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Create lab-sql-python-connection.ipynb #354

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344 changes: 344 additions & 0 deletions lab-sql-python-connection.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "47ed2e29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pymysql in /opt/anaconda3/lib/python3.13/site-packages (1.1.2)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install pymysql"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c5060bba",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pymysql\n",
"from sqlalchemy import create_engine, text\n",
"import getpass\n",
"password=getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4582723a",
"metadata": {},
"outputs": [],
"source": [
"bd= \"sakila\"\n",
"connection_string= ''"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "494b90ff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Engine(mysql+pymysql://root:***@localhost/sakila)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bd = \"sakila\"\n",
"connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+bd\n",
"engine = create_engine(connection_string)\n",
"engine"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6b47e247",
"metadata": {},
"outputs": [],
"source": [
"def rentals_month(engine, month, year):\n",
" query = f\"\"\"\n",
" SELECT \n",
" rental_id,\n",
" rental_date,\n",
" inventory_id,\n",
" customer_id,\n",
" return_date,\n",
" staff_id\n",
" FROM rental\n",
" WHERE MONTH(rental_date) = {month}\n",
" AND YEAR(rental_date) = {year};\n",
" \"\"\"\n",
" df = pd.read_sql_query(query, engine)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4ad87897",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rental_id</th>\n",
" <th>rental_date</th>\n",
" <th>inventory_id</th>\n",
" <th>customer_id</th>\n",
" <th>return_date</th>\n",
" <th>staff_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2005-05-24 22:53:30</td>\n",
" <td>367</td>\n",
" <td>130</td>\n",
" <td>2005-05-26 22:04:30</td>\n",
" <td>1</td>\n",
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" <th>1</th>\n",
" <td>2</td>\n",
" <td>2005-05-24 22:54:33</td>\n",
" <td>1525</td>\n",
" <td>459</td>\n",
" <td>2005-05-28 19:40:33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2005-05-24 23:03:39</td>\n",
" <td>1711</td>\n",
" <td>408</td>\n",
" <td>2005-06-01 22:12:39</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2005-05-24 23:04:41</td>\n",
" <td>2452</td>\n",
" <td>333</td>\n",
" <td>2005-06-03 01:43:41</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2005-05-24 23:05:21</td>\n",
" <td>2079</td>\n",
" <td>222</td>\n",
" <td>2005-06-02 04:33:21</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rental_id rental_date inventory_id customer_id \\\n",
"0 1 2005-05-24 22:53:30 367 130 \n",
"1 2 2005-05-24 22:54:33 1525 459 \n",
"2 3 2005-05-24 23:03:39 1711 408 \n",
"3 4 2005-05-24 23:04:41 2452 333 \n",
"4 5 2005-05-24 23:05:21 2079 222 \n",
"\n",
" return_date staff_id \n",
"0 2005-05-26 22:04:30 1 \n",
"1 2005-05-28 19:40:33 1 \n",
"2 2005-06-01 22:12:39 1 \n",
"3 2005-06-03 01:43:41 2 \n",
"4 2005-06-02 04:33:21 1 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rentals = rentals_month(engine, 5, 2005)\n",
"df_rentals.head()\n"
]
},
{
"cell_type": "markdown",
"id": "c5a4eeef",
"metadata": {},
"source": [
"Develop a Python function called rental_count_month that takes the DataFrame provided by rentals_month as input along with the month and year and returns a new DataFrame containing the number of rentals made by each customer_id during the selected month and year."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9d6101c4",
"metadata": {},
"outputs": [],
"source": [
"def rental_count_month(df, month, year):\n",
" result = (\n",
" df.groupby(\"customer_id\")\n",
" .agg(rental_count=(\"rental_id\", \"count\"))\n",
" .reset_index()\n",
" )\n",
" result[\"month\"] = month\n",
" result[\"year\"] = year\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3702dc53",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" customer_id rental_count month year\n",
"0 1 2 5 2005\n",
"1 2 1 5 2005\n",
"2 3 2 5 2005\n",
"3 5 3 5 2005\n",
"4 6 3 5 2005\n"
]
}
],
"source": [
"df_rentals = rentals_month(engine, 5, 2005)\n",
"df_counts = rental_count_month(df_rentals, 5, 2005)\n",
"print(df_counts.head())\n"
]
},
{
"cell_type": "markdown",
"id": "ff3dd271",
"metadata": {},
"source": [
"Create a Python function called compare_rentals that takes two DataFrames as input containing the number of rentals made by each customer in different months and years. The function should return a combined DataFrame with a new 'difference' column, which is the difference between the number of rentals in the two months."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a5a57a25",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def compare_rentals(df1, df2):\n",
" merged = pd.merge(df1, df2, on=\"customer_id\", suffixes=(\"_1\", \"_2\"), how=\"outer\").fillna(0)\n",
" merged[\"difference\"] = merged[\"rental_count_2\"] - merged[\"rental_count_1\"]\n",
" return merged"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "47fccace",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" customer_id rental_count_1 month_1 year_1 rental_count_2 month_2 \\\n",
"0 1 2.0 5.0 2005.0 7.0 6.0 \n",
"1 2 1.0 5.0 2005.0 1.0 6.0 \n",
"2 3 2.0 5.0 2005.0 4.0 6.0 \n",
"3 4 0.0 0.0 0.0 6.0 6.0 \n",
"4 5 3.0 5.0 2005.0 5.0 6.0 \n",
"\n",
" year_2 difference \n",
"0 2005.0 5.0 \n",
"1 2005.0 0.0 \n",
"2 2005.0 2.0 \n",
"3 2005.0 6.0 \n",
"4 2005.0 2.0 \n"
]
}
],
"source": [
"df_may = rental_count_month(rentals_month(engine, 5, 2005), 5, 2005)\n",
"df_june = rental_count_month(rentals_month(engine, 6, 2005), 6, 2005)\n",
"\n",
"comparison = compare_rentals(df_may, df_june)\n",
"print(comparison.head())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c2a4eef",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}