analisis_exploratorio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TOvht7vqQGdR"
   },
   "source": [
    "# Análisis Exploratorio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8hg00soETQ3z"
   },
   "source": [
    "**¿Qué es el análisis exploratorio de datos?**\n",
    "\n",
    "El análisis exploratorio de datos (*EDA, Exploratory Data Analysis*) es entender los datos resumiendo sus principales características, generalmente utilizando algún tipo de representación gráfica. Este es un paso muy importante para llegar a la modelización de los datos que luego se puede utilizar para aplicar Machine learning para algún tipo de análisis predictivo. Explorar los datos no es tarea sencilla, suele llevar una cantidad de tiempo considerable. A través del análisis exploratorio podemos definir claramente el problema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZfelutoyTS25"
   },
   "source": [
    "**¿Cómo realizamos el análisis exploratorio?**\n",
    "\n",
    "Bueno, esta es LA pregunta. Y lamentablemente como suele suceder en estos casos la respuesta es: Depende. Básicamente porque depende del conjunto de datos que se está analizando. No existe una receta que se aplique para todos los casos. La idea fundamental es aplicar diferentes herramientas y estrategias que permitan tener una mejor idea. Puede ser un proceso iterativo en donde cada ciclo hace que uno tenga mejor entendimiento."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n3VfNkBBw15s"
   },
   "source": [
    "**¿Qué datos vamos a explorar hoy?**\n",
    "\n",
    "Hoy vamos a analizar datos sobre autos. El dataset tiene más de 10k registros con más de 10 columnas que detallan características de los autos como por ejemplo: el tipo de combustible que usan, la potencia del motor, la caja de trasmisión que usan, el consumo en ciudad y en ruta y mucho más. En este ejercicio vamos a explorar los datos y dejarlos listos para modelarlos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FPSqz1wzTXvz"
   },
   "source": [
    "## 1. Importamos los paquetes necesarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9eLMx1Ebwa92"
   },
   "source": [
    "A continuación tenemos los paquetes que vamos a utilizar en este ejercicio. Esto lo hacemos siempre al principio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GGyDovL2QDLa"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns                       #visualisation\n",
    "import matplotlib.pyplot as plt             #visualisation\n",
    "%matplotlib inline     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Y3Z2DbKTfJt"
   },
   "source": [
    "## 2. Cargamos la \"data\" en un \"dataframe\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ko5zGJFCySaz"
   },
   "source": [
    "El dataset viene en un formato de archivo que se llama CSV (*comma separated values*). Es un archivo de texto plano donde cada registro se pone en una línea del mismo y cada columna es separada por comas. Es un formato muy simple que se utiliza muchísimo para el intercambio de datos. \n",
    "\n",
    "Cargar los datos en un *dataframe* es uno de los pasos más importantes en el análisis exploratorio. Pandas tiene un método que nos permite leer un archivo CSV y cargarlo directamente en un dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LgzUzD61IM8h"
   },
   "source": [
    "> **Google Colab**: para cargar el archivo CSV en Colab a la izquierda del notebook vas a ver un ícono de una carpeta. Si hacés click sobre esa carpeta te da la opción de subir el archivo. También podés ya tenerlo cargado en el Drive. \n"
   ]
  },
  {
   "attachments": {
    "upload_csv_colab.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![upload_csv_colab.png](attachment:upload_csv_colab.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para leer el archivo tenemos que usar el método `read_csv` que viene con Pandas. Como argumento le tenemos que pasar el nombre del CSV, en este caso: `car-data.csv`. Como a Pandas lo importamos con el nombre `pd` tenemos que usar la siguiente sintaxis: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "colab_type": "code",
    "id": "0oVZnezwQ159",
    "outputId": "f1e0fe18-8fa0-482a-e2b9-2ecd87d97d9d"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"car-data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si Python no nos tiró ningún error, entonces ahora en la variable `df` tenemos un *dataframe* que tiene los datos que se leyeron del CSV.\n",
    "\n",
    "Para ver los primeros 5 registros usamos el método `head` (cabeza):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
       "0  BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
       "1  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "2  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "3  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "4  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "\n",
       "   Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
       "0               6.0            MANUAL  rear wheel drive              2.0   \n",
       "1               6.0            MANUAL  rear wheel drive              2.0   \n",
       "2               6.0            MANUAL  rear wheel drive              2.0   \n",
       "3               6.0            MANUAL  rear wheel drive              2.0   \n",
       "4               6.0            MANUAL  rear wheel drive              2.0   \n",
       "\n",
       "                         Market Category Vehicle Size Vehicle Style  \\\n",
       "0  Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
       "1                     Luxury,Performance      Compact   Convertible   \n",
       "2                Luxury,High-Performance      Compact         Coupe   \n",
       "3                     Luxury,Performance      Compact         Coupe   \n",
       "4                                 Luxury      Compact   Convertible   \n",
       "\n",
       "   highway MPG  city mpg  Popularity   MSRP  \n",
       "0           26        19        3916  46135  \n",
       "1           28        19        3916  40650  \n",
       "2           28        20        3916  36350  \n",
       "3           28        18        3916  29450  \n",
       "4           28        18        3916  34500  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos ver los últimos 5 registros, usamos `tail` (cola):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "colab_type": "code",
    "id": "Fm-9dzdTRKpe",
    "outputId": "7892eaf7-0605-4b92-e139-cf0553041e51"
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11909</th>\n",
       "      <td>Acura</td>\n",
       "      <td>ZDX</td>\n",
       "      <td>2012</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>all wheel drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Crossover,Hatchback,Luxury</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>4dr Hatchback</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>204</td>\n",
       "      <td>46120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11910</th>\n",
       "      <td>Acura</td>\n",
       "      <td>ZDX</td>\n",
       "      <td>2012</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>all wheel drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Crossover,Hatchback,Luxury</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>4dr Hatchback</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>204</td>\n",
       "      <td>56670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11911</th>\n",
       "      <td>Acura</td>\n",
       "      <td>ZDX</td>\n",
       "      <td>2012</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>all wheel drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Crossover,Hatchback,Luxury</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>4dr Hatchback</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>204</td>\n",
       "      <td>50620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11912</th>\n",
       "      <td>Acura</td>\n",
       "      <td>ZDX</td>\n",
       "      <td>2013</td>\n",
       "      <td>premium unleaded (recommended)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>all wheel drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Crossover,Hatchback,Luxury</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>4dr Hatchback</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>204</td>\n",
       "      <td>50920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11913</th>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Zephyr</td>\n",
       "      <td>2006</td>\n",
       "      <td>regular unleaded</td>\n",
       "      <td>221.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>front wheel drive</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Midsize</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "      <td>28995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Make   Model  Year                Engine Fuel Type  Engine HP  \\\n",
       "11909    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
       "11910    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
       "11911    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
       "11912    Acura     ZDX  2013  premium unleaded (recommended)      300.0   \n",
       "11913  Lincoln  Zephyr  2006                regular unleaded      221.0   \n",
       "\n",
       "       Engine Cylinders Transmission Type      Driven_Wheels  Number of Doors  \\\n",
       "11909               6.0         AUTOMATIC    all wheel drive              4.0   \n",
       "11910               6.0         AUTOMATIC    all wheel drive              4.0   \n",
       "11911               6.0         AUTOMATIC    all wheel drive              4.0   \n",
       "11912               6.0         AUTOMATIC    all wheel drive              4.0   \n",
       "11913               6.0         AUTOMATIC  front wheel drive              4.0   \n",
       "\n",
       "                  Market Category Vehicle Size  Vehicle Style  highway MPG  \\\n",
       "11909  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
       "11910  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
       "11911  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
       "11912  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
       "11913                      Luxury      Midsize          Sedan           26   \n",
       "\n",
       "       city mpg  Popularity   MSRP  \n",
       "11909        16         204  46120  \n",
       "11910        16         204  56670  \n",
       "11911        16         204  50620  \n",
       "11912        16         204  50920  \n",
       "11913        17          61  28995  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y también podemos usar la función `len` para que nos diga cuántos registros tiene este *dataframe*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11914"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O también podemos usar el atributo `shape` del *dataframe* que nos va a devolver una tupla con la cantidad de registros y la cantidad de columnas por registro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11914, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y por último podemos usar el atributo `describe` del *dataframe* para conocer los estadísticos descriptivos básicos del dataframe como ser:\n",
    "\n",
    "* count: Cantidad de valores no nulos\n",
    "* mean: Media\n",
    "* std: Desviación estandar\n",
    "* min/25%/50%/75%/max: Valores mínimo, de percentiles 25, 50 y 75 y máximo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11845.00000</td>\n",
       "      <td>11884.000000</td>\n",
       "      <td>11908.000000</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>1.191400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2010.384338</td>\n",
       "      <td>249.38607</td>\n",
       "      <td>5.628829</td>\n",
       "      <td>3.436093</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>4.059474e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.579740</td>\n",
       "      <td>109.19187</td>\n",
       "      <td>1.780559</td>\n",
       "      <td>0.881315</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>6.010910e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.000000</td>\n",
       "      <td>55.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>170.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>2.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>227.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>2.999500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>300.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4.223125e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1001.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>2.065902e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year    Engine HP  Engine Cylinders  Number of Doors  \\\n",
       "count  11914.000000  11845.00000      11884.000000     11908.000000   \n",
       "mean    2010.384338    249.38607          5.628829         3.436093   \n",
       "std        7.579740    109.19187          1.780559         0.881315   \n",
       "min     1990.000000     55.00000          0.000000         2.000000   \n",
       "25%     2007.000000    170.00000          4.000000         2.000000   \n",
       "50%     2015.000000    227.00000          6.000000         4.000000   \n",
       "75%     2016.000000    300.00000          6.000000         4.000000   \n",
       "max     2017.000000   1001.00000         16.000000         4.000000   \n",
       "\n",
       "        highway MPG      city mpg    Popularity          MSRP  \n",
       "count  11914.000000  11914.000000  11914.000000  1.191400e+04  \n",
       "mean      26.637485     19.733255   1554.911197  4.059474e+04  \n",
       "std        8.863001      8.987798   1441.855347  6.010910e+04  \n",
       "min       12.000000      7.000000      2.000000  2.000000e+03  \n",
       "25%       22.000000     16.000000    549.000000  2.100000e+04  \n",
       "50%       26.000000     18.000000   1385.000000  2.999500e+04  \n",
       "75%       30.000000     22.000000   2009.000000  4.223125e+04  \n",
       "max      354.000000    137.000000   5657.000000  2.065902e+06  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vAmC369yTpMF"
   },
   "source": [
    "## 3. Revisando los tipos de datos de cada columna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9ESKxikIzA1d"
   },
   "source": [
    "Este paso es importante porque muchas veces por ejemplo, el precio o algún otro dato puede venir como una cadena de texto cuando en realidad queremos que sea un número de algún tipo. Si éste fuera el caso, tenemos que hacer la transformación del tipo de dato en la columna. \n",
    "\n",
    "En este caso en particular, por suerte, los datos vinieron correctamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "qPbKQ0noRptD",
    "outputId": "6a5aea47-ad0c-4118-8471-d91b6432b339"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                  object\n",
       "Model                 object\n",
       "Year                   int64\n",
       "Engine Fuel Type      object\n",
       "Engine HP            float64\n",
       "Engine Cylinders     float64\n",
       "Transmission Type     object\n",
       "Driven_Wheels         object\n",
       "Number of Doors      float64\n",
       "Market Category       object\n",
       "Vehicle Size          object\n",
       "Vehicle Style         object\n",
       "highway MPG            int64\n",
       "city mpg               int64\n",
       "Popularity             int64\n",
       "MSRP                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QoHuBhXxT5E9"
   },
   "source": [
    "## 4. Eliminando columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_3cy877Mze4H"
   },
   "source": [
    "Este paso es necesario en cada análisis exploratorio que vayas a realizar para evitar tener columnas en tu *dataframe* que no vayan a aportarte información en tu análisis. \n",
    "\n",
    "En este caso, a modo de ejemplo, podemos eliminar las columnas: Engine Fuel Type, Market Category, Vehicle style, Popularity, Number of doors, Vehicle Size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "uvSkK8swTr9H",
    "outputId": "1734f538-a4a1-45b9-d656-7ddc0124dc35"
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year  Engine HP  Engine Cylinders Transmission Type  \\\n",
       "0  BMW  1 Series M  2011      335.0               6.0            MANUAL   \n",
       "1  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
       "2  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
       "3  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
       "4  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
       "\n",
       "      Driven_Wheels  highway MPG  city mpg   MSRP  \n",
       "0  rear wheel drive           26        19  46135  \n",
       "1  rear wheel drive           28        19  40650  \n",
       "2  rear wheel drive           28        20  36350  \n",
       "3  rear wheel drive           28        18  29450  \n",
       "4  rear wheel drive           28        18  34500  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Engine Fuel Type', 'Market Category', 'Vehicle Style', 'Popularity', 'Number of Doors', 'Vehicle Size'], axis=1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "caAownWdUZso"
   },
   "source": [
    "## 5. Renombrando las columnas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UABLiEcyz-2G"
   },
   "source": [
    "En esta etapa lo que hacemos es ponerle un nombre que nos resulte más descriptivo. Muchas veces los datos vienen con nombres en las columnas un tanto confusos y siempre es mejor \"explícito\". Es un buen ejercicio que **mejora la legibilidad**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "1wHW41whURub",
    "outputId": "dec9b1b3-e344-4b33-92fd-6e9f4c03c878"
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>HP</th>\n",
       "      <th>Cilindros</th>\n",
       "      <th>Transmision</th>\n",
       "      <th>Tracción</th>\n",
       "      <th>MPG-H</th>\n",
       "      <th>MPG-C</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year     HP  Cilindros Transmision          Tracción  \\\n",
       "0  BMW  1 Series M  2011  335.0        6.0      MANUAL  rear wheel drive   \n",
       "1  BMW    1 Series  2011  300.0        6.0      MANUAL  rear wheel drive   \n",
       "2  BMW    1 Series  2011  300.0        6.0      MANUAL  rear wheel drive   \n",
       "3  BMW    1 Series  2011  230.0        6.0      MANUAL  rear wheel drive   \n",
       "4  BMW    1 Series  2011  230.0        6.0      MANUAL  rear wheel drive   \n",
       "\n",
       "   MPG-H  MPG-C  Precio  \n",
       "0     26     19   46135  \n",
       "1     28     19   40650  \n",
       "2     28     20   36350  \n",
       "3     28     18   29450  \n",
       "4     28     18   34500  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={\"Engine HP\": \"HP\", \"Engine Cylinders\": \"Cilindros\", \"Transmission Type\": \"Transmision\", \"Driven_Wheels\": \"Tracción\",\"highway MPG\": \"MPG-H\", \"city mpg\": \"MPG-C\", \"MSRP\": \"Precio\" })\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uiW7x_O4WIDX"
   },
   "source": [
    "## 6. Eliminando registros duplicados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LpR5NW70hXm"
   },
   "source": [
    "Es altamente probable que en un dataset de +10k registros algunos de ellos se encuentren duplicados. Tener registros duplicados en nuestro análisis puede alterar los resultados y por ende, nuestras conclusiones y el modelo que proponemos.\n",
    "\n",
    "Con `shape` vemos que actualmente el *dataframe* tiene 11914 registros con 10 columas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "1nBN_BCDWSmv",
    "outputId": "9a070a7d-a4d4-45c7-cac8-acb2c4db0e72"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11914, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminar los duplicados con un *dataframe* de Pandas es tan complejo como llamar al método `drop_duplicates`. Este médoto nos devuelve un *nuevo dataframe*, si no queremos tener un nuevo *dataframe* lo que hacemos es simplemente darle el mismo nombre al nuevo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10925, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "yB8t6o0wH7If",
    "outputId": "d4778cd1-5372-4e27-db62-20635493786e"
   },
   "source": [
    "Ahora el *dataframe* tiene 10925 registros, es decir que había 989 registros duplicados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "989"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11914 - 10925"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JkXUQtyQW3Dy"
   },
   "source": [
    "## 7. Eliminando registros con valores incompletos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los *dataframes* de Pandas tienen un método `count` que nos informa por columna cuántos registros tienen valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "kuobmetTV820",
    "outputId": "c6d6ce5c-6a38-4cd2-ee99-151124a1f84d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make           10925\n",
       "Model          10925\n",
       "Year           10925\n",
       "HP             10856\n",
       "Cilindros      10895\n",
       "Transmision    10925\n",
       "Tracción       10925\n",
       "MPG-H          10925\n",
       "MPG-C          10925\n",
       "Precio         10925\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K5pKvJi41YCp"
   },
   "source": [
    "Este paso es muy similar al anterior, sólo que esta vez vamos a eliminar los registros que no tengan valores.\n",
    "Es común en vez de eliminar estos registros, reemplazar los valores faltantes por el valor promedio. En este ejemplo, los valores faltantes están en el orden de las decenas mientras que tenemos un total de 10k registros. Es decir, el porcentaje es muy bajo y por eso optamos por descartar esos valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10925 - 10856"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10925 - 10895"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notemos que en la columna `HP` tenemos 10856 registros con datos sobre un total de 10925. Es decir, hay 69 registros que no tienen un valor.\n",
    "Y en la columna `Cilindros` tenemos 10895 registros con datos. Es decir, hay 30 registros que no tienen un valor. \n",
    "\n",
    "También podemos comprobarlo preguntándole al *dataframe* que nos diga si hay datos vacíos o en *null* y pidiéndole que nos haga la suma de esos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "Tzdlg-1OWjMz",
    "outputId": "7375ab6e-1473-4346-e5b5-0c61189cc716"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make            0\n",
       "Model           0\n",
       "Year            0\n",
       "HP             69\n",
       "Cilindros      30\n",
       "Transmision     0\n",
       "Tracción        0\n",
       "MPG-H           0\n",
       "MPG-C           0\n",
       "Precio          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iWJqTVxTJQnO"
   },
   "source": [
    "Para borrar esos registros que no tienen datos, registros que tienen \"datos no disponibles\" (del inglés *not available*) usamos el método `dropna`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "KbbV0xHPWoad",
    "outputId": "17dda8ec-1282-4814-de79-8f5e1aff3a5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make           10827\n",
       "Model          10827\n",
       "Year           10827\n",
       "HP             10827\n",
       "Cilindros      10827\n",
       "Transmision    10827\n",
       "Tracción       10827\n",
       "MPG-H          10827\n",
       "MPG-C          10827\n",
       "Precio         10827\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2t7L9l2mJSoX"
   },
   "source": [
    "Ahora nuestro dataset sabemos que no tiene registros duplicados y que todos nuestros registros tienen valores en las 10 columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "V-DmX1O4Wtox",
    "outputId": "2d50fc20-3535-413b-e317-75a7f94fa2a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make           0\n",
       "Model          0\n",
       "Year           0\n",
       "HP             0\n",
       "Cilindros      0\n",
       "Transmision    0\n",
       "Tracción       0\n",
       "MPG-H          0\n",
       "MPG-C          0\n",
       "Precio         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bk8RAHqQJVJK"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Py3sQc_ZxyU"
   },
   "source": [
    "## 8. Aplicando transformaciones\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El origen de estos datos utiliza el sistema inglés. Por eso tenemos MPG, millas por galón. No sé ustedes, pero yo prefiero utilizar el sistema internacional. Sabiendo que 1 MPG equivale a 0.425144 KPL (Kilómetros por litro) podemos transformar esas columnas para hacer una mejor interpretación.\n",
    "\n",
    "Si llamamos al *dataframe* con el nombre de una columna podemos operar con esa columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        26\n",
       "1        28\n",
       "2        28\n",
       "3        28\n",
       "4        28\n",
       "         ..\n",
       "11909    23\n",
       "11910    23\n",
       "11911    23\n",
       "11912    23\n",
       "11913    26\n",
       "Name: MPG-H, Length: 10827, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MPG-H\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MPG-H\"] = df[\"MPG-H\"] * 0.425144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        11.053744\n",
       "1        11.904032\n",
       "2        11.904032\n",
       "3        11.904032\n",
       "4        11.904032\n",
       "           ...    \n",
       "11909     9.778312\n",
       "11910     9.778312\n",
       "11911     9.778312\n",
       "11912     9.778312\n",
       "11913    11.053744\n",
       "Name: MPG-H, Length: 10827, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MPG-H\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo mismo hacemos para la columna `MPG-C`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        8.077736\n",
       "1        8.077736\n",
       "2        8.502880\n",
       "3        7.652592\n",
       "4        7.652592\n",
       "           ...   \n",
       "11909    6.802304\n",
       "11910    6.802304\n",
       "11911    6.802304\n",
       "11912    6.802304\n",
       "11913    7.227448\n",
       "Name: MPG-C, Length: 10827, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MPG-C\"] = df[\"MPG-C\"] * 0.425144\n",
    "df[\"MPG-C\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ahora deberíamos cambiar el nombre, porque ese nombre ya no es significativo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>HP</th>\n",
       "      <th>Cilindros</th>\n",
       "      <th>Transmision</th>\n",
       "      <th>Tracción</th>\n",
       "      <th>KPL-R</th>\n",
       "      <th>KPL-C</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>11.053744</td>\n",
       "      <td>8.077736</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>11.904032</td>\n",
       "      <td>8.077736</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>11.904032</td>\n",
       "      <td>8.502880</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>11.904032</td>\n",
       "      <td>7.652592</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>11.904032</td>\n",
       "      <td>7.652592</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year     HP  Cilindros Transmision          Tracción  \\\n",
       "0  BMW  1 Series M  2011  335.0        6.0      MANUAL  rear wheel drive   \n",
       "1  BMW    1 Series  2011  300.0        6.0      MANUAL  rear wheel drive   \n",
       "2  BMW    1 Series  2011  300.0        6.0      MANUAL  rear wheel drive   \n",
       "3  BMW    1 Series  2011  230.0        6.0      MANUAL  rear wheel drive   \n",
       "4  BMW    1 Series  2011  230.0        6.0      MANUAL  rear wheel drive   \n",
       "\n",
       "       KPL-R     KPL-C  Precio  \n",
       "0  11.053744  8.077736   46135  \n",
       "1  11.904032  8.077736   40650  \n",
       "2  11.904032  8.502880   36350  \n",
       "3  11.904032  7.652592   29450  \n",
       "4  11.904032  7.652592   34500  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={\"MPG-H\": \"KPL-R\", \"MPG-C\": \"KPL-C\"})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Py3sQc_ZxyU"
   },
   "source": [
    "## 9. Detectando valores atípicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1g6CJPofZzHN"
   },
   "source": [
    "Un valor atípico es un punto o un set de puntos que son diferentes de otros en el dataset. A veces puede ser valores que están muy por debajo o muy por encima. En general suele ser una buena medida quitar estos valores que no resultan representativos. Estos valores atípicos suelen generar modelos que no resultan representativos. Es por ello que es una buena alternativa removerlos del dataset.  \n",
    "\n",
    "Abajo vemos cómo con Boxplot (o diagrama de cajas) podemos observar los valores atípicos rápidamente. \n",
    "\n",
    "Si quieren más info sobre cómo detectar valores atípicos pueden ver el siguiente artículo de [towards data science](https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b1e950>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NMJEktWaYSJJaM0wkSa0ZJpKk1gwTSVJrhokkqTXDRJLUmmEiSWrNMJEktWaYSJJaM0wk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\na4aJJKm1/wfLBFaTY5VPfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos por separado para evitar el sesgo de Precio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "vtxX_y6zZ2ri",
    "outputId": "e3f93522-9244-4c32-c34c-103e0834e93b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16d86d90>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEKCAYAAAAl5S8KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Precio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "9HtvQYVHZ8u5",
    "outputId": "3dc30a01-6fb1-41d9-dec8-0ceeead6a358"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c8c6f0>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEKCAYAAAAl5S8KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADWBJREFUeJzt3X9snPV9wPH3hxwlJKYLIRliTjMX\nuZSioaXFWmHbH2hLOsYP759MSwUibIgq0uR50SQ0IFJixD8TZTTLpqqwdUhT1E7rqo1ECISg/Mtk\na6FNSQq31aXJ2pJCYXUSbXX63R/32DsbO7Gds+9j3/slWbl77uvn131567nHTohSCpKk9ruk3Tsg\nSWowyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJSsIgS1ISBlmSkqjNZ/CGDRtKT0/PIu2KJK1MIyMj\nPy6lbLzQuHkFuaenh+Hh4YXvlSR1oIj43lzGectCkpIwyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJ\nSsIgS1ISBlmSkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElKwiBLUhIGWZKSMMiSlIRBlqQk5vX/\n1FtpDhw4QL1eb+k6T548CUB3d3dL19vb28vAwEBL1ykpl44Ocr1e58jRY5xbs75l61x15n0Afvg/\nrTu1q86827J1Scqro4MMcG7Nes5ef3vL1nf58ecAFmWdklY27yFLUhIGWZKSMMiSlIRBlqQkDLIk\nJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJSsIgS1ISBlmS\nkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElKwiBLUhIGWZKSMMiSlIRBlqQkDLIkJWGQJSkJgyxJ\nSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJSsIgS1ISSxLkAwcOcODAgaXY\nlFYI54w6UW0pNlKv15diM1pBnDPqRN6ykKQkDLIkJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKU\nhEGWpCQMsiQlYZAlKQmDLElJGGRJSsIgS1ISBlmSkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElK\nwiBLUhIGWZKSMMiSlIRBlqQkDLIkJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQl\nYZAlKQmDLElJGGRJSsIgS1IStXbvgHQ+99xzDydOnLjguHXr1vHee++xZs0azpw5c1Hb3LVrF9u2\nbeOhhx7ijTfeAGDz5s289dZbc/r+LVu2cOTIETZu3MipU6cmlz/xxBMcP36cp59+enIbQ0ND7N27\nl6uuumrGdR08eHBy/Llz5yYf79ixg5dffplHH32UvXv3csUVV/Dggw/y+OOPc9NNN01ZR71eZ3Bw\nkP3799Pb27vAs9K5hoeHZz23rRallDkP7uvrK8PDw/PeyODgIAD79++f9/cupsHBQUb+80ecvf72\nlq3z8uPPAbR8nTdde3W687eYJubMa6+91pbt9/f38+yzz7Z0nV1dXYyNjU3ZxqFDh+jv72f37t0z\nfs+tt9464/JXXnmFrVu3Mj4+Tq1WY/Xq1YyNjdHV1cXhw4enjL3vvvsYHR2lp6eHZ555plWH0zHu\nvPPOWc/tXEXESCml70LjvGWhtF5//fW2bbvVMQamxBjg0KFDlFJ4/vnneeeddz4w/uDBg7Ou67HH\nHmN8fByA8fHxyXWPjY0xMjIyOa5erzM6OgrA6Ogo9Xr9Yg+jowwPD896bhfDklwhb9++nbNnz6b7\nuFSv1/np/xZOb9nRsnUuxhXy2iNf5YoPRbrzt5jq9TqnT59u924siVqtxh133PGBq+TZro4vpPlK\nbuLqeIJXyfMzcXU8YaFXyS27Qo6Iz0XEcEQMN98Pk9Qa4+PjvPjiiy1bX3NAmmM803Od3/RPNdOf\nt9oFf6hXSnkKeAoaV8gL2Uh3dzeQ9x5ydj9f/WF6O/AecrvuHy+1Wq3Gtm3bWra+rq6uycc9PT0f\nuELW3E2/7998bheD95CV1qWXXtruXVhUEQHAqlWruPfeez/w+gMPPDDr927dunXW14aGhiYf79mz\nZ8pr05/r/Pbt2zflefO5XQwGWWndcMMNbdt2f39/y9c5/erqrrvuIiK47bbbZvy1t7vvvnvWde3Z\ns4darfEBt1arTa67q6tryq9m9fb2Tl4V9/T0dNTPIVqhr69v1nO7GAyyUtu0adOcxq1btw6ANWvW\nXPQ2d+3axc6dO7nuuusml23evHnO379lyxYANm7cOGX50NDQ5FXvxDZuvPHGGa+OJzSPb34M8PDD\nDwPwyCOPsG/fPi655JIZr+D27NnD2rVrvTpeoPOd21bz95D9PeSUss4ZaSH8PWRJWmYMsiQlYZAl\nKQmDLElJGGRJSsIgS1ISBlmSkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElKwiBLUhIGWZKSMMiS\nlIRBlqQkDLIkJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJ\nSsIgS1ISBlmSkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElKwiBLUhK1pdhIb2/vUmxGK4hzRp1o\nSYI8MDCwFJvRCuKcUSfyloUkJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQlYZAl\nKQmDLElJGGRJSsIgS1ISBlmSkjDIkpSEQZakJAyyJCVhkCUpCYMsSUkYZElKwiBLUhIGWZKSMMiS\nlIRBlqQkDLIkJWGQJSkJgyxJSRhkSUrCIEtSEgZZkpIwyJKUhEGWpCQMsiQlYZAlKQmDLElJGGRJ\nSsIgS1ISBlmSkjDIkpSEQZakJGrt3oF2W3XmXS4//lwL1/cOQIvX+S5wdcvWJymnjg5yb29vy9d5\n8uQ4AN3drQzo1Yuyr5Jy6eggDwwMtHsXJGmS95AlKQmDLElJGGRJSsIgS1ISBlmSkjDIkpSEQZak\nJAyyJCVhkCUpCYMsSUkYZElKwiBLUhIGWZKSMMiSlIRBlqQkDLIkJWGQJSkJgyxJSRhkSUrCIEtS\nElFKmfvgiFPA9xZvd9LaAPy43TvRRh5/Zx8/eA4u9vh/uZSy8UKD5hXkThURw6WUvnbvR7t4/J19\n/OA5WKrj95aFJCVhkCUpCYM8N0+1ewfazONXp5+DJTl+7yFLUhJeIUtSEh0f5Ij4SER8IyKORcS3\nI2KwWr4+Il6MiDerP6+slkdE/FVE1CPimxHxqfYeQWtExKqI+PeIOFw9/2hEvFod/z9GxIeq5ZdV\nz+vV6z3t3O9WiYh1EfG1iDhezYVbOmkORMTuav4fjYivRMTqlT4HIuLLEfF2RBxtWjbv9zwidlbj\n34yInRezTx0fZGAc+LNSyieAm4E/jogbgD8HXiqlfAx4qXoO8LvAx6qvzwFfXPpdXhSDwLGm538B\nPFkd/0+A+6vl9wM/KaX0Ak9W41aC/cDzpZTrgV+lcS46Yg5ERDfwJ0BfKeVXgFXADlb+HHgGuG3a\nsnm95xGxHtgLfBr4NWDvRMQXpJTiV9MX8K/ANuA7wDXVsmuA71SPvwR8tmn85Ljl+gVsqibfbwGH\ngaDxS/C16vVbgBeqxy8At1SPa9W4aPcxXOTxfxj47vTj6JQ5AHQD3wfWV+/pYeB3OmEOAD3A0YW+\n58BngS81LZ8ybr5fXiE3qT56fRJ4Fbi6lPIDgOrPX6yGTUzeCSeqZcvZF4AHgZ9Xz68C3iuljFfP\nm49x8vir19+vxi9n1wKngL+vbtv8bUSspUPmQCnlJPB54C3gBzTe0xE6aw5MmO973tK5YJArEdEF\n/DPwp6WU/z7f0BmWLdtfVYmIO4G3SykjzYtnGFrm8NpyVQM+BXyxlPJJ4DT//1F1JivqHFQfsX8P\n+CjwS8BaGh/Rp1vJc+BCZjvmlp4LgwxExKU0YnywlPL1avGPIuKa6vVrgLer5SeAjzR9+ybgv5Zq\nXxfBbwD9ETEKfJXGbYsvAOsiolaNaT7GyeOvXv8F4N2l3OFFcAI4UUp5tXr+NRqB7pQ5sBX4binl\nVCnlZ8DXgV+ns+bAhPm+5y2dCx0f5IgI4O+AY6WUv2x66Vlg4iemO2ncW55Yfm/1U9ebgfcnPuIs\nR6WUh0opm0opPTR+kPNyKeVu4BvA9mrY9OOfOC/bq/HL+uqolPJD4PsR8fFq0W8Dr9Mhc4DGrYqb\nI2JN9d/DxPF3zBxoMt/3/AXgMxFxZfVJ4zPVsoVp9031dn8Bv0njI8Y3gSPV1+007om9BLxZ/bm+\nGh/A3wD/AXyLxk+m234cLToXtwKHq8fXAv8G1IF/Ai6rlq+unter169t93636Ni3AMPVPPgX4MpO\nmgPAEHAcOAr8A3DZSp8DwFdo3DP/GY0r3fsX8p4Df1SdizrwhxezT/5NPUlKouNvWUhSFgZZkpIw\nyJKUhEGWpCQMsiQlYZC1rETE2LTn90XEX1eP90XEyYg4Uv2rZf3t2UtpYQyyVponSylbgN8HvhwR\nznEtG05WrUillGM0/mnVDe3eF2muahceIqVyeUQcaXq+nsZfa50iIj5N41+vO7VUOyZdLIOs5eZs\ndUsCaNxDBvqaXt8dEfcAPwX+oPhXUbWMGGStNE+WUj7f7p2QFsJ7yJKUhEGWpCT8194kKQmvkCUp\nCYMsSUkYZElKwiBLUhIGWZKSMMiSlIRBlqQkDLIkJfF/8mszsUNDm64AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['HP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a89390>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEKCAYAAAAl5S8KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Cilindros'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kCv110_cJiDz"
   },
   "source": [
    "Analicemos la disperción del precio que como se ve, tiene valores outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.082700e+04\n",
       "mean     4.249325e+04\n",
       "std      6.229451e+04\n",
       "min      2.000000e+03\n",
       "25%      2.197250e+04\n",
       "50%      3.084500e+04\n",
       "75%      4.330000e+04\n",
       "max      2.065902e+06\n",
       "Name: Precio, dtype: float64"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vNNZdq5njVQnMLCdKnSpERFnStcD9QBG4PSK2SboR2BIRG4DbgDsljZL1Plan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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Precio'])\n",
    "df['Precio'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que Precio tiene una larga cola hacia la derecha.\n",
    "\n",
    "Analicemos los deciles y algunos percentiles para entender esos valores anómalos. Para esto, usaremos el método quantile para calcular los percentiles.\n",
    "\n",
    "* Los cuartiles, que dividen a la distribución en cuatro partes (corresponden a los cuantiles 0,25; 0,50 y 0,75);\n",
    "* Los quintiles, que dividen a la distribución en cinco partes (corresponden a los cuantiles 0,20; 0,40; 0,60 y 0,80);\n",
    "* Los deciles, que dividen a la distribución en diez partes;\n",
    "* Los percentiles, que dividen a la distribución en cien partes.\n",
    "* En el método Quantile: 0, 0.5 y 1 equivalen a valores mínimo, mediana y máximo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00       2000.00\n",
       "0.10       3455.80\n",
       "0.20      19796.00\n",
       "0.30      23800.00\n",
       "0.40      27095.00\n",
       "0.50      30845.00\n",
       "0.60      34888.00\n",
       "0.70      39995.00\n",
       "0.80      47658.00\n",
       "0.90      67208.00\n",
       "0.97     185098.00\n",
       "0.98     221990.00\n",
       "0.99     291859.44\n",
       "1.00    2065902.00\n",
       "Name: Precio, dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GQIZL8b4Kvcp/+0mS3idpOIRwpaQzkvol9YT8DZ1GJH05fl34QUl9IYT3S+qR9N8Zi94p\n6S8hhCskfVXScNU3BihTYuEqQE29xcwOxOk/KH/vgndKejGE8Hgsv0b5G5P/Mf5Dh5WS/qx8aL8U\nQnhKkkK8E1isU3CdpI/H139rZi1m9rYQwitV3SqgDAQyvPlvCOEDpQUxUM+UFknaG0K4aUa9K9RY\nt9/EeYZTFliOHpf0YTNLSZKZXWhm75X0N0nvNLMPxvLVJbecLPi9pE/G1zdKOh3mv6cuUDMcIWPZ\nCSGcMrNPSXrAzJpjcX8I4e9m9glJg2b2FuXPH/fMmP0OST80s2ckva43bqkI1B0fewMAJzhlAQBO\nEMgA4ASBDABOEMgA4ASBDABOEMgA4ASBDABOEMgA4MT/AZkPHXqJzce1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Precio'])\n",
    "df['Precio'].quantile([0, .1, .2, .3, .4, .5, .6, .7, .8, .9, .97, .98, .99, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recortemos los outliers... por ejemplo, recortando entre 200 y el cuartil del 90% (67208 como vimos en la celda anterior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8e08d0d61971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcorte_inferior\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcorte_superior\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Precio\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Precio\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mcorte_superior\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Precio\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mcorte_inferior\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "corte_inferior = 200\n",
    "corte_superior = df[\"Precio\"].quantile(0.9)\n",
    "\n",
    "df = df[(df[\"Precio\"] < corte_superior) & (df[\"Precio\"] > corte_inferior)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos cómo quedó la distribución después de eliminar los outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a4c050>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BMeZgrDFcCiEcCSEMhhCGJP2jkrcVKf856ZP0e2aWGLF82Lbi978k6ZOzfzSTY2YzlYzx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\nJd1bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Precio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a21950>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sBXZ2RtgM4yspN126hIOnRvjm8/uBoBustjrMlSuaC1nNorh0aQNrFtXxP391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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Precio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Cómo definimos el corte inferior? Mejor preguntemosle al área de negocio que nos pasó este set de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WqFPRda8eEp_"
   },
   "source": [
    "## 10. Gráficas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z-W6Q9-hJosZ"
   },
   "source": [
    "### Histograma\n",
    "\n",
    "El histograma es una gráfica que representa la cantidad de ocurrencias de una variable en un intervalo determinado. En este dataset tenemos principalmente 10 diferentes marcas de fabricantes, pero es importante saber saber quién tiene la mayor cantidad de autos. Este histograma es una solución trivial para saber cuántos autos fabrica cada compañía."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431
    },
    "colab_type": "code",
    "id": "dAnd4DSyeHDb",
    "outputId": "44b04e39-9dc7-40fc-9ddb-b7182f4f6e1f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmSJEka0JSO4RQR84CnAz8FNsnMa6AkpSLiEXWzzYCr+t62pK67Zng1lSRJ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\n+68GPgc8kRCJnwXcUqtUpQSatiKCTWsQbnhH2v5Nhbavcym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KHCILqHMu0AjFJ8li061V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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Make\"].value_counts().plot(kind='bar', figsize=(20,5))\n",
    "plt.title(\"Cantidad de autos por marca\")\n",
    "plt.ylabel('Cantidad de autos')\n",
    "plt.xlabel('Marca');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c37WtYYWJuAQ"
   },
   "source": [
    "### Mapas de calor\n",
    "\n",
    "Los mapas de calor son gráficas que sirven para determinar variables dependientes. Una de las mejores maneras para encontrar estas relaciones es utilizando un mapa de calor. En el siguiente mapa de calor encontramos que el precio es una característica dependiente del tamaño del motor, de la potencia y de la cantidad de cilindros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "id": "yhHfLVTj6nmy",
    "outputId": "50a65ae0-841a-42ec-87e6-1a99da1ea57b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>HP</th>\n",
       "      <th>Cilindros</th>\n",
       "      <th>KPL-R</th>\n",
       "      <th>KPL-C</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341624</td>\n",
       "      <td>-0.061565</td>\n",
       "      <td>0.288027</td>\n",
       "      <td>0.238074</td>\n",
       "      <td>0.406290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HP</th>\n",
       "      <td>0.341624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.751681</td>\n",
       "      <td>-0.394449</td>\n",
       "      <td>-0.451526</td>\n",
       "      <td>0.782439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cilindros</th>\n",
       "      <td>-0.061565</td>\n",
       "      <td>0.751681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.613523</td>\n",
       "      <td>-0.633368</td>\n",
       "      <td>0.527209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPL-R</th>\n",
       "      <td>0.288027</td>\n",
       "      <td>-0.394449</td>\n",
       "      <td>-0.613523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836345</td>\n",
       "      <td>-0.174628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPL-C</th>\n",
       "      <td>0.238074</td>\n",
       "      <td>-0.451526</td>\n",
       "      <td>-0.633368</td>\n",
       "      <td>0.836345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.218243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precio</th>\n",
       "      <td>0.406290</td>\n",
       "      <td>0.782439</td>\n",
       "      <td>0.527209</td>\n",
       "      <td>-0.174628</td>\n",
       "      <td>-0.218243</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year        HP  Cilindros     KPL-R     KPL-C    Precio\n",
       "Year       1.000000  0.341624  -0.061565  0.288027  0.238074  0.406290\n",
       "HP         0.341624  1.000000   0.751681 -0.394449 -0.451526  0.782439\n",
       "Cilindros -0.061565  0.751681   1.000000 -0.613523 -0.633368  0.527209\n",
       "KPL-R      0.288027 -0.394449  -0.613523  1.000000  0.836345 -0.174628\n",
       "KPL-C      0.238074 -0.451526  -0.633368  0.836345  1.000000 -0.218243\n",
       "Precio     0.406290  0.782439   0.527209 -0.174628 -0.218243  1.000000"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n+MfVC/c1X2e7H7hP0jbltn2BK8sJxFZIurnumNkAkjYHtrB9W7n9MuDq8vUi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\nSQ4REdEgySEiIhokOURERIP/D+Nw6V28HoAaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlaciones= df.corr()\n",
    "sns.heatmap(correlaciones)\n",
    "correlaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1ROThOLQfRZw"
   },
   "source": [
    "### Dispersión\n",
    "\n",
    "Usamos este tipo de gráficas para encontrar correlación entre 2 variables. Acá graficamos precio en función de la potencia.\n",
    "We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between Horsepower and Price and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 401
    },
    "colab_type": "code",
    "id": "2aOfHNFefSrX",
    "outputId": "4b5286f0-5419-48bc-d2de-476aeb36f022"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G7YHnxjTIj+v+qTMxo0dEFJLJeC1T7W7xodPO5sFRcAtMvQpHrKzkrsOnW7ZD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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['HP'], df['Precio'])\n",
    "ax.set_xlabel('HP')\n",
    "ax.set_ylabel('Precio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con la gráfica anterior vemos que se puede trazar claramente una línea de tendencia. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hQXy8o_gKFS5"
   },
   "source": [
    "**Como vimos a lo largo de este ejercicio estos son algunos de los pasos que podemos realizar para un análisis exploratorio de datos. Hay mucho más cosas para hacer, pero por ahora esto consideramos que representa una muy buena introducción que brinda un panorama bastante amplio sobre la disciplina.**\n",
    "\n",
    "**Sigan en contacto para más detalles**\n",
    "\n",
    "## Gracias!\n",
    "\n",
    "# #DataSchool"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Exploratory data Analysis 1.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}