solved

connection.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff965a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Establish a connection between Python and the Sakila database.\n",
+    "\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Rellena con tus datos reales\n",
+    "USER = \"root\"\n",
+    "PASSWORD = \"xxxx\"   # cambiar por contraseña de MySQL\n",
+    "HOST = \"localhost\"      # o la IP/host donde esté MySQL\n",
+    "PORT = \"3306\"           # puerto por defecto de MySQL\n",
+    "DB = \"sakila\"\n",
+    "\n",
+    "# Cadena de conexión usando el driver pymysql\n",
+    "connection_string = f\"mysql+pymysql://{USER}:{PASSWORD}@{HOST}:{PORT}/{DB}\"\n",
+    "\n",
+    "engine = create_engine(connection_string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3d06cf2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Write a Python function called rentals_month that retrieves rental data for a \n",
+    "# given month and year (passed as parameters) from the Sakila database as a Pandas DataFrame.\n",
+    "#  The function should take in three parameters\n",
+    "\n",
+    "def rentals_month(engine, month, year):\n",
+    "    \"\"\"\n",
+    "    Recupera todos los alquileres del mes y año especificados.\n",
+    "    \"\"\"\n",
+    "    query = f\"\"\"\n",
+    "        SELECT *\n",
+    "        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\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "501f11dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Develop a Python function called rental_count_month that takes the DataFrame provided \n",
+    "# by rentals_month as input along with the month and year and returns a new DataFrame containing \n",
+    "# the number of rentals made by each customer_id during the selected month and year.\n",
+    "\n",
+    "\n",
+    "def rental_count_month(df, month, year):\n",
+    "    \"\"\"\n",
+    "    Devuelve un DataFrame con el número total de alquileres por cliente.\n",
+    "    \"\"\"\n",
+    "    col_name = f\"alquileres_{month:02d}_{year}\"\n",
+    "    df_counts = (\n",
+    "        df.groupby(\"customer_id\")\n",
+    "          .size()\n",
+    "          .reset_index(name=col_name)\n",
+    "    )\n",
+    "    return df_counts\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "60f23c52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# 4. Create a Python function called compare_rentals that takes two DataFrames as input containing \n",
+    "# the number of rentals made by each customer in different months and years. \n",
+    "# The function should return a combined DataFrame with a new 'difference' column, which is \n",
+    "# the difference between the number of rentals in the two months.\n",
+    "\n",
+    "def compare_rentals(df1, df2):\n",
+    "    \"\"\"\n",
+    "    Combina dos DataFrames de conteo de alquileres por cliente\n",
+    "    y calcula la diferencia entre ambos meses.\n",
+    "    \"\"\"\n",
+    "    df_merged = pd.merge(df1, df2, on=\"customer_id\", how=\"outer\").fillna(0)\n",
+    "    col1 = df1.columns[1]\n",
+    "    col2 = df2.columns[1]\n",
+    "    df_merged[\"diferencia\"] = df_merged[col2] - df_merged[col1]\n",
+    "    return df_merged\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9cf34905",
+   "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>customer_id</th>\n",
+       "      <th>alquileres_05_2005</th>\n",
+       "      <th>alquileres_06_2005</th>\n",
+       "      <th>diferencia</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  alquileres_05_2005  alquileres_06_2005  diferencia\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                 0.0                 6.0         6.0\n",
+       "4            5                 3.0                 5.0         2.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#check\n",
+    "\n",
+    "df_may = rentals_month(engine, 5, 2005)\n",
+    "df_jun = rentals_month(engine, 6, 2005)\n",
+    "count_may = rental_count_month(df_may, 5, 2005)\n",
+    "count_jun = rental_count_month(df_jun, 6, 2005)\n",
+    "comparison = compare_rentals(count_may, count_jun)\n",
+    "comparison.head()\n"
+   ]
+  }
+ ],
+ "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": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}