae14e5c4", "metadata": {}, "outputs": [], "source": [ "cursor.execute('DROP VIEW IF EXISTS base2')\n", "\n", "sql = ( \"\"\"\n", " CREATE VIEW base2 AS\n", " SELECT\n", " n*sum_xx - sum_x*sum_x AS factor1,\n", " n*sum_yy - sum_y*sum_y AS factor2\n", " FROM base1; \n", " \"\"\"\n", " )\n", "\n", "cursor.execute(sql)" ] }, { "cell_type": "code", "execution_count": 8, "id": "710e8541-fd8c-4401-9d37-80f8a7c57647", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pearson
00.470177
\n", "
" ], "text/plain": [ " pearson\n", "0 0.470177" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sql = ( \"\"\"\n", " SELECT (n*sum_xy - sum_x*sum_y)\n", " / SQRT(factor1*factor2) AS pearson\n", " FROM base1, base2 \n", " \"\"\"\n", " )\n", "\n", "_, df = dataframe_query(conn, sql)\n", "df" ] }, { "attachments": { "a1f120ff-dfcf-4460-bd41-11094a094c2f.png": { "image/png": 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PUnzyLHJ3HdkF57Qs9gb+ps34Et4VbxpP+7B9+/b+7R5/PRriDSLCfffdt9tvv/2qY1JG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\nbxr3ig0xC88jFuuz93Fubq5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rZSzUZaDesmVLyblvhEO7sgYZ5SNrukP+BgchEnF/g7O8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\nMd66desv9VMzPmtA+gdDrA2RvfI5EspB50p4v0j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} }, "cell_type": "markdown", "id": "43eac527-8879-46f1-9de5-61e38384206e", "metadata": {}, "source": [ "![correlation-formula-2.png](attachment:a1f120ff-dfcf-4460-bd41-11094a094c2f.png)" ] }, { "cell_type": "code", "execution_count": 9, "id": "e72e8378-15c6-4928-81f7-3eeed8c97a77", "metadata": {}, "outputs": [], "source": [ "cursor.execute('DROP VIEW IF EXISTS base3')\n", "\n", "sql = ( \"\"\"\n", " CREATE VIEW base3 AS\n", " SELECT AVG(weight_kg) AS mean_x,\n", " AVG(length_cm) AS mean_y\n", " FROM sizes\n", " \"\"\"\n", " )\n", " \n", "cursor.execute(sql)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c06be94c-0f69-48b1-bc6b-021ba1c7f3a6", "metadata": {}, "outputs": [], "source": [ "cursor.execute('DROP VIEW IF EXISTS base4')\n", "\n", "sql = ( \"\"\"\n", " CREATE VIEW base4 AS \n", " SELECT (weight_kg - mean_x) AS factor1,\n", " (length_cm - mean_y) AS factor2\n", " FROM sizes, base3\n", " \"\"\"\n", " )\n", " \n", "cursor.execute(sql)" ] }, { "cell_type": "code", "execution_count": 11, "id": "da63743b-7f00-4d34-9cd1-b401ab580912", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pearson
00.470177
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" ], "text/plain": [ " pearson\n", "0 0.470177" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sql = ( \"\"\"\n", " SELECT SUM(factor1*factor2)\n", " / SQRT(SUM(factor1*factor1)*SUM(factor2*factor2)) AS pearson\n", " FROM base4\n", " \"\"\"\n", " )\n", " \n", "_, df = dataframe_query(conn, sql)\n", "df" ] }, { "cell_type": "code", "execution_count": 12, "id": "952f4020-77fb-42ac-b499-3898d4c31459", "metadata": {}, "outputs": [], "source": [ "cursor.close()\n", "conn.close()" ] }, { "cell_type": "markdown", "id": "d0bac922-b067-44b4-82ed-46334c7e6a3e", "metadata": {}, "source": [ "# PROBLEM 3\n", "#### [30 points] The MySQL `RAND()` function returns a random value between 0 and 1 each time it is called. For example, to assign a random 6-digit passcode to each student, the SQL code:\n", "```\n", "SELECT 100000 + FLOOR(100000*RAND()) AS passcode, \n", " CONCAT(first, ' ', last) AS name\n", "FROM school.student\n", "ORDER BY passcode\n", "```\n", "#### can produce the result\n", "```\n", " 158237 Mary Jane\n", " 164790 Leslie Klein\n", " 188571 Tim Novak\n", " 192688 John Doe\n", " 196817 Kim Smith\n", "```\n", "#### which will be different each time we run the code.\n", "#### Suppose we want to download a small random sample from a very large dataset in a database. We can use `RAND()` to select *n* random records.\n", "#### Write a **stored procedure** named `random_passengers` that has a single parameter `n`. Each time it is called, it should access the **passenger** table of the **titanic** database to query and return *n* randomly selected rows.\n", "#### Test your procedure from Python by calling it *twice* with argument 5 and then *twice* with argument 10 and disply the results to verify that you get different rows each time." ] }, { "cell_type": "code", "execution_count": 13, "id": "300a1657-39ed-46c6-a43c-4cfcf5377e2e", "metadata": {}, "outputs": [], "source": [ "conn = make_connection(config_file = 'titanic.ini')\n", "cursor = conn.cursor()" ] }, { "cell_type": "code", "execution_count": 14, "id": "1cd6fb38-2546-46d0-9eb1-96178b49f1f2", "metadata": { "tags": [] }, "outputs": [], "source": [ "cursor.execute('DROP PROCEDURE IF EXISTS random_passengers')\n", "\n", "cursor.execute(\n", " \"\"\"\n", " CREATE PROCEDURE random_passengers(IN count INT)\n", " BEGIN\n", " SELECT *\n", " FROM titanic.passengers\n", " ORDER BY RAND()\n", " LIMIT count;\n", " END\n", " \"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "id": "2011c9ee-35f5-4786-b13d-d61252f145e5", "metadata": {}, "outputs": [], "source": [ "def fetch_random_passengers(count):\n", " generator = cursor.execute(f'CALL random_passengers({count})', multi=True)\n", "\n", " for result in generator:\n", " columns = result.description\n", "\n", " if columns != None:\n", " column_names = [column_info[0] \n", " for column_info in columns]\n", "\n", " rows = result.fetchall()\n", "\n", " df = DataFrame(rows)\n", " df.columns = column_names\n", "\n", " display(df)" ] }, { "cell_type": "code", "execution_count": 16, "id": "e364e6dd-b49c-4ead-8337-06d5c1942c65", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namesurvivedsexageclass
0Jalsevac, Mr. Ivanyesmale29.03rd
1Krekorian, Mr. Neshanyesmale25.03rd
2Celotti, Mr. Francesconomale24.03rd
3Stahelin-Maeglin, Dr. Maxyesmale32.01st
4Chapman, Mr. John Henrynomale37.02nd
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" ], "text/plain": [ " name survived sex age class\n", "0 Jalsevac, Mr. Ivan yes male 29.0 3rd\n", "1 Krekorian, Mr. Neshan yes male 25.0 3rd\n", "2 Celotti, Mr. Francesco no male 24.0 3rd\n", "3 Stahelin-Maeglin, Dr. Max yes male 32.0 1st\n", "4 Chapman, Mr. John Henry no male 37.0 2nd" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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namesurvivedsexageclass
0McCarthy, Mr. Timothy Jnomale54.01st
1Saad, Mr. Aminnomale0.03rd
2Andrew, Mr. Edgardo Samuelnomale18.02nd
3Drew, Master. Marshall Brinesyesmale8.02nd
4Harknett, Miss. Alice Phoebenofemale0.03rd
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" ], "text/plain": [ " name survived sex age class\n", "0 McCarthy, Mr. Timothy J no male 54.0 1st\n", "1 Saad, Mr. Amin no male 0.0 3rd\n", "2 Andrew, Mr. Edgardo Samuel no male 18.0 2nd\n", "3 Drew, Master. Marshall Brines yes male 8.0 2nd\n", "4 Harknett, Miss. Alice Phoebe no female 0.0 3rd" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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namesurvivedsexageclass
0Quick, Mrs. Frederick Charles (yesfemale33.02nd
1Cor, Mr. Liudevitnomale19.03rd
2Somerton, Mr. Francis Williamnomale30.03rd
3Deacon, Mr. Percy Williamnomale17.02nd
4Robins, Mrs. Alexander A (Gracenofemale47.03rd
5Doyle, Miss. Elizabethnofemale24.03rd
6Stoytcheff, Mr. Ilianomale19.03rd
7Ivanoff, Mr. Kanionomale0.03rd
8Touma, Mrs. Darwis (Hanne Youssyesfemale29.03rd
9Angheloff, Mr. Minkonomale26.03rd
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" ], "text/plain": [ " name survived sex age class\n", "0 Quick, Mrs. Frederick Charles ( yes female 33.0 2nd\n", "1 Cor, Mr. Liudevit no male 19.0 3rd\n", "2 Somerton, Mr. Francis William no male 30.0 3rd\n", "3 Deacon, Mr. Percy William no male 17.0 2nd\n", "4 Robins, Mrs. Alexander A (Grace no female 47.0 3rd\n", "5 Doyle, Miss. Elizabeth no female 24.0 3rd\n", "6 Stoytcheff, Mr. Ilia no male 19.0 3rd\n", "7 Ivanoff, Mr. Kanio no male 0.0 3rd\n", "8 Touma, Mrs. Darwis (Hanne Youss yes female 29.0 3rd\n", "9 Angheloff, Mr. Minko no male 26.0 3rd" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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namesurvivedsexageclass
0Crease, Mr. Ernest Jamesnomale19.03rd
1Svensson, Mr. Olofnomale24.03rd
2Zakarian, Mr. Mapriededernomale26.53rd
3Davidson, Mrs. Thornton (Orianyesfemale27.01st
4West, Miss. Constance Miriumyesfemale5.02nd
5Sloper, Mr. William Thompsonyesmale28.01st
6Vander Planke, Mr. Juliusnomale31.03rd
7Ovies y Rodriguez, Mr. Servandonomale28.51st
8Adams, Mr. Johnnomale26.03rd
9Baclini, Mrs. Solomon (Latifa Qyesfemale24.03rd
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" ], "text/plain": [ " name survived sex age class\n", "0 Crease, Mr. Ernest James no male 19.0 3rd\n", "1 Svensson, Mr. Olof no male 24.0 3rd\n", "2 Zakarian, Mr. Mapriededer no male 26.5 3rd\n", "3 Davidson, Mrs. Thornton (Orian yes female 27.0 1st\n", "4 West, Miss. Constance Mirium yes female 5.0 2nd\n", "5 Sloper, Mr. William Thompson yes male 28.0 1st\n", "6 Vander Planke, Mr. Julius no male 31.0 3rd\n", "7 Ovies y Rodriguez, Mr. Servando no male 28.5 1st\n", "8 Adams, Mr. John no male 26.0 3rd\n", "9 Baclini, Mrs. Solomon (Latifa Q yes female 24.0 3rd" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fetch_random_passengers(5)\n", "fetch_random_passengers(5)\n", "fetch_random_passengers(10)\n", "fetch_random_passengers(10)" ] }, { "cell_type": "code", "execution_count": 17, "id": "d702588d-fe4e-4117-9f27-18a3d4277cdf", "metadata": {}, "outputs": [], "source": [ "cursor.close()\n", "conn.close()" ] }, { "cell_type": "code", "execution_count": 18, "id": "086896e6-3cd6-4d7c-a996-89a7f144204e", "metadata": {}, "outputs": [], "source": [ "conn = make_connection(config_file = 'titanic.ini')\n", "cursor = conn.cursor()" ] }, { "attachments": { "25aa0bd1-9ffd-41f3-b2eb-c968206b6b11.png": { "image/png": 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fz79+/fX55/SFWHKROX7a/D/L/Mt2Ev6sdP4NKIvZUvzMMv87rjNLCyi6hBcDNRgDU+uH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\n4+GjvWFgq6C0M47EwzcevrXFH9voS3wAO+PM8fOZEh2yEFl8xj8f4bW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} }, "cell_type": "markdown", "id": "0c239362-06bd-499a-b3a3-8b4b04c8c8e3", "metadata": {}, "source": [ "# PROBLEM 4\n", "#### [30 points] Complete a small GUI-based Python application that accesses the **passenger** table of the **titanic** database to count and display the number of passengers in each passenger class.\n", "#### Go to the Canvas ***Files*** page to download `titanic_window.ui` which will generate the application window\n", "![TitanicApp.png](attachment:25aa0bd1-9ffd-41f3-b2eb-c968206b6b11.png)\n", "#### Write the Python code `TitanicWindow.py` containing class `TitanicWindow` that will load `titanic_window.ui` and populate the menu (a QComboBox component) named `class_menu` with menu items `1st`, `2nd`, and `3rd`. Whenever the user chooses a class by selecting a menu item, the application should query the **passenger** table for the count of passengers in the chosen class, and then display the count in the text box (a QLabel component) named `the_count`. The text box should initially display the count in first class, as shown above.\n", "#### The cells below will create and start `TitanicWindow`. Remember that after the first run of the application, you may need to restart the Python kernel to run it again (from the Jupyter **Run** menu menu, select **Restart Kernel and Run All Cells ...**)." ] }, { "cell_type": "code", "execution_count": 19, "id": "b03e7149-9607-4f24-ba98-4e5f08d0d3aa", "metadata": {}, "outputs": [], "source": [ "import sys\n", "from PyQt5.QtWidgets import QApplication\n", "from TitanicWindow import TitanicWindow" ] }, { "cell_type": "code", "execution_count": 20, "id": "3c63d08d-8469-4401-83d5-9679053e7e71", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "app = QApplication(sys.argv)\n", "form = TitanicWindow()\n", "app.exec_()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }