diff --git a/Notebooks/Laboratorio con Soluciones.ipynb b/Notebooks/Laboratorio con Soluciones.ipynb
deleted file mode 100644
index c841bd0..0000000
--- a/Notebooks/Laboratorio con Soluciones.ipynb	
+++ /dev/null
@@ -1,1545 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Dataging](https://raw.githubusercontent.com/dataging/public-resources/61263724aea5476ba5ebf38478beada519091957/logodataging.png)\n",
-    "\n",
-    "# Base de datos Sakila\n",
-    "\n",
-    "Uno de los mejores ejemplos de bases de datos es la <a href=\"https://dev.mysql.com/doc/sakila/en/\">Base de Datos Sakila</a>, que fue creada originalmente por MySQL y ha sido liberada bajo los términos de la Licencia BSD.\n",
-    "\n",
-    "La base de datos de Sakila es un esquema muy bien normalizado que modela una tienda de alquiler de DVD, con cosas como películas, actores, relaciones entre actores y una tabla de inventario central que conecta películas, tiendas y alquileres.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "## Vamos allá! "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "from dotenv import load_dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "load_dotenv() #lee las variables de entorno del archivo .env\n",
-    "\n",
-    "servidor = os.getenv(\"SERVIDOR_MYSQL\")\n",
-    "usuario = os.getenv(\"USUARIO_MYSQL\")\n",
-    "password = os.getenv(\"PASSWORD_MYSQL\")\n",
-    "\n",
-    "import mysql.connector\n",
-    "try:\n",
-    "    cnx = mysql.connector.connect(user=usuario, password=password,\n",
-    "                              host=servidor, database='sakila') # Definimos la cadena de conexión de la base de datos e intentamos conectar\n",
-    "    cursor=cnx.cursor() #Creamos un cursor para poder ejecutar consultas    \n",
-    "except :\n",
-    "    print(\"Error conectando a la base de datos \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cargamos los Datos:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\adminsql\\AppData\\Local\\Temp\\ipykernel_10316\\1934773457.py:1: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
-      "  df = pd.read_sql('''\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "df = pd.read_sql('''\n",
-    "    SELECT\n",
-    "        rental.rental_id, rental.rental_date, rental.return_date,\n",
-    "        customer.last_name AS customer_lastname,\n",
-    "        store.store_id,\n",
-    "        city.city AS rental_store_city,\n",
-    "        film.title AS film_title, film.rental_duration AS film_rental_duration,\n",
-    "        film.rental_rate AS film_rental_rate, film.replacement_cost AS film_replacement_cost,\n",
-    "        film.rating AS film_rating\n",
-    "    FROM rental\n",
-    "    INNER JOIN customer ON rental.customer_id = customer.customer_id\n",
-    "    INNER JOIN inventory ON rental.inventory_id = inventory.inventory_id\n",
-    "    INNER JOIN store ON inventory.store_id = store.store_id\n",
-    "    INNER JOIN address ON store.address_id = address.address_id\n",
-    "    INNER JOIN city ON address.city_id = city.city_id\n",
-    "    INNER JOIN film ON inventory.film_id = film.film_id\n",
-    "    ;\n",
-    "''', cnx, index_col='rental_id', parse_dates=['rental_date', 'return_date'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Echamos un vistazo a los datos:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "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_date</th>\n",
-       "      <th>return_date</th>\n",
-       "      <th>customer_lastname</th>\n",
-       "      <th>store_id</th>\n",
-       "      <th>rental_store_city</th>\n",
-       "      <th>film_title</th>\n",
-       "      <th>film_rental_duration</th>\n",
-       "      <th>film_rental_rate</th>\n",
-       "      <th>film_replacement_cost</th>\n",
-       "      <th>film_rating</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rental_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4863</th>\n",
-       "      <td>2005-07-08 19:03:15</td>\n",
-       "      <td>2005-07-11 21:29:15</td>\n",
-       "      <td>FRANCISCO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>ACADEMY DINOSAUR</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>20.99</td>\n",
-       "      <td>PG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11433</th>\n",
-       "      <td>2005-08-02 20:13:10</td>\n",
-       "      <td>2005-08-11 21:35:10</td>\n",
-       "      <td>HARDER</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>ACADEMY DINOSAUR</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>20.99</td>\n",
-       "      <td>PG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14714</th>\n",
-       "      <td>2005-08-21 21:27:43</td>\n",
-       "      <td>2005-08-30 22:26:43</td>\n",
-       "      <td>SHELTON</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>ACADEMY DINOSAUR</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>20.99</td>\n",
-       "      <td>PG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>972</th>\n",
-       "      <td>2005-05-30 20:21:07</td>\n",
-       "      <td>2005-06-06 00:36:07</td>\n",
-       "      <td>CURRIER</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>ACADEMY DINOSAUR</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>20.99</td>\n",
-       "      <td>PG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2117</th>\n",
-       "      <td>2005-06-17 20:24:00</td>\n",
-       "      <td>2005-06-23 17:45:00</td>\n",
-       "      <td>ARNOLD</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>ACADEMY DINOSAUR</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>20.99</td>\n",
-       "      <td>PG</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  rental_date         return_date customer_lastname  store_id  \\\n",
-       "rental_id                                                                       \n",
-       "4863      2005-07-08 19:03:15 2005-07-11 21:29:15         FRANCISCO         1   \n",
-       "11433     2005-08-02 20:13:10 2005-08-11 21:35:10            HARDER         1   \n",
-       "14714     2005-08-21 21:27:43 2005-08-30 22:26:43           SHELTON         1   \n",
-       "972       2005-05-30 20:21:07 2005-06-06 00:36:07           CURRIER         1   \n",
-       "2117      2005-06-17 20:24:00 2005-06-23 17:45:00            ARNOLD         1   \n",
-       "\n",
-       "          rental_store_city        film_title  film_rental_duration  \\\n",
-       "rental_id                                                             \n",
-       "4863             Lethbridge  ACADEMY DINOSAUR                     6   \n",
-       "11433            Lethbridge  ACADEMY DINOSAUR                     6   \n",
-       "14714            Lethbridge  ACADEMY DINOSAUR                     6   \n",
-       "972              Lethbridge  ACADEMY DINOSAUR                     6   \n",
-       "2117             Lethbridge  ACADEMY DINOSAUR                     6   \n",
-       "\n",
-       "           film_rental_rate  film_replacement_cost film_rating  \n",
-       "rental_id                                                       \n",
-       "4863                   0.99                  20.99          PG  \n",
-       "11433                  0.99                  20.99          PG  \n",
-       "14714                  0.99                  20.99          PG  \n",
-       "972                    0.99                  20.99          PG  \n",
-       "2117                   0.99                  20.99          PG  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(16044, 10)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 16044 entries, 4863 to 12894\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column                 Non-Null Count  Dtype         \n",
-      "---  ------                 --------------  -----         \n",
-      " 0   rental_date            16044 non-null  datetime64[ns]\n",
-      " 1   return_date            15861 non-null  datetime64[ns]\n",
-      " 2   customer_lastname      16044 non-null  object        \n",
-      " 3   store_id               16044 non-null  int64         \n",
-      " 4   rental_store_city      16044 non-null  object        \n",
-      " 5   film_title             16044 non-null  object        \n",
-      " 6   film_rental_duration   16044 non-null  int64         \n",
-      " 7   film_rental_rate       16044 non-null  float64       \n",
-      " 8   film_replacement_cost  16044 non-null  float64       \n",
-      " 9   film_rating            16044 non-null  object        \n",
-      "dtypes: datetime64[ns](2), float64(2), int64(2), object(4)\n",
-      "memory usage: 1.3+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "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_date</th>\n",
-       "      <th>return_date</th>\n",
-       "      <th>store_id</th>\n",
-       "      <th>film_rental_duration</th>\n",
-       "      <th>film_rental_rate</th>\n",
-       "      <th>film_replacement_cost</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>16044</td>\n",
-       "      <td>15861</td>\n",
-       "      <td>16044.000000</td>\n",
-       "      <td>16044.00000</td>\n",
-       "      <td>16044.000000</td>\n",
-       "      <td>16044.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2005-07-23 08:12:53.217526912</td>\n",
-       "      <td>2005-07-25 23:58:03.138768128</td>\n",
-       "      <td>1.506171</td>\n",
-       "      <td>4.93549</td>\n",
-       "      <td>2.942630</td>\n",
-       "      <td>20.215443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>2005-05-24 22:53:30</td>\n",
-       "      <td>2005-05-25 23:55:21</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>3.00000</td>\n",
-       "      <td>0.990000</td>\n",
-       "      <td>9.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2005-07-07 00:58:40.500000</td>\n",
-       "      <td>2005-07-10 15:49:36</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>4.00000</td>\n",
-       "      <td>0.990000</td>\n",
-       "      <td>14.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>2005-07-28 16:04:32.500000</td>\n",
-       "      <td>2005-08-01 19:45:29</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5.00000</td>\n",
-       "      <td>2.990000</td>\n",
-       "      <td>20.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2005-08-17 21:16:23</td>\n",
-       "      <td>2005-08-20 23:35:55</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>6.00000</td>\n",
-       "      <td>4.990000</td>\n",
-       "      <td>25.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>2006-02-14 15:16:03</td>\n",
-       "      <td>2005-09-02 02:35:22</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>7.00000</td>\n",
-       "      <td>4.990000</td>\n",
-       "      <td>29.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.499978</td>\n",
-       "      <td>1.40169</td>\n",
-       "      <td>1.649678</td>\n",
-       "      <td>6.081771</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         rental_date                    return_date  \\\n",
-       "count                          16044                          15861   \n",
-       "mean   2005-07-23 08:12:53.217526912  2005-07-25 23:58:03.138768128   \n",
-       "min              2005-05-24 22:53:30            2005-05-25 23:55:21   \n",
-       "25%       2005-07-07 00:58:40.500000            2005-07-10 15:49:36   \n",
-       "50%       2005-07-28 16:04:32.500000            2005-08-01 19:45:29   \n",
-       "75%              2005-08-17 21:16:23            2005-08-20 23:35:55   \n",
-       "max              2006-02-14 15:16:03            2005-09-02 02:35:22   \n",
-       "std                              NaN                            NaN   \n",
-       "\n",
-       "           store_id  film_rental_duration  film_rental_rate  \\\n",
-       "count  16044.000000           16044.00000      16044.000000   \n",
-       "mean       1.506171               4.93549          2.942630   \n",
-       "min        1.000000               3.00000          0.990000   \n",
-       "25%        1.000000               4.00000          0.990000   \n",
-       "50%        2.000000               5.00000          2.990000   \n",
-       "75%        2.000000               6.00000          4.990000   \n",
-       "max        2.000000               7.00000          4.990000   \n",
-       "std        0.499978               1.40169          1.649678   \n",
-       "\n",
-       "       film_replacement_cost  \n",
-       "count           16044.000000  \n",
-       "mean               20.215443  \n",
-       "min                 9.990000  \n",
-       "25%                14.990000  \n",
-       "50%                20.990000  \n",
-       "75%                25.990000  \n",
-       "max                29.990000  \n",
-       "std                 6.081771  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Análisis Numérico y Visualización\n",
-    "\n",
-    "Analiza estadísticamente la columna `film_rental_rate` :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    16044.000000\n",
-       "mean         2.942630\n",
-       "std          1.649678\n",
-       "min          0.990000\n",
-       "25%          0.990000\n",
-       "50%          2.990000\n",
-       "75%          4.990000\n",
-       "max          4.990000\n",
-       "Name: film_rental_rate, dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['film_rental_rate'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(2.9426302667663933)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calcula la Media\n",
-    "df['film_rental_rate'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(2.99)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calcula la Mediana\n",
-    "df['film_rental_rate'].median()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Xp8mTJ5cAetxxx4Vb1wIZNSMvukMOOaTx12eccUa5zftc5E0F84aCa15tI8eSHMO+973vlcP882k8OZrkK2zQ9ub7vffeS2eeeWaJZttuu205f/6+++5r8nvQOl155ZXlNl9UYU3Tp09PJ510Unk/z3W7du2a7GmRX4T98Ic/TOecc04aPHhwmjFjRrObvVObc/3WW2+lU045pcS07bbbrpy+NWfOHPtYtXL5tJz8H+r8vToH0Pw9OQeTfIXhzJqOO9fWdNtmbcdifbcNr7zySolhb775Ztphhx3SZz/72fToo4+W96Ot67pKPt8BAAAAAIKyBxkAAAAAoQlkAAAAAIQmkAEAAAAQmkAGAAAAQGgCGQAAAAChCWQAAAAAhCaQAQAAABCaQAYAAABAaAIZAAAAAKEJZAAAAACEJpABAAAAkCL7Py16kglEbba7AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Box Plot Horizontal del campo film_rental_rate\n",
-    "\n",
-    "df['film_rental_rate'].plot(kind='box', vert=False, figsize=(14,6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: ylabel='Density'>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Gráfico de Densidad del campo film_rental_rate\n",
-    "\n",
-    "df['film_rental_rate'].plot(kind='density', figsize=(14,6)) # kde"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Número de Alquileres')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Gráfico de Barras con el número de alquileres por cada valor de film_rental_rate\n",
-    "\n",
-    "ax = df['film_rental_rate'].value_counts().plot(kind='bar', figsize=(14,6))\n",
-    "ax.set_ylabel('Número de Alquileres')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Análisis Categórico y Visualización \n",
-    "\n",
-    "Ahora vamos a analizar la columna `rental_store_city`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rental_store_city\n",
-       "Woodridge     8121\n",
-       "Lethbridge    7923\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Cuántos valores de cada ciudad tenemos?\n",
-    "\n",
-    "df['rental_store_city'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: ylabel='count'>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Los mostramos como un gráfico de tarta\n",
-    "\n",
-    "df['rental_store_city'].value_counts().plot(kind='pie', figsize=(6,6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Número de Alquileres')"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Los mostramos como un gráfico de barras\n",
-    "\n",
-    "ax = df['rental_store_city'].value_counts().plot(kind='bar', figsize=(14,6))\n",
-    "ax.set_ylabel('Número de Alquileres')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Limpieza de las Columnas\n",
-    "\n",
-    "Recuerda que podemos crear nuevas columnas y/o modificar columnas existentes.\n",
-    "\n",
-    "### Agrega y calcula una nueva columna `rental_rate_return` \n",
-    "\n",
-    "Queremos saber el ratio de retorno de cada pelígcula. Utilizaremos esta fórmula: \n",
-    "\n",
-    "$$ rental\\_gain\\_return = \\frac{film\\_rental\\_rate}{film\\_replacement\\_cost} * 100 $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rental_id\n",
-       "4863     4.716532\n",
-       "11433    4.716532\n",
-       "14714    4.716532\n",
-       "972      4.716532\n",
-       "2117     4.716532\n",
-       "Name: rental_gain_return, dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['rental_gain_return'] = df['film_rental_rate'] / df['film_replacement_cost'] * 100\n",
-    "\n",
-    "df['rental_gain_return'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: ylabel='Density'>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# La mostramos como un gráfico de densidad\n",
-    "\n",
-    "df['rental_gain_return'].plot(kind='density', figsize=(14,6))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(16.34)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calculamos la media de ganancia redondeando a 2 decimales\n",
-    "df['rental_gain_return'].mean().round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(13.6)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Ahora la mediana \n",
-    "df['rental_gain_return'].median().round(2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.lines.Line2D at 0x1e182393110>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Ahora lo mostramos como un gráfico de densidad incluyendo una linea vertical para la media de color rojo y otra para la mediana de color verde\n",
-    "\n",
-    "ax = df['rental_gain_return'].plot(kind='density', figsize=(14,6)) # kde\n",
-    "ax.axvline(df['rental_gain_return'].mean(), color='red')\n",
-    "ax.axvline(df['rental_gain_return'].median(), color='green')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> Cada Alquiler representa <b>13.6%</b> del coste ."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Por lo tanto necesitamos <b>7.35</b> para recuperar cada precio de venta (`film_replacement_cost`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.352941176470589"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "100 / 13.6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "De media cada película se alquila <b>16.74</b> veces. Calcúlalo!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(16.747390396659707)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['film_title'].value_counts().mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filtrado e Indexación:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Obtenemos los alquileres del cliente con apellido  `HANSEN`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "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_date</th>\n",
-       "      <th>return_date</th>\n",
-       "      <th>customer_lastname</th>\n",
-       "      <th>store_id</th>\n",
-       "      <th>rental_store_city</th>\n",
-       "      <th>film_title</th>\n",
-       "      <th>film_rental_duration</th>\n",
-       "      <th>film_rental_rate</th>\n",
-       "      <th>film_replacement_cost</th>\n",
-       "      <th>film_rating</th>\n",
-       "      <th>rental_gain_return</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rental_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1368</th>\n",
-       "      <td>2005-06-15 14:27:47</td>\n",
-       "      <td>2005-06-23 18:07:47</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>HUNCHBACK IMPOSSIBLE</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>28.99</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>17.212832</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14350</th>\n",
-       "      <td>2005-08-21 08:58:38</td>\n",
-       "      <td>2005-08-30 03:29:38</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>PRIMARY GLASS</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>16.99</td>\n",
-       "      <td>G</td>\n",
-       "      <td>5.826957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8597</th>\n",
-       "      <td>2005-07-29 12:55:55</td>\n",
-       "      <td>2005-08-05 18:54:55</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>TUXEDO MILE</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>24.99</td>\n",
-       "      <td>R</td>\n",
-       "      <td>11.964786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5209</th>\n",
-       "      <td>2005-07-09 11:22:39</td>\n",
-       "      <td>2005-07-17 09:31:39</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Lethbridge</td>\n",
-       "      <td>WHALE BIKINI</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>11.99</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>41.618015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2603</th>\n",
-       "      <td>2005-06-19 06:21:25</td>\n",
-       "      <td>2005-06-26 03:19:25</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>CAT CONEHEADS</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>G</td>\n",
-       "      <td>33.288859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6497</th>\n",
-       "      <td>2005-07-12 03:04:29</td>\n",
-       "      <td>2005-07-17 21:36:29</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>DANCING FEVER</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>25.99</td>\n",
-       "      <td>G</td>\n",
-       "      <td>3.809157</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13477</th>\n",
-       "      <td>2005-08-20 01:07:00</td>\n",
-       "      <td>2005-08-26 02:47:00</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>FINDING ANACONDA</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>10.99</td>\n",
-       "      <td>R</td>\n",
-       "      <td>9.008189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5635</th>\n",
-       "      <td>2005-07-10 06:28:39</td>\n",
-       "      <td>2005-07-17 08:35:39</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>FISH OPUS</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>22.99</td>\n",
-       "      <td>R</td>\n",
-       "      <td>13.005655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12179</th>\n",
-       "      <td>2005-08-18 01:21:21</td>\n",
-       "      <td>2005-08-19 00:59:21</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>FOREVER CANDIDATE</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>28.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>10.313901</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2005-05-24 23:05:21</td>\n",
-       "      <td>2005-06-02 04:33:21</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>IDOLS SNATCHERS</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>29.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>9.969990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>809</th>\n",
-       "      <td>2005-05-29 19:10:20</td>\n",
-       "      <td>2005-06-05 19:05:20</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>INDIAN LOVE</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>26.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>3.668025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>2005-05-25 21:48:41</td>\n",
-       "      <td>2005-06-02 18:28:41</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>JUMPING WRATH</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>18.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>5.213270</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7786</th>\n",
-       "      <td>2005-07-28 07:18:26</td>\n",
-       "      <td>2005-07-29 03:00:26</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>KARATE MOON</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>21.99</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>4.502046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8787</th>\n",
-       "      <td>2005-07-29 20:43:49</td>\n",
-       "      <td>2005-07-31 15:15:49</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>LEGALLY SECRETARY</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>PG</td>\n",
-       "      <td>33.288859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>416</th>\n",
-       "      <td>2005-05-27 15:02:10</td>\n",
-       "      <td>2005-05-29 10:34:10</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>LESSON CLEOPATRA</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>28.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>3.414971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5266</th>\n",
-       "      <td>2005-07-09 14:17:40</td>\n",
-       "      <td>2005-07-16 10:42:40</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>LOATHING LEGALLY</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>29.99</td>\n",
-       "      <td>R</td>\n",
-       "      <td>3.301100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5592</th>\n",
-       "      <td>2005-07-10 04:26:13</td>\n",
-       "      <td>2005-07-19 02:32:13</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>LUKE MUMMY</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>21.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>13.597090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10043</th>\n",
-       "      <td>2005-07-31 19:02:07</td>\n",
-       "      <td>2005-08-07 17:58:07</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>MARS ROMAN</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>21.99</td>\n",
-       "      <td>NC-17</td>\n",
-       "      <td>4.502046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1006</th>\n",
-       "      <td>2005-05-31 00:57:08</td>\n",
-       "      <td>2005-06-02 22:35:08</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>SALUTE APOLLO</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>29.99</td>\n",
-       "      <td>R</td>\n",
-       "      <td>9.969990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6129</th>\n",
-       "      <td>2005-07-11 08:15:09</td>\n",
-       "      <td>2005-07-18 13:00:09</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>STOCK GLASS</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>10.99</td>\n",
-       "      <td>PG</td>\n",
-       "      <td>27.206551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8300</th>\n",
-       "      <td>2005-07-29 02:57:59</td>\n",
-       "      <td>2005-08-05 01:12:59</td>\n",
-       "      <td>HANSEN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Woodridge</td>\n",
-       "      <td>VOYAGE LEGALLY</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>28.99</td>\n",
-       "      <td>PG-13</td>\n",
-       "      <td>3.414971</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  rental_date         return_date customer_lastname  store_id  \\\n",
-       "rental_id                                                                       \n",
-       "1368      2005-06-15 14:27:47 2005-06-23 18:07:47            HANSEN         1   \n",
-       "14350     2005-08-21 08:58:38 2005-08-30 03:29:38            HANSEN         1   \n",
-       "8597      2005-07-29 12:55:55 2005-08-05 18:54:55            HANSEN         1   \n",
-       "5209      2005-07-09 11:22:39 2005-07-17 09:31:39            HANSEN         1   \n",
-       "2603      2005-06-19 06:21:25 2005-06-26 03:19:25            HANSEN         2   \n",
-       "6497      2005-07-12 03:04:29 2005-07-17 21:36:29            HANSEN         2   \n",
-       "13477     2005-08-20 01:07:00 2005-08-26 02:47:00            HANSEN         2   \n",
-       "5635      2005-07-10 06:28:39 2005-07-17 08:35:39            HANSEN         2   \n",
-       "12179     2005-08-18 01:21:21 2005-08-19 00:59:21            HANSEN         2   \n",
-       "5         2005-05-24 23:05:21 2005-06-02 04:33:21            HANSEN         2   \n",
-       "809       2005-05-29 19:10:20 2005-06-05 19:05:20            HANSEN         2   \n",
-       "134       2005-05-25 21:48:41 2005-06-02 18:28:41            HANSEN         2   \n",
-       "7786      2005-07-28 07:18:26 2005-07-29 03:00:26            HANSEN         2   \n",
-       "8787      2005-07-29 20:43:49 2005-07-31 15:15:49            HANSEN         2   \n",
-       "416       2005-05-27 15:02:10 2005-05-29 10:34:10            HANSEN         2   \n",
-       "5266      2005-07-09 14:17:40 2005-07-16 10:42:40            HANSEN         2   \n",
-       "5592      2005-07-10 04:26:13 2005-07-19 02:32:13            HANSEN         2   \n",
-       "10043     2005-07-31 19:02:07 2005-08-07 17:58:07            HANSEN         2   \n",
-       "1006      2005-05-31 00:57:08 2005-06-02 22:35:08            HANSEN         2   \n",
-       "6129      2005-07-11 08:15:09 2005-07-18 13:00:09            HANSEN         2   \n",
-       "8300      2005-07-29 02:57:59 2005-08-05 01:12:59            HANSEN         2   \n",
-       "\n",
-       "          rental_store_city            film_title  film_rental_duration  \\\n",
-       "rental_id                                                                 \n",
-       "1368             Lethbridge  HUNCHBACK IMPOSSIBLE                     4   \n",
-       "14350            Lethbridge         PRIMARY GLASS                     7   \n",
-       "8597             Lethbridge           TUXEDO MILE                     3   \n",
-       "5209             Lethbridge          WHALE BIKINI                     4   \n",
-       "2603              Woodridge         CAT CONEHEADS                     5   \n",
-       "6497              Woodridge         DANCING FEVER                     6   \n",
-       "13477             Woodridge      FINDING ANACONDA                     4   \n",
-       "5635              Woodridge             FISH OPUS                     4   \n",
-       "12179             Woodridge     FOREVER CANDIDATE                     7   \n",
-       "5                 Woodridge       IDOLS SNATCHERS                     5   \n",
-       "809               Woodridge           INDIAN LOVE                     4   \n",
-       "134               Woodridge         JUMPING WRATH                     4   \n",
-       "7786              Woodridge           KARATE MOON                     4   \n",
-       "8787              Woodridge     LEGALLY SECRETARY                     7   \n",
-       "416               Woodridge      LESSON CLEOPATRA                     3   \n",
-       "5266              Woodridge      LOATHING LEGALLY                     4   \n",
-       "5592              Woodridge            LUKE MUMMY                     5   \n",
-       "10043             Woodridge            MARS ROMAN                     6   \n",
-       "1006              Woodridge         SALUTE APOLLO                     4   \n",
-       "6129              Woodridge           STOCK GLASS                     7   \n",
-       "8300              Woodridge        VOYAGE LEGALLY                     6   \n",
-       "\n",
-       "           film_rental_rate  film_replacement_cost film_rating  \\\n",
-       "rental_id                                                        \n",
-       "1368                   4.99                  28.99       PG-13   \n",
-       "14350                  0.99                  16.99           G   \n",
-       "8597                   2.99                  24.99           R   \n",
-       "5209                   4.99                  11.99       PG-13   \n",
-       "2603                   4.99                  14.99           G   \n",
-       "6497                   0.99                  25.99           G   \n",
-       "13477                  0.99                  10.99           R   \n",
-       "5635                   2.99                  22.99           R   \n",
-       "12179                  2.99                  28.99       NC-17   \n",
-       "5                      2.99                  29.99       NC-17   \n",
-       "809                    0.99                  26.99       NC-17   \n",
-       "134                    0.99                  18.99       NC-17   \n",
-       "7786                   0.99                  21.99       PG-13   \n",
-       "8787                   4.99                  14.99          PG   \n",
-       "416                    0.99                  28.99       NC-17   \n",
-       "5266                   0.99                  29.99           R   \n",
-       "5592                   2.99                  21.99       NC-17   \n",
-       "10043                  0.99                  21.99       NC-17   \n",
-       "1006                   2.99                  29.99           R   \n",
-       "6129                   2.99                  10.99          PG   \n",
-       "8300                   0.99                  28.99       PG-13   \n",
-       "\n",
-       "           rental_gain_return  \n",
-       "rental_id                      \n",
-       "1368                17.212832  \n",
-       "14350                5.826957  \n",
-       "8597                11.964786  \n",
-       "5209                41.618015  \n",
-       "2603                33.288859  \n",
-       "6497                 3.809157  \n",
-       "13477                9.008189  \n",
-       "5635                13.005655  \n",
-       "12179               10.313901  \n",
-       "5                    9.969990  \n",
-       "809                  3.668025  \n",
-       "134                  5.213270  \n",
-       "7786                 4.502046  \n",
-       "8787                33.288859  \n",
-       "416                  3.414971  \n",
-       "5266                 3.301100  \n",
-       "5592                13.597090  \n",
-       "10043                4.502046  \n",
-       "1006                 9.969990  \n",
-       "6129                27.206551  \n",
-       "8300                 3.414971  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['customer_lastname'] == 'HANSEN']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creamos una lista con todas las películas que tienen el mayor coste de reemplazo "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(29.99)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Coste de reemplazo máximo\n",
-    "\n",
-    "df['film_replacement_cost'].max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['BLINDNESS GUN', 'CHARIOTS CONSPIRACY', 'CRUELTY UNFORGIVEN',\n",
-       "       'CUPBOARD SINNERS', 'DESPERATE TRAINSPOTTING', 'DIRTY ACE',\n",
-       "       'DOCTOR GRAIL', 'EARTH VISION', 'EVERYONE CRAFT', 'FANTASIA PARK',\n",
-       "       'FEUD FROGMEN', 'FLATLINERS KILLER', 'GILMORE BOILED',\n",
-       "       'GOLDFINGER SENSIBILITY', 'GRAFFITI LOVE', 'HILLS NEIGHBORS',\n",
-       "       'HOLLYWOOD ANONYMOUS', 'HONEY TIES', 'IDOLS SNATCHERS',\n",
-       "       'JEEPERS WEDDING', 'JERICHO MULAN', 'LAWLESS VISION',\n",
-       "       'LOATHING LEGALLY', 'OSCAR GOLD', 'PATIENT SISTER',\n",
-       "       'POSEIDON FOREVER', 'PRINCESS GIANT', 'QUEST MUSSOLINI',\n",
-       "       'REIGN GENTLEMEN', 'RIVER OUTLAW', 'SALUTE APOLLO',\n",
-       "       'SLACKER LIAISONS', 'SMILE EARRING', 'SONG HEDWIG', 'TRACY CIDER',\n",
-       "       'UNCUT SUICIDES', 'VIRGIN DAISY', 'WEST LION', 'WYOMING STORM',\n",
-       "       'ARABIA DOGMA', 'BALLROOM MOCKINGBIRD', 'BONNIE HOLOCAUST',\n",
-       "       'CLOCKWORK PARADISE', 'CLYDE THEORY', 'EXTRAORDINARY CONQUERER',\n",
-       "       'JAPANESE RUN', 'JINGLE SAGEBRUSH', 'LOVER TRUMAN', 'RANDOM GO',\n",
-       "       'RESERVOIR ADAPTATION', 'RIGHT CRANES', 'SASSY PACKER'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Título de las películas con el coste de reemplazo máximo\n",
-    "\n",
-    "df.loc[df['film_replacement_cost'] == df['film_replacement_cost'].max(), 'film_title'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cuántas películas con rating `PG` o `PG-13` se han alquilado?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6797"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[(df['film_rating'] == 'PG') | (df['film_rating'] == 'PG-13')].shape[0]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "entornoIFFE",
-   "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": "3.13.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}