{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67912104-5713-4e43-963f-f4d502e18b83",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Estadística descriptiva\n",
    "Uso de Python (Pandas) para hacer estadística descriptiva"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15643e7-749c-4a25-a4c4-d3a7633d0d2b",
   "metadata": {},
   "source": [
    "## 0. Bibliotecas que se usarán"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "daf1ea1d-7db5-468d-a811-fcbcc7dd135a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                 # numpy\n",
    "import pandas as pd                # pandas\n",
    "import matplotlib.pyplot as plt    # matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a31868-6b8d-414f-8799-ab9be3c69692",
   "metadata": {},
   "source": [
    "## 1. Carga de datos y exploración inicial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ad4c28-da28-439f-b25b-2f5af24e0dbc",
   "metadata": {},
   "source": [
    "### 1.1 Carga de datos y visualización inicial ( _pandas.read_csv()_ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8e153a4-b780-48de-b55b-7f58b2f34090",
   "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>Marca</th>\n",
       "      <th>Combustible</th>\n",
       "      <th>Aire</th>\n",
       "      <th>Puertas</th>\n",
       "      <th>Cuerpo</th>\n",
       "      <th>Traccion</th>\n",
       "      <th>UbicacionMotor</th>\n",
       "      <th>Distancia entre ejes</th>\n",
       "      <th>Largo</th>\n",
       "      <th>Ancho</th>\n",
       "      <th>...</th>\n",
       "      <th>TamanoMotor</th>\n",
       "      <th>SistemaCombustible</th>\n",
       "      <th>DiametroCilindro</th>\n",
       "      <th>stroke</th>\n",
       "      <th>RadioCompresion</th>\n",
       "      <th>CaballosFuerza</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>KmCiudad</th>\n",
       "      <th>KmCarretera</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Marca Combustible   Aire  Puertas       Cuerpo Traccion  \\\n",
       "0    alfa-romero    gasolina    std        2  convertible      rwd   \n",
       "1    alfa-romero    gasolina    std        2  convertible      rwd   \n",
       "2    alfa-romero    gasolina    std        2    hatchback      rwd   \n",
       "3           audi    gasolina    std        4        sedan      fwd   \n",
       "4           audi    gasolina    std        4        sedan      4wd   \n",
       "..           ...         ...    ...      ...          ...      ...   \n",
       "200        volvo    gasolina    std        4        sedan      rwd   \n",
       "201        volvo    gasolina  turbo        4        sedan      rwd   \n",
       "202        volvo    gasolina    std        4        sedan      rwd   \n",
       "203        volvo      diesel  turbo        4        sedan      rwd   \n",
       "204        volvo    gasolina  turbo        4        sedan      rwd   \n",
       "\n",
       "    UbicacionMotor  Distancia entre ejes  Largo  Ancho  ...  TamanoMotor  \\\n",
       "0          frontal                  88.6  168.8   64.1  ...          130   \n",
       "1          frontal                  88.6  168.8   64.1  ...          130   \n",
       "2          frontal                  94.5  171.2   65.5  ...          152   \n",
       "3          frontal                  99.8  176.6   66.2  ...          109   \n",
       "4          frontal                  99.4  176.6   66.4  ...          136   \n",
       "..             ...                   ...    ...    ...  ...          ...   \n",
       "200        frontal                 109.1  188.8   68.9  ...          141   \n",
       "201        frontal                 109.1  188.8   68.8  ...          141   \n",
       "202        frontal                 109.1  188.8   68.9  ...          173   \n",
       "203        frontal                 109.1  188.8   68.9  ...          145   \n",
       "204        frontal                 109.1  188.8   68.9  ...          141   \n",
       "\n",
       "     SistemaCombustible DiametroCilindro  stroke  RadioCompresion  \\\n",
       "0                  mpfi             3.47    2.68              9.0   \n",
       "1                  mpfi             3.47    2.68              9.0   \n",
       "2                  mpfi             2.68    3.47              9.0   \n",
       "3                  mpfi             3.19     3.4             10.0   \n",
       "4                  mpfi             3.19     3.4              8.0   \n",
       "..                  ...              ...     ...              ...   \n",
       "200                mpfi             3.78    3.15              9.5   \n",
       "201                mpfi             3.78    3.15              8.7   \n",
       "202                mpfi             3.58    2.87              8.8   \n",
       "203                 idi             3.01     3.4             23.0   \n",
       "204                mpfi             3.78    3.15              9.5   \n",
       "\n",
       "    CaballosFuerza peak-rpm KmCiudad  KmCarretera Precio  \n",
       "0              111     5000       21           27  13495  \n",
       "1              111     5000       21           27  16500  \n",
       "2              154     5000       19           26  16500  \n",
       "3              102     5500       24           30  13950  \n",
       "4              115     5500       18           22  17450  \n",
       "..             ...      ...      ...          ...    ...  \n",
       "200            114     5400       23           28  16845  \n",
       "201            160     5300       19           25  19045  \n",
       "202            134     5500       18           23  21485  \n",
       "203            106     4800       26           27  22470  \n",
       "204            114     5400       19           25  22625  \n",
       "\n",
       "[205 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datosEntrada/automoviles.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa025f1-2921-41e2-8660-a508668805d5",
   "metadata": {},
   "source": [
    "### 1.2 Visualización de tipos de datos, nulos\n",
    "#### 1.2.1 Visualización de Tipos de datos con _dtypes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "134bc62a-ce24-4e7b-aa14-f4385f5a75ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marca                    object\n",
       "Combustible              object\n",
       "Aire                     object\n",
       "Puertas                   int64\n",
       "Cuerpo                   object\n",
       "Traccion                 object\n",
       "UbicacionMotor           object\n",
       "Distancia entre ejes    float64\n",
       "Largo                   float64\n",
       "Ancho                   float64\n",
       "Altura                  float64\n",
       "Peso                      int64\n",
       "TipoMotor                object\n",
       "Cilindros                 int64\n",
       "TamanoMotor               int64\n",
       "SistemaCombustible       object\n",
       "DiametroCilindro         object\n",
       "stroke                   object\n",
       "RadioCompresion         float64\n",
       "CaballosFuerza           object\n",
       "peak-rpm                 object\n",
       "KmCiudad                  int64\n",
       "KmCarretera               int64\n",
       "Precio                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622b4f7b-e32a-44cf-9b9a-8762df98194c",
   "metadata": {},
   "source": [
    "#### 1.2.2 Cambio de tipo de datos con _astype()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b49d40a-a36e-4473-9124-4c6e285b85fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marca                    object\n",
       "Combustible              object\n",
       "Aire                     object\n",
       "Puertas                 float64\n",
       "Cuerpo                   object\n",
       "Traccion                 object\n",
       "UbicacionMotor           object\n",
       "Distancia entre ejes    float64\n",
       "Largo                   float64\n",
       "Ancho                   float64\n",
       "Altura                  float64\n",
       "Peso                      int64\n",
       "TipoMotor                object\n",
       "Cilindros                 int64\n",
       "TamanoMotor               int64\n",
       "SistemaCombustible       object\n",
       "DiametroCilindro         object\n",
       "stroke                   object\n",
       "RadioCompresion         float64\n",
       "CaballosFuerza           object\n",
       "peak-rpm                 object\n",
       "KmCiudad                  int64\n",
       "KmCarretera               int64\n",
       "Precio                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.astype({\"Puertas\":\"float64\"})\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38147920-9bf9-4337-aff0-9832f739636c",
   "metadata": {},
   "source": [
    "#### 1.2.3 Número de valores no nulos por campo con _count()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd00ced6-a7d8-47a7-9734-cae51a6ebf36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marca                   205\n",
       "Combustible             205\n",
       "Aire                    205\n",
       "Puertas                 205\n",
       "Cuerpo                  205\n",
       "Traccion                205\n",
       "UbicacionMotor          205\n",
       "Distancia entre ejes    205\n",
       "Largo                   205\n",
       "Ancho                   205\n",
       "Altura                  205\n",
       "Peso                    205\n",
       "TipoMotor               205\n",
       "Cilindros               205\n",
       "TamanoMotor             205\n",
       "SistemaCombustible      205\n",
       "DiametroCilindro        205\n",
       "stroke                  205\n",
       "RadioCompresion         205\n",
       "CaballosFuerza          205\n",
       "peak-rpm                205\n",
       "KmCiudad                205\n",
       "KmCarretera             205\n",
       "Precio                  205\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff439474-d32b-4b4e-a72a-436f2ec66ad6",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.3 Descripción general\n",
    "#### 1.3.1 Uso de _describe()_ para estadísticos básicos por campo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c29d659-c8de-474a-89d7-9e18a0a91e47",
   "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>Puertas</th>\n",
       "      <th>Distancia entre ejes</th>\n",
       "      <th>Largo</th>\n",
       "      <th>Ancho</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Cilindros</th>\n",
       "      <th>TamanoMotor</th>\n",
       "      <th>RadioCompresion</th>\n",
       "      <th>KmCiudad</th>\n",
       "      <th>KmCarretera</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.131707</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>4.380488</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13173.819512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.993715</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>1.080854</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7873.884108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10345.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Puertas  Distancia entre ejes       Largo       Ancho      Altura  \\\n",
       "count  205.000000            205.000000  205.000000  205.000000  205.000000   \n",
       "mean     3.131707             98.756585  174.049268   65.907805   53.724878   \n",
       "std      0.993715              6.021776   12.337289    2.145204    2.443522   \n",
       "min      2.000000             86.600000  141.100000   60.300000   47.800000   \n",
       "25%      2.000000             94.500000  166.300000   64.100000   52.000000   \n",
       "50%      4.000000             97.000000  173.200000   65.500000   54.100000   \n",
       "75%      4.000000            102.400000  183.100000   66.900000   55.500000   \n",
       "max      4.000000            120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "              Peso   Cilindros  TamanoMotor  RadioCompresion    KmCiudad  \\\n",
       "count   205.000000  205.000000   205.000000       205.000000  205.000000   \n",
       "mean   2555.565854    4.380488   126.907317        10.142537   25.219512   \n",
       "std     520.680204    1.080854    41.642693         3.972040    6.542142   \n",
       "min    1488.000000    2.000000    61.000000         7.000000   13.000000   \n",
       "25%    2145.000000    4.000000    97.000000         8.600000   19.000000   \n",
       "50%    2414.000000    4.000000   120.000000         9.000000   24.000000   \n",
       "75%    2935.000000    4.000000   141.000000         9.400000   30.000000   \n",
       "max    4066.000000   12.000000   326.000000        23.000000   49.000000   \n",
       "\n",
       "       KmCarretera        Precio  \n",
       "count   205.000000    205.000000  \n",
       "mean     30.751220  13173.819512  \n",
       "std       6.886443   7873.884108  \n",
       "min      16.000000   5118.000000  \n",
       "25%      25.000000   7788.000000  \n",
       "50%      30.000000  10345.000000  \n",
       "75%      34.000000  16500.000000  \n",
       "max      54.000000  45400.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0451d1-9663-4089-a711-319650fa7848",
   "metadata": {},
   "source": [
    "## 2. Limpieza de datos\n",
    "### Nulos, Quitar columnas, Quitar registros, Cambiar valores, etc.\n",
    "#### 2.1 Eliminar columnas con _drop()_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f2bd68-6212-4f63-992b-df89def4bd8d",
   "metadata": {},
   "source": [
    "¿Cómo quitar columnas?: (columns= [cada una de los nombres de las columnas que quiero eliminar]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e75671ff-5d47-48b0-b304-2e936e6cadc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marca</th>\n",
       "      <th>Combustible</th>\n",
       "      <th>Aire</th>\n",
       "      <th>Puertas</th>\n",
       "      <th>Cuerpo</th>\n",
       "      <th>Traccion</th>\n",
       "      <th>UbicacionMotor</th>\n",
       "      <th>Distancia entre ejes</th>\n",
       "      <th>Largo</th>\n",
       "      <th>Ancho</th>\n",
       "      <th>...</th>\n",
       "      <th>Peso</th>\n",
       "      <th>TipoMotor</th>\n",
       "      <th>Cilindros</th>\n",
       "      <th>DiametroCilindro</th>\n",
       "      <th>stroke</th>\n",
       "      <th>CaballosFuerza</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>KmCiudad</th>\n",
       "      <th>KmCarretera</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2.0</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2.0</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>2.0</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>6</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2952</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3049</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>std</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>3012</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>6</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>3217</td>\n",
       "      <td>ohc</td>\n",
       "      <td>6</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>volvo</td>\n",
       "      <td>gasolina</td>\n",
       "      <td>turbo</td>\n",
       "      <td>4.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>frontal</td>\n",
       "      <td>109.1</td>\n",
       "      <td>188.8</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>3062</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Marca Combustible   Aire  Puertas       Cuerpo Traccion  \\\n",
       "0    alfa-romero    gasolina    std      2.0  convertible      rwd   \n",
       "1    alfa-romero    gasolina    std      2.0  convertible      rwd   \n",
       "2    alfa-romero    gasolina    std      2.0    hatchback      rwd   \n",
       "3           audi    gasolina    std      4.0        sedan      fwd   \n",
       "4           audi    gasolina    std      4.0        sedan      4wd   \n",
       "..           ...         ...    ...      ...          ...      ...   \n",
       "200        volvo    gasolina    std      4.0        sedan      rwd   \n",
       "201        volvo    gasolina  turbo      4.0        sedan      rwd   \n",
       "202        volvo    gasolina    std      4.0        sedan      rwd   \n",
       "203        volvo      diesel  turbo      4.0        sedan      rwd   \n",
       "204        volvo    gasolina  turbo      4.0        sedan      rwd   \n",
       "\n",
       "    UbicacionMotor  Distancia entre ejes  Largo  Ancho  ...  Peso  TipoMotor  \\\n",
       "0          frontal                  88.6  168.8   64.1  ...  2548       dohc   \n",
       "1          frontal                  88.6  168.8   64.1  ...  2548       dohc   \n",
       "2          frontal                  94.5  171.2   65.5  ...  2823       ohcv   \n",
       "3          frontal                  99.8  176.6   66.2  ...  2337        ohc   \n",
       "4          frontal                  99.4  176.6   66.4  ...  2824        ohc   \n",
       "..             ...                   ...    ...    ...  ...   ...        ...   \n",
       "200        frontal                 109.1  188.8   68.9  ...  2952        ohc   \n",
       "201        frontal                 109.1  188.8   68.8  ...  3049        ohc   \n",
       "202        frontal                 109.1  188.8   68.9  ...  3012       ohcv   \n",
       "203        frontal                 109.1  188.8   68.9  ...  3217        ohc   \n",
       "204        frontal                 109.1  188.8   68.9  ...  3062        ohc   \n",
       "\n",
       "    Cilindros  DiametroCilindro stroke CaballosFuerza peak-rpm KmCiudad  \\\n",
       "0           4              3.47   2.68            111     5000       21   \n",
       "1           4              3.47   2.68            111     5000       21   \n",
       "2           6              2.68   3.47            154     5000       19   \n",
       "3           4              3.19    3.4            102     5500       24   \n",
       "4           5              3.19    3.4            115     5500       18   \n",
       "..        ...               ...    ...            ...      ...      ...   \n",
       "200         4              3.78   3.15            114     5400       23   \n",
       "201         4              3.78   3.15            160     5300       19   \n",
       "202         6              3.58   2.87            134     5500       18   \n",
       "203         6              3.01    3.4            106     4800       26   \n",
       "204         4              3.78   3.15            114     5400       19   \n",
       "\n",
       "     KmCarretera  Precio  \n",
       "0             27   13495  \n",
       "1             27   16500  \n",
       "2             26   16500  \n",
       "3             30   13950  \n",
       "4             22   17450  \n",
       "..           ...     ...  \n",
       "200           28   16845  \n",
       "201           25   19045  \n",
       "202           23   21485  \n",
       "203           27   22470  \n",
       "204           25   22625  \n",
       "\n",
       "[205 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.drop(columns=[\"SistemaCombustible\",\"RadioCompresion\",\"TamanoMotor\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f144ed8a-72d6-4c80-b77f-b12fba0b28bb",
   "metadata": {},
   "source": [
    "## 3. Estadística descriptiva\n",
    "### 3.1 Tipos de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4312caca-942d-43f3-9ac4-2305abda85e5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.2 Medidas de posición / tendencia central\n",
    "- Mediana _median()_\n",
    "- Media aritmética _mean()_\n",
    "- Moda _mode()_\n",
    "- Percentiles _quantile()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7eaed51f-282e-4579-8040-4b6ce3f9d9e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10345.0\n",
      "13173.819512195121\n",
      "54.1\n",
      "53.724878048780525\n",
      "0    toyota\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.Precio.median())\n",
    "print(df.Precio.mean())\n",
    "print(df.Altura.median())\n",
    "print(df.Altura.mean())\n",
    "print(df.Marca.mode())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "adb8bda2-84b6-4783-895f-c4573898d8c0",
   "metadata": {},
   "source": [
    "print(df.Altura.quantiles())\n",
    "print(df.Altura.quantiles(q=0.93))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e414de1-cb26-44d6-8204-31d6d918c449",
   "metadata": {},
   "source": [
    "¿Cómo saber cuántos Toyota hay en Marca?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f1a3bea-b536-4187-a42b-891d874fdd14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.1\n",
      "56.7\n"
     ]
    }
   ],
   "source": [
    "print(df.Altura.quantile())\n",
    "print(df.Altura.quantile(q=0.93))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c2e034-03a0-4884-b962-794e8bf90982",
   "metadata": {},
   "source": [
    "### 3.3 Medidas de dispersión\n",
    "- Mínimo _min()_\n",
    "- Máximo _max()_\n",
    "- Varianza _var()_\n",
    "- Desviación estándar _std()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33ac4249-8796-49e9-a655-1c3eb9da5102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5118\n",
      "45400\n",
      "61998050.952558614\n",
      "7873.884108402829\n"
     ]
    }
   ],
   "source": [
    "print(df.Precio.min())\n",
    "print(df.Precio.max())\n",
    "print(df.Precio.var())\n",
    "print(df.Precio.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263fcf0f-08ef-4b67-894b-2d03b6190885",
   "metadata": {},
   "source": [
    "#### 3.3.1 Coeficiente de variación o coeficiente de variación de Pearson\n",
    "Variación con respecto a la media (si se multiplica por 100 se obtiene un porcentaje) y representa qué tan dispersos están los datos. Entre dos conjuntos de datos, el que tenga un número mayor es más disperso.\n",
    "$$\\dfrac{\\sigma}{\\bar{x}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ef3aa1c-bd2d-4291-a07c-e20d48ce1da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeficientes de variación\n",
      "Puertas 31.730784703052194 %\n",
      "Precio 59.76918160381585 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Coeficientes de variación\")\n",
    "print(\"Puertas\", 100*df.Puertas.std() / df.Puertas.mean(), \"%\")\n",
    "print(\"Precio\", 100*df.Precio.std() / df.Precio.mean(), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8744312-fcba-484e-9a92-8cfe42b23b18",
   "metadata": {},
   "source": [
    "### 3.4 Medidas de forma\n",
    "<img src=\"./imagenes/skew-wikipedia.png\" style=\"height: 200px;\"/>\n",
    "\n",
    "#### **Sesgo o Asimetría _skew()_**\n",
    "  - \\> 0 Cola a la derecha\n",
    "  - < 0 Cola a la izquierda\n",
    "  - 0  Simétrica perfecta\n",
    "  \n",
    "Tercer momento central\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b84f5653-a7e7-4730-bbc2-4f920f8c72d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asimetría 0.1559537713215604\n"
     ]
    }
   ],
   "source": [
    "print(\"Asimetría\", df.Largo.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3090f319-16f0-452b-93a5-d11d87f611df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Largo.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6efda9-5bf0-48d3-958e-2b24b1357b48",
   "metadata": {},
   "source": [
    "#### Curtosis _kurtosis()_ y _kurt()_\n",
    "<img src=\"./imagenes/kurtosis.jpg\" style=\"height: 200px;\"/>\n",
    "\n",
    "  - \\> 0 Leptocúrtica\n",
    "  - 0  Mesocúrtica (distribución normal)\n",
    "  - <0  Platicúrtica\n",
    "Cuarto momento central"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6fdb47e-8352-4905-8c62-2ca879a9a06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9474368224961656"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Puertas.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63297045-dacf-4e77-9bba-c292bfa8cdb7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4. Histogramas, Correlaciones, gráficas\n",
    "### 4.1 Histogramas con _hist()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f427939-dd3f-4fb0-843b-dfb98fa17a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Altura.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851517f5-6f65-4f3a-b465-b0b24ed2282d",
   "metadata": {},
   "source": [
    "Configuración de número de divisiones (_bins_) en un histograma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66c75de-b870-472e-ac91-f906ac5b87e2",
   "metadata": {},
   "source": [
    "Generación de histogramas para cada variable del dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470da894-1b79-4228-af82-2b96cb525576",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf35cbbc-253f-4d01-8898-6c145d2deec3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df5099f7-b731-4471-ac48-a8d98c7f18b0",
   "metadata": {},
   "source": [
    "Etiquetas en los ejes $x$ y $y$ con _set_xlabel()_, _set_ylabel() o _set()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72653b6-6291-415c-a078-75d8ca04bec2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a4869ac8-7957-4878-80f4-da2a3a182d4c",
   "metadata": {},
   "source": [
    "Histograma de valores discretos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de91a736-fb76-4796-9798-92b7fdf1cd88",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df.drop(columns =[\"Distancia entre ejes\",\"KmCiudad\",\"KmCarretera\",\"SistemaCombustible\",\"RadioCompresion\",\"TamanoMotor\"])\n",
    "pd.plotting.scatter_matrix(df2);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "480719fa-335b-4383-a2e5-482feee514b9",
   "metadata": {},
   "source": [
    "### 4.2 Correlaciones\n",
    "#### 4.2.1 Diagramas de dispersión con _plot.scatter()_\n",
    "##### De dos variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc369330-f860-4cce-97c5-d879adcbd3c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Altura\", y=\"Largo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13ed621-6d84-4a2f-a385-6d5b31e2d65c",
   "metadata": {},
   "source": [
    "##### De TRES variables\n",
    "Ejemplos de colormap en https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
    "Configuración de ejes con _set()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ebe53d3-6311-4c5b-a543-0148b2fba51d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e85ab7c8-3774-4e92-8538-a21caf9ee929",
   "metadata": {},
   "source": [
    "#### 4.2.2 Matriz de diagramas de dispersión con _plotting.scatter.matrix()_\n",
    "Grabado de imagen en archivo con _savefig()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b3951bd-e21b-438b-ac29-ce7dee07492c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2141e9cd-2572-43f3-b0ca-d93a37454a99",
   "metadata": {},
   "source": [
    "#### 4.2.3 Coeficientes de correlación [-1, 0]\n",
    "- 0  No se encuentra correlación\n",
    "- 1  Directamente proporcional perfecta\n",
    "- -1 Inversamente proporcional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4358bee6-b372-48a7-ba9b-04d259420c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e008798-493e-4d67-9224-1785eba291c9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'matriz' 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-18-ed30a9e17adf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmatriz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"correlaciones.csv\"\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"figura.png\"\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 'matriz' is not defined"
     ]
    }
   ],
   "source": [
    "matriz.to.csv(\"correlaciones.csv\")\n",
    "plt.savefig(\"figura.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb7b8dd-3c37-4610-a065-388b826a87cd",
   "metadata": {},
   "source": [
    "¿Quiénes tienen mayor correlación (positiva, negativa) y quiénes menor?\n",
    "- Mayor correlación: Números cercanos al 1 (directamente proporcionales) y -1 (inversamente proporcionales)\n",
    "- Menor correlación son valores cercanos al cero (0)\n",
    "\n",
    "Considerando Precio ¿con quién tiene una mejor correlación?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b894c585-ab7b-4c38-9a01-cf62b32f718f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0728c261-5ff6-49c1-866c-4f5e89265a39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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