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Solved lab #424

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343 changes: 343 additions & 0 deletions Connecting Python to SQL.ipynb
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Original file line number Diff line number Diff line change
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"start_time": "2026-04-28T17:57:48.621050Z"
}
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"source": [
"import pandas as pd\n",
"from getpass import getpass\n",
"from urllib.parse import quote_plus\n",
"from sqlalchemy import create_engine\n",
"from sqlalchemy import text\n",
"import sys\n",
"\n",
"!{sys.executable} -m pip install cryptography\n",
"!{sys.executable} -m pip install --upgrade pip"
],
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: cryptography in ./.venv/lib/python3.14/site-packages (47.0.0)\r\n",
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"Requirement already satisfied: pycparser in ./.venv/lib/python3.14/site-packages (from cffi>=2.0.0->cryptography) (3.0)\r\n",
"Requirement already satisfied: pip in ./.venv/lib/python3.14/site-packages (26.1)\r\n"
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"execution_count": 6
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"end_time": "2026-04-28T17:13:13.280137Z",
"start_time": "2026-04-28T17:13:06.668853Z"
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"cell_type": "code",
"source": [
"#Create access to MySQL account and to sakila database.\n",
"password = quote_plus(getpass(\"Enter MySQL password: \"))\n",
"engine = create_engine(f\"mysql+pymysql://root:{password}@localhost:3306/sakila\")"
],
"id": "733d9835745ff29b",
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
" n\n",
"0 599\n"
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"start_time": "2026-04-28T17:57:59.050536Z"
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},
"cell_type": "code",
"source": [
"#Function that retrieves rental data for a given month and year from the Sakila database as a Pandas DataFrame.\n",
"def rentals_month(engine, month, year):\n",
" query = text(\"\"\"SELECT * FROM rental WHERE :month = MONTH(rental_date) AND :year = YEAR(rental_date)\"\"\")\n",
" df = pd.read_sql(query, engine, params={\"month\": month, \"year\": year})\n",
" return df"
],
"id": "3f7898fde952f22f",
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"execution_count": 7
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{
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"end_time": "2026-04-28T19:55:49.643255Z",
"start_time": "2026-04-28T19:55:49.513251Z"
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},
"cell_type": "code",
"source": [
"may_2005 = rentals_month(engine, 5, 2005)\n",
"june_2005 = rentals_month(engine, 6, 2005)\n"
],
"id": "256e140c79d1bf7c",
"outputs": [],
"execution_count": 28
},
{
"metadata": {
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"end_time": "2026-04-28T18:57:58.993220Z",
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"cell_type": "code",
"source": [
"#Function that returns a new DataFrame containing the number of rentals made by each customer_id during the selected month and year.\n",
"\n",
"def rental_count_month(df, month, year):\n",
" df = df.groupby(\"customer_id\").size().reset_index()\n",
" df = df.rename(columns = {0: f\"rentals_{month:02d}_{year}\"})\n",
" return df"
],
"id": "e3985fae2117c236",
"outputs": [],
"execution_count": 17
},
{
"metadata": {
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"end_time": "2026-04-28T19:56:03.204149Z",
"start_time": "2026-04-28T19:56:03.180948Z"
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},
"cell_type": "code",
"source": [
"df_may_rental = rental_count_month(may_2005, 5, 2005)\n",
"df_june_rental = rental_count_month(june_2005, 6, 2005)\n"
],
"id": "bf3e6d2c1f8785b9",
"outputs": [],
"execution_count": 29
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-04-28T20:11:30.590048Z",
"start_time": "2026-04-28T20:11:30.526548Z"
}
},
"cell_type": "code",
"source": [
"#Function that returns a combined DataFrame with the difference between the number of rentals in the two months.\n",
"\n",
"def compare_rentals(df_earlier_month, df_later_month):\n",
" df_3 = pd.merge(df_earlier_month, df_later_month, on=\"customer_id\", how=\"outer\")\n",
" df_3[\"difference\"] = df_3[df_3.columns[2]] - df_3[df_3.columns[1]]\n",
" return df_3"
],
"id": "da7164088f6d846a",
"outputs": [],
"execution_count": 42
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-04-28T20:11:31.896391Z",
"start_time": "2026-04-28T20:11:31.872021Z"
}
},
"cell_type": "code",
"source": "df_may_june = compare_rentals(df_may_rental, df_june_rental)",
"id": "e8be0fa7a4445929",
"outputs": [],
"execution_count": 43
},
{
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},
"cell_type": "code",
"source": "df_may_june",
"id": "cb8303b7d03694",
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{
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" customer_id rentals_05_2005 rentals_06_2005 difference\n",
"0 1 2.0 7.0 5.0\n",
"1 2 1.0 1.0 0.0\n",
"2 3 2.0 4.0 2.0\n",
"3 4 NaN 6.0 NaN\n",
"4 5 3.0 5.0 2.0\n",
".. ... ... ... ...\n",
"593 595 1.0 2.0 1.0\n",
"594 596 6.0 2.0 -4.0\n",
"595 597 2.0 3.0 1.0\n",
"596 598 NaN 1.0 NaN\n",
"597 599 1.0 4.0 3.0\n",
"\n",
"[598 rows x 4 columns]"
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