Convertir variables categóricas en numéricas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "mount_file_id": "1TejJmrxq59-K6F4ReIXqc55FhWvA-eE8",
      "authorship_tag": "ABX9TyPi/65+82QHibpVsshpw8ew",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Naren8520/Python/blob/main/Convertir%20variables%20categ%C3%B3ricas%20en%20num%C3%A9ricas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n12NSTFRn-Hp"
      },
      "source": [
        "# **Convertir variables categoricas en numéricas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "cellView": "form",
        "id": "TiTQOt6et5MH",
        "outputId": "f1da23c7-74c5-4b75-b5b3-06afa314dc35"
      },
      "source": [
        "#@title **Método One-Hot**\r\n",
        "from IPython.display import Image\r\n",
        "Image(\"/content/drive/MyDrive/imagenes/one_hot.png\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stVqWG8jsqNB"
      },
      "source": [
        "**¿Qué es One Hot Encoding?**\r\n",
        "\r\n",
        "Una codificación one hot es una representación de variables categóricas como vectores binarios.\r\n",
        "\r\n",
        "Esto primero requiere que los valores categóricos se asignen a valores enteros.\r\n",
        "\r\n",
        "Luego, cada valor entero se representa como un vector binario que tiene todos los valores cero excepto el índice del entero, que está marcado con un 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_XSHTIUs6ZC"
      },
      "source": [
        "**¿Por qué utilizar una codificación One Hot?**\r\n",
        "\r\n",
        "Una codificación one hot permite que la representación de datos categóricos sea más expresiva.\r\n",
        "\r\n",
        "Muchos algoritmos de aprendizaje automático no pueden trabajar directamente con datos categóricos. Las categorías deben convertirse en números. Esto es necesario para las variables de entrada y salida que son categóricas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHS9pvxXtVhh"
      },
      "source": [
        "En estos casos, nos gustaría darle a la red más poder expresivo para aprender un número similar a la probabilidad para cada posible valor de etiqueta. Esto puede ayudar a que el problema sea más fácil de modelar para la red. Cuando se usa una codificación en caliente para la variable de salida, puede ofrecer un conjunto de predicciones más matizado que una sola etiqueta."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgj4VAD6ooig"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import seaborn as sns"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "wHsvq7TZvxZF",
        "outputId": "0e49eaf1-2b8d-4d16-ec6b-ac8f5f95d27d"
      },
      "source": [
        "datos=sns.load_dataset(\"tips\")\r\n",
        "datos.head(10)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>25.29</td>\n",
              "      <td>4.71</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8.77</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>26.88</td>\n",
              "      <td>3.12</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>15.04</td>\n",
              "      <td>1.96</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14.78</td>\n",
              "      <td>3.23</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4\n",
              "5       25.29  4.71    Male     No  Sun  Dinner     4\n",
              "6        8.77  2.00    Male     No  Sun  Dinner     2\n",
              "7       26.88  3.12    Male     No  Sun  Dinner     4\n",
              "8       15.04  1.96    Male     No  Sun  Dinner     2\n",
              "9       14.78  3.23    Male     No  Sun  Dinner     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "Ivu6QHe3wWx-",
        "outputId": "8e9a5903-c3c3-4f20-fafe-4d5d5b93a96a"
      },
      "source": [
        "numerica=datos.drop([\"sex\",\"smoker\", \"day\", \"time\"], axis=1)\r\n",
        "numerica.head(10)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>25.29</td>\n",
              "      <td>4.71</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8.77</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>26.88</td>\n",
              "      <td>3.12</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>15.04</td>\n",
              "      <td>1.96</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14.78</td>\n",
              "      <td>3.23</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_bill   tip  size\n",
              "0       16.99  1.01     2\n",
              "1       10.34  1.66     3\n",
              "2       21.01  3.50     3\n",
              "3       23.68  3.31     2\n",
              "4       24.59  3.61     4\n",
              "5       25.29  4.71     4\n",
              "6        8.77  2.00     2\n",
              "7       26.88  3.12     4\n",
              "8       15.04  1.96     2\n",
              "9       14.78  3.23     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "4X6KkpOYw9h1",
        "outputId": "b3c3e636-bd14-4512-be7f-cecd18a93435"
      },
      "source": [
        "categorica=datos.filter([\"sex\",\"smoker\", \"day\", \"time\"])\r\n",
        "categorica.head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      sex smoker  day    time\n",
              "0  Female     No  Sun  Dinner\n",
              "1    Male     No  Sun  Dinner\n",
              "2    Male     No  Sun  Dinner\n",
              "3    Male     No  Sun  Dinner\n",
              "4  Female     No  Sun  Dinner\n",
              "5    Male     No  Sun  Dinner\n",
              "6    Male     No  Sun  Dinner\n",
              "7    Male     No  Sun  Dinner\n",
              "8    Male     No  Sun  Dinner\n",
              "9    Male     No  Sun  Dinner"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxyZFxYixaIe"
      },
      "source": [
        "vamos a empezar con la variable sexo.\r\n",
        "\r\n",
        "vamos a usar de pandas la función get_dummies()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c6yogUdxdPS",
        "outputId": "9231dd4a-6e5c-4c25-bf1a-8128b78e83d4"
      },
      "source": [
        "categorica.iloc[:,0]#variable sexo"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      Female\n",
              "1        Male\n",
              "2        Male\n",
              "3        Male\n",
              "4      Female\n",
              "        ...  \n",
              "239      Male\n",
              "240    Female\n",
              "241      Male\n",
              "242      Male\n",
              "243    Female\n",
              "Name: sex, Length: 244, dtype: category\n",
              "Categories (2, object): ['Male', 'Female']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "vpqIANNUyg6E",
        "outputId": "ebb626fc-8eaa-4271-8eae-4faaceb59277"
      },
      "source": [
        "#usamos la función get_dummies\r\n",
        "cat_numerical=pd.get_dummies(categorica.iloc[:,0], drop_first=True)\r\n",
        "cat_numerical"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Female\n",
              "0         1\n",
              "1         0\n",
              "2         0\n",
              "3         0\n",
              "4         1\n",
              "..      ...\n",
              "239       0\n",
              "240       1\n",
              "241       0\n",
              "242       0\n",
              "243       1\n",
              "\n",
              "[244 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "WbUZRe13y8L2",
        "outputId": "07610f66-5e56-4b4f-bfeb-191213a07256"
      },
      "source": [
        "cat_numerical=pd.get_dummies(categorica.iloc[:,0], drop_first=False)\r\n",
        "cat_numerical"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Male</th>\n",
              "      <th>Female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Male  Female\n",
              "0       0       1\n",
              "1       1       0\n",
              "2       1       0\n",
              "3       1       0\n",
              "4       0       1\n",
              "..    ...     ...\n",
              "239     1       0\n",
              "240     0       1\n",
              "241     1       0\n",
              "242     1       0\n",
              "243     0       1\n",
              "\n",
              "[244 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "P8iLcC6OzJlU",
        "outputId": "295e77df-6f81-4ddc-a542-3ffc4920b8bd"
      },
      "source": [
        "cat_numerical=pd.get_dummies(categorica, drop_first=True)\r\n",
        "cat_numerical"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex_Female</th>\n",
              "      <th>smoker_No</th>\n",
              "      <th>day_Fri</th>\n",
              "      <th>day_Sat</th>\n",
              "      <th>day_Sun</th>\n",
              "      <th>time_Dinner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>244 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sex_Female  smoker_No  day_Fri  day_Sat  day_Sun  time_Dinner\n",
              "0             1          1        0        0        1            1\n",
              "1             0          1        0        0        1            1\n",
              "2             0          1        0        0        1            1\n",
              "3             0          1        0        0        1            1\n",
              "4             1          1        0        0        1            1\n",
              "..          ...        ...      ...      ...      ...          ...\n",
              "239           0          1        0        1        0            1\n",
              "240           1          0        0        1        0            1\n",
              "241           0          0        0        1        0            1\n",
              "242           0          1        0        1        0            1\n",
              "243           1          1        0        0        0            1\n",
              "\n",
              "[244 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "VueKbQA4ze_j",
        "outputId": "481f1aef-bc27-4524-e67c-0a8cf8b32cb5"
      },
      "source": [
        "datos=pd.concat([numerica, cat_numerical], axis=1)\r\n",
        "datos.head(20)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "      <th>sex_Female</th>\n",
              "      <th>smoker_No</th>\n",
              "      <th>day_Fri</th>\n",
              "      <th>day_Sat</th>\n",
              "      <th>day_Sun</th>\n",
              "      <th>time_Dinner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>25.29</td>\n",
              "      <td>4.71</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8.77</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>26.88</td>\n",
              "      <td>3.12</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>15.04</td>\n",
              "      <td>1.96</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14.78</td>\n",
              "      <td>3.23</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.27</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>35.26</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>15.42</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>18.43</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14.83</td>\n",
              "      <td>3.02</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>21.58</td>\n",
              "      <td>3.92</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>10.33</td>\n",
              "      <td>1.67</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16.29</td>\n",
              "      <td>3.71</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>16.97</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20.65</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    total_bill   tip  size  sex_Female  ...  day_Fri  day_Sat  day_Sun  time_Dinner\n",
              "0        16.99  1.01     2           1  ...        0        0        1            1\n",
              "1        10.34  1.66     3           0  ...        0        0        1            1\n",
              "2        21.01  3.50     3           0  ...        0        0        1            1\n",
              "3        23.68  3.31     2           0  ...        0        0        1            1\n",
              "4        24.59  3.61     4           1  ...        0        0        1            1\n",
              "5        25.29  4.71     4           0  ...        0        0        1            1\n",
              "6         8.77  2.00     2           0  ...        0        0        1            1\n",
              "7        26.88  3.12     4           0  ...        0        0        1            1\n",
              "8        15.04  1.96     2           0  ...        0        0        1            1\n",
              "9        14.78  3.23     2           0  ...        0        0        1            1\n",
              "10       10.27  1.71     2           0  ...        0        0        1            1\n",
              "11       35.26  5.00     4           1  ...        0        0        1            1\n",
              "12       15.42  1.57     2           0  ...        0        0        1            1\n",
              "13       18.43  3.00     4           0  ...        0        0        1            1\n",
              "14       14.83  3.02     2           1  ...        0        0        1            1\n",
              "15       21.58  3.92     2           0  ...        0        0        1            1\n",
              "16       10.33  1.67     3           1  ...        0        0        1            1\n",
              "17       16.29  3.71     3           0  ...        0        0        1            1\n",
              "18       16.97  3.50     3           1  ...        0        0        1            1\n",
              "19       20.65  3.35     3           0  ...        0        1        0            1\n",
              "\n",
              "[20 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}