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209 changes: 209 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": 4,
"id": "44a1386b-2c39-439e-b750-f465308f3592",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: sqlalchemy in c:\\users\\punk_\\anaconda3\\lib\\site-packages (2.0.43)\n",
"Requirement already satisfied: greenlet>=1 in c:\\users\\punk_\\anaconda3\\lib\\site-packages (from sqlalchemy) (3.2.4)\n",
"Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\punk_\\anaconda3\\lib\\site-packages (from sqlalchemy) (4.15.0)\n"
]
}
],
"source": [
"!pip install sqlalchemy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "23d3cee8-248d-4fad-9463-35e55b08e138",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting pymysql\n",
" Downloading pymysql-1.1.2-py3-none-any.whl.metadata (4.3 kB)\n",
"Downloading pymysql-1.1.2-py3-none-any.whl (45 kB)\n",
"Installing collected packages: pymysql\n",
"Successfully installed pymysql-1.1.2\n"
]
}
],
"source": [
"!pip install pymysql"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2409c6a1-5739-43c6-b4a6-863d9ccbcc88",
"metadata": {},
"outputs": [],
"source": [
"!pip install pymysql sqlalchemy pandas"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "70bdb1b5-b6bd-4489-9d9d-cc2ea215c8b8",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
" ········\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"conection : sakila!\n"
]
}
],
"source": [
"# 1. Establish a connection between Python and the Sakila database.\n",
"import pandas as pd\n",
"import numpy as np\n",
"import pymysql\n",
"from sqlalchemy import create_engine\n",
"import getpass\n",
"\n",
"password = getpass.getpass() #ask for the password\n",
"bd = \"sakila\" #data base\n",
"\n",
"connection_string = 'mysql+pymysql://root:' + password + '@localhost/' + bd\n",
"engine = create_engine(connection_string)\n",
"\n",
"try:\n",
" with engine.connect() as connection:\n",
" print(f\"conection : {bd}!\")\n",
"except Exception as e:\n",
" print(f\"Error : {e}\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "db4db652-2b60-4967-92de-7d9b00b0fcb9",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 8\u001b[39m df = pd.read_sql(query, engine)\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[43mdf\u001b[49m\n",
"\u001b[31mNameError\u001b[39m: name 'df' is not defined"
]
}
],
"source": [
"#2. Write a Python function called rentals_month \n",
"def rentals_month(engine, month, year):\n",
" query = f\"\"\"\n",
" SELECT * FROM rental \n",
" WHERE MONTH(rental_date) = {month} \n",
" AND YEAR(rental_date) = {year};\n",
" \"\"\"\n",
" df = pd.read_sql(query, engine)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "056ef1a6-a6df-49ea-94f2-5a1041d6b519",
"metadata": {},
"outputs": [],
"source": [
"#3. Develop a Python function called rental_count_month\n",
"def rental_count_month(df, month, year):\n",
" count_df = df.groupby('customer_id')[['rental_id']].count()\n",
" \n",
" column_name = f\"rentals_{month:02d}_{year}\"\n",
" count_df.columns = [column_name]\n",
" \n",
" return count_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f469b4c-3318-4738-9e14-2f7575ebbad5",
"metadata": {},
"outputs": [],
"source": [
"#4. Create a Python function called compare_rentals \n",
"def compare_rentals(df1, df2):\n",
" combined_df = pd.merge(df1, df2, on='customer_id', how='inner')\n",
" combined_df['difference'] = combined_df.iloc[:, 2] - combined_df.iloc[:, 1]\n",
" \n",
" return combined_df"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "28bdb8c4-e2d0-4a2c-a965-3c0fa66addc4",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rental_count_month' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m df_june = rentals_month(engine, \u001b[32m6\u001b[39m, \u001b[32m2005\u001b[39m)\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# Transformar a conteos\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m may_counts = \u001b[43mrental_count_month\u001b[49m(df_may, \u001b[32m5\u001b[39m, \u001b[32m2005\u001b[39m)\n\u001b[32m 7\u001b[39m june_counts = rental_count_month(df_june, \u001b[32m6\u001b[39m, \u001b[32m2005\u001b[39m)\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m# Comparar y mostrar reporte\u001b[39;00m\n",
"\u001b[31mNameError\u001b[39m: name 'rental_count_month' is not defined"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "74651bad-d6c3-41b0-99ff-9d485df60f54",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python [conda env:base] *",
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"name": "conda-base-py"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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