diff --git a/lab_python_sql.ipynb b/lab_python_sql.ipynb
new file mode 100644
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--- /dev/null
+++ b/lab_python_sql.ipynb
@@ -0,0 +1,907 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dced7a96",
+   "metadata": {},
+   "source": [
+    "Establecer una conexión entre Python y la base de datos Sakila."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb289843",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conexión correcta.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sqlalchemy import create_engine\n",
+    "from getpass import getpass   # para no revelar contraseña\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Datos de conexión\n",
+    "user = \"root\"\n",
+    "password = getpass(\"Introduce tu contraseña de MySQL: \")   # para no revelar contraseña\n",
+    "host = \"localhost\"\n",
+    "database = \"sakila\"\n",
+    "\n",
+    "# Crear el engine\n",
+    "engine = create_engine(f\"mysql+pymysql://{user}:{password}@{host}/{database}\")\n",
+    "\n",
+    "# Probar la conexión leyendo una tabla\n",
+    "try:\n",
+    "    df_total = pd.read_sql(\"SELECT * FROM rental LIMIT 10;\", engine)\n",
+    "    print(\"Conexión correcta.\")\n",
+    "except Exception as e:\n",
+    "    print(\"Error al conectar:\", e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "17174180",
+   "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",
+       "      <th>last_update</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",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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         last_update  \n",
+       "0 2005-05-26 22:04:30         1 2006-02-15 21:30:53  \n",
+       "1 2005-05-28 19:40:33         1 2006-02-15 21:30:53  \n",
+       "2 2005-06-01 22:12:39         1 2006-02-15 21:30:53  \n",
+       "3 2005-06-03 01:43:41         2 2006-02-15 21:30:53  \n",
+       "4 2005-06-02 04:33:21         1 2006-02-15 21:30:53  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_total.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0edf3ff",
+   "metadata": {},
+   "source": [
+    "Vamos a ver el rango total de fechas, pero primero quito la lomitacion de 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9b2fb147",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16044, 7)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_total = pd.read_sql(\"SELECT * FROM rental;\", engine)\n",
+    "df_total['rental_date'].min(), df_total['rental_date'].max()\n",
+    "df_total.shape\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4587a5dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PeriodArray>\n",
+       "['2005-05', '2005-06', '2005-07', '2005-08', '2006-02']\n",
+       "Length: 5, dtype: period[M]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_total['rental_date'].dt.to_period('M').unique() # esta formula nos dice los meses años que vienen en la base de datos\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6c48cabc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rental_date\n",
+       "2005-05    1156\n",
+       "2005-06    2311\n",
+       "2005-07    6709\n",
+       "2005-08    5686\n",
+       "2006-02     182\n",
+       "Freq: M, Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_total['rental_date'].dt.to_period('M').value_counts().sort_index()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8e19b5",
+   "metadata": {},
+   "source": [
+    "Escriba una función de Python llamada rentals_month que recupere datos de alquiler para un mes y año determinados (pasados ​​como parámetros) de la base de datos Sakila como un DataFrame de Pandas. La función debe tomar tres parámetros: engine: un objeto que representa el motor de conexión a la base de datos que se utilizará para establecer una conexión con la base de datos Sakila. month: un entero que representa el mes para el que se recuperarán los datos de alquiler. year: un entero que representa el año para el que se recuperarán los datos de alquiler  \n",
+    "La función debe ejecutar una consulta SQL para recuperar los datos de alquiler del mes y año especificados de la tabla de alquileres en la base de datos Sakila, y devolverlos como un DataFrame de pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13a6c91a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year): # engie para el return month, year para hacer la query de SQL\n",
+    "    \n",
+    "    query = f\"\"\"\n",
+    "        SELECT *\n",
+    "        FROM rental\n",
+    "        WHERE MONTH(rental_date) = {month} \n",
+    "          AND YEAR(rental_date) = {year}; \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    return pd.read_sql(query, engine) #  engine conectada a Sakila con \n",
+    "                                        # (function) def read_sql(sql: _SQLStatement,  con: _SQLConnection,-...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5813d2c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1156, 7)\n"
+     ]
+    },
+    {
+     "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",
+       "      <th>last_update</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",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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",
+       "      <td>2006-02-15 21:30:53</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         last_update  \n",
+       "0 2005-05-26 22:04:30         1 2006-02-15 21:30:53  \n",
+       "1 2005-05-28 19:40:33         1 2006-02-15 21:30:53  \n",
+       "2 2005-06-01 22:12:39         1 2006-02-15 21:30:53  \n",
+       "3 2005-06-03 01:43:41         2 2006-02-15 21:30:53  \n",
+       "4 2005-06-02 04:33:21         1 2006-02-15 21:30:53  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_rentals_5_2005 = rentals_month(engine,5,2005) \n",
+    "print(df_rentals_5_2005.shape)\n",
+    "df_rentals_5_2005.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "117f28ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(182, 7)\n"
+     ]
+    },
+    {
+     "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",
+       "      <th>last_update</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11496</td>\n",
+       "      <td>2006-02-14 15:16:03</td>\n",
+       "      <td>2047</td>\n",
+       "      <td>155</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11541</td>\n",
+       "      <td>2006-02-14 15:16:03</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>335</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11563</td>\n",
+       "      <td>2006-02-14 15:16:03</td>\n",
+       "      <td>1545</td>\n",
+       "      <td>83</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11577</td>\n",
+       "      <td>2006-02-14 15:16:03</td>\n",
+       "      <td>4106</td>\n",
+       "      <td>219</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11593</td>\n",
+       "      <td>2006-02-14 15:16:03</td>\n",
+       "      <td>817</td>\n",
+       "      <td>99</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   rental_id         rental_date  inventory_id  customer_id return_date  \\\n",
+       "0      11496 2006-02-14 15:16:03          2047          155        None   \n",
+       "1      11541 2006-02-14 15:16:03          2026          335        None   \n",
+       "2      11563 2006-02-14 15:16:03          1545           83        None   \n",
+       "3      11577 2006-02-14 15:16:03          4106          219        None   \n",
+       "4      11593 2006-02-14 15:16:03           817           99        None   \n",
+       "\n",
+       "   staff_id         last_update  \n",
+       "0         1 2006-02-15 21:30:53  \n",
+       "1         1 2006-02-15 21:30:53  \n",
+       "2         1 2006-02-15 21:30:53  \n",
+       "3         2 2006-02-15 21:30:53  \n",
+       "4         1 2006-02-15 21:30:53  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_rentals_2_2006 = rentals_month(engine,2,2006) \n",
+    "print(df_rentals_2_2006.shape)\n",
+    "df_rentals_2_2006.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa9fa48f",
+   "metadata": {},
+   "source": [
+    "Desarrolle una función de Python llamada Rental_count_month que tome el DataFrame proporcionado por Rentals_month como entrada junto con el mes y el año y devuelva un nuevo DataFrame que contenga la cantidad de alquileres realizados por cada customer_id durante el mes y año seleccionados.  \n",
+    "\n",
+    "La función también debe incluir el mes y el año como parámetros y usarlos para nombrar la nueva columna según el mes y el año, por ejemplo, si el mes de entrada es 05 y el año es 2005, el nombre de la columna debe ser \"alquileres_05_2005\".  \n",
+    "\n",
+    "Sugerencia: considere utilizar pandas groupby()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "19546fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(df, month, year):\n",
+    "\n",
+    "    nombre_columna_nueva = \"alquileres\"+\"_\"+ str(month).zfill(2) + \"_\" + str(year)\n",
+    "    nombre_columna_nueva\n",
+    "    serie_alquileres = df.groupby('customer_id').size()\n",
+    "    df_final = serie_alquileres.reset_index()\n",
+    "    df_final.columns = [\"customer_id\",nombre_columna_nueva ]\n",
+    "    df_final.head()\n",
+    "\n",
+    "    return df_final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6ca58b80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <th>3</th>\n",
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+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  alquileres_05_2005\n",
+       "0            1                   2\n",
+       "1            2                   1\n",
+       "2            3                   2\n",
+       "3            5                   3\n",
+       "4            6                   3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_alquileres_5_2005 = rental_count_month(df_rentals_5_2005,5,2005) \n",
+    "df_alquileres_5_2005.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3d0b2394",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "    }\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_02_2006</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  alquileres_02_2006\n",
+       "0            5                   1\n",
+       "1            9                   1\n",
+       "2           11                   1\n",
+       "3           14                   1\n",
+       "4           15                   2"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_alquileres_2_2006 = rental_count_month(df_rentals_2_2006,2,2006) \n",
+    "df_alquileres_2_2006.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efcbc164",
+   "metadata": {},
+   "source": [
+    "Crea una función en Python compare_rentals que reciba como entrada dos DataFrames que contengan el número de alquileres realizados por cada cliente en diferentes meses y años.   \n",
+    "La función debe devolver un DataFrame combinado con una nueva columna llamada 'diferencia', que representa la diferencia entre el número de alquileres en los dos meses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "16ae0a80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    df_merged = df1.merge(df2, on=\"customer_id\", how=\"outer\").fillna(0).astype(int)# por que piden data frame combinado y aprovecho y quito int\n",
+    "  \n",
+    "    col1 = df1.columns[1]\n",
+    "    col2 = df2.columns[1]\n",
+    "\n",
+    "    df_merged[\"diferencia\"] = (df_merged[col1] - df_merged[col2]).astype(int)\n",
+    "\n",
+    "    return df_merged\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "58730767",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "</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_02_2006</th>\n",
+       "      <th>diferencia</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>594</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>535</th>\n",
+       "      <td>595</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>596</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>597</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>599</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>539 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  alquileres_05_2005  alquileres_02_2006  diferencia\n",
+       "0              1                   2                   0           2\n",
+       "1              2                   1                   0           1\n",
+       "2              3                   2                   0           2\n",
+       "3              5                   3                   1           2\n",
+       "4              6                   3                   0           3\n",
+       "..           ...                 ...                 ...         ...\n",
+       "534          594                   4                   0           4\n",
+       "535          595                   1                   0           1\n",
+       "536          596                   6                   1           5\n",
+       "537          597                   2                   1           1\n",
+       "538          599                   1                   0           1\n",
+       "\n",
+       "[539 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_rentals(df_alquileres_5_2005,df_alquileres_2_2006)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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