Create solved_lab_python_sql.ipynb

solved_lab_python_sql.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "933a3adf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# 1. Establecer conexión (Ajusta el usuario y contraseña según tu configuración)\n",
+    "# Formato: 'mysql+pymysql://usuario:contraseña@host:puerto/sakila'\n",
+    "engine = create_engine('mysql+pymysql://root:password@localhost:3306/sakila')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a66db92b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Función rentals_month\n",
+    "def rentals_month(engine, month, year):\n",
+    "    \"\"\"\n",
+    "    Recupera datos de alquiler de la tabla 'rental' para un mes y año específicos.\n",
+    "    \"\"\"\n",
+    "    query = f\"\"\"\n",
+    "    SELECT * \n",
+    "    FROM rental\n",
+    "    WHERE MONTH(rental_date) = {month} AND YEAR(rental_date) = {year}\n",
+    "    \"\"\"\n",
+    "    df = pd.read_sql(query, engine)\n",
+    "    return df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a8d8dc41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Función rental_count_month\n",
+    "def rental_count_month(df, month, year):\n",
+    "    \"\"\"\n",
+    "    Cuenta los alquileres por cliente y renombra la columna según el mes y año.\n",
+    "    \"\"\"\n",
+    "    # Agrupamos por customer_id y contamos las filas\n",
+    "    df_count = df.groupby('customer_id')[['rental_id']].count()\n",
+    "    \n",
+    "    # Formateamos el nombre de la columna (ej: rentals_05_2005)\n",
+    "    col_name = f\"rentals_{month:02d}_{year}\"\n",
+    "    df_count.columns = [col_name]\n",
+    "    \n",
+    "    return df_count.reset_index()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e3809726",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4. Función compare_rentals\n",
+    "def compare_rentals(df1, df2):\n",
+    "    \"\"\"\n",
+    "    Combina dos DataFrames de conteo y calcula la diferencia de actividad.\n",
+    "    Utiliza un 'inner join' para identificar clientes activos en AMBOS meses.\n",
+    "    \"\"\"\n",
+    "    # Combinamos los DataFrames usando customer_id como clave\n",
+    "    df_combined = pd.merge(df1, df2, on='customer_id', how='inner')\n",
+    "    \n",
+    "    # Identificamos los nombres de las columnas de rentals dinámicamente\n",
+    "    cols = [col for col in df_combined.columns if col.startswith('rentals_')]\n",
+    "    \n",
+    "    # Calculamos la diferencia (Mes 2 - Mes 1)\n",
+    "    df_combined['difference'] = df_combined[cols[1]] - df_combined[cols[0]]\n",
+    "    \n",
+    "    return df_combined"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}