03a_Precipitacion_RegresionLogistica.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMxjAd8B4dT2N3T3u1Lhvk6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sergioGarcia91/ML_and_EDA/blob/main/03a_Precipitacion_RegresionLogistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regresión Logística - Dataset: Precipitación Santander 2020\n",
        "\n",
        "> *Ser tan rápidos como el más lento, \\\n",
        "> y ser tan lentos como el más rápido.*\n",
        "\n",
        "**Autor:** Sergio Andrés García Arias  \n",
        "**Versión 01:** Diciembre 2023"
      ],
      "metadata": {
        "id": "ShPowuDSeytx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introducción\n",
        "\n",
        "En este Notebook utilizaremos un método de análisis de clasificación conocido como `Logistic Regression`. Se utiliza cuando estamos interesados clasificar considerando la probabilidad de que ocurra un evento binario (como el éxito o el fracaso, la aprobación o la desaprobación, etc.) en función de un conjunto de variables independientes.\n",
        "\n",
        "La `Regresión Logística` es una técnica especialmente valiosa en problemas de clasificación, donde deseamos asignar una observación a una de dos categorías posibles. A diferencia de la regresión lineal, que predice valores continuos, la regresión logística modela la relación entre las variables independientes y la probabilidad de ocurrencia del evento deseado mediante una función logística.\n",
        "\n",
        "> **Observación:** En este notebook hemos decidido omitir la realización del EDA. En un Notebook posterior abordaremos este aspecto para profundizar en la comprensión de los datos. Lo recomendable es llevar a cabo el EDA antes de aplicar `Logistic Regression` para obtener una visión más completa del conjunto de datos.\n",
        "\n",
        "## ¿Cómo funciona?\n",
        "\n",
        "La `Logistic Regression` utiliza una curva para representar la probabilidad estimada de que ocurra el evento binario en función de las variables independientes. La curva logística se ajusta de manera que la salida cae entre 0 y 1, lo que permite interpretar el resultado como una probabilidad.\n",
        "\n",
        "\n",
        "<center>\n",
        "<img src='https://upload.wikimedia.org/wikipedia/commons/thumb/c/cb/Exam_pass_logistic_curve.svg/800px-Exam_pass_logistic_curve.svg.png' width=500 />\n",
        "\n",
        "Representacion grafica de una curva de regresión logística, donde se muestra la probabilidad estimada de aprobar un examen (variable dependiente binaria) versus las horas de estudio (variable independiente escalar). *Fuente: [Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)*\n",
        "</center>\n"
      ],
      "metadata": {
        "id": "r8lRFc9afs7f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inicio"
      ],
      "metadata": {
        "id": "X386C6wNfGDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression # Para el modelo de Logistic Regression\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay # Para evaluar el modelo"
      ],
      "metadata": {
        "id": "zGELXKtMfHOY"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cargar CSV"
      ],
      "metadata": {
        "id": "M-QsCoyPi5sc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://github.com/sergioGarcia91/ML_and_EDA/raw/main/Datasets/PrecipitacionSantander_IDEAM_2020.csv'\n",
        "df = pd.read_csv(url, sep=';', decimal='.')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "Da7cEpcRi7GE",
        "outputId": "650f3d1a-c5c0-4ea1-cef2-0b4251f1f56e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CodigoEstacion                   NombreEstacion   Latitud   Longitud  \\\n",
              "0        23195502  AEROPUERTO PALONEGRO [23195502]  7.121472 -73.184528   \n",
              "1        23195502  AEROPUERTO PALONEGRO [23195502]  7.121472 -73.184528   \n",
              "2        23195502  AEROPUERTO PALONEGRO [23195502]  7.121472 -73.184528   \n",
              "3        23195502  AEROPUERTO PALONEGRO [23195502]  7.121472 -73.184528   \n",
              "4        23195502  AEROPUERTO PALONEGRO [23195502]  7.121472 -73.184528   \n",
              "\n",
              "   Altitud             Categoria  \\\n",
              "0     1189  SinÃ³ptica Principal   \n",
              "1     1189  SinÃ³ptica Principal   \n",
              "2     1189  SinÃ³ptica Principal   \n",
              "3     1189  SinÃ³ptica Principal   \n",
              "4     1189  SinÃ³ptica Principal   \n",
              "\n",
              "                                             Entidad  \\\n",
              "0  INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...   \n",
              "1  INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...   \n",
              "2  INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...   \n",
              "3  INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...   \n",
              "4  INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...   \n",
              "\n",
              "                            AreaOperativa Departamento Municipio  ...  \\\n",
              "0  Area Operativa 08 - Santanderes-Arauca    Santander   Lebrija  ...   \n",
              "1  Area Operativa 08 - Santanderes-Arauca    Santander   Lebrija  ...   \n",
              "2  Area Operativa 08 - Santanderes-Arauca    Santander   Lebrija  ...   \n",
              "3  Area Operativa 08 - Santanderes-Arauca    Santander   Lebrija  ...   \n",
              "4  Area Operativa 08 - Santanderes-Arauca    Santander   Lebrija  ...   \n",
              "\n",
              "  FechaSuspension    IdParametro  Etiqueta     DescripcionSerie Frecuencia  \\\n",
              "0             NaN  PRECIPITACION  PTPM_CON  DÃ­a pluviomÃ©trico     Diaria   \n",
              "1             NaN  PRECIPITACION  PTPM_CON  DÃ­a pluviomÃ©trico     Diaria   \n",
              "2             NaN  PRECIPITACION  PTPM_CON  DÃ­a pluviomÃ©trico     Diaria   \n",
              "3             NaN  PRECIPITACION  PTPM_CON  DÃ­a pluviomÃ©trico     Diaria   \n",
              "4             NaN  PRECIPITACION  PTPM_CON  DÃ­a pluviomÃ©trico     Diaria   \n",
              "\n",
              "             Fecha Valor  Grado  Calificador  NivelAprobacion  \n",
              "0  20/08/2020 0:00   4.9     50          NaN              900  \n",
              "1  21/08/2020 0:00   4.0     50          NaN              900  \n",
              "2  22/08/2020 0:00   2.6     50          NaN              900  \n",
              "3  23/08/2020 0:00   6.0     50          NaN              900  \n",
              "4  24/08/2020 0:00   0.6     50          NaN              900  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5c8c44a1-ff42-4070-8a9e-747b464b8181\" class=\"colab-df-container\">\n",
              "    <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>CodigoEstacion</th>\n",
              "      <th>NombreEstacion</th>\n",
              "      <th>Latitud</th>\n",
              "      <th>Longitud</th>\n",
              "      <th>Altitud</th>\n",
              "      <th>Categoria</th>\n",
              "      <th>Entidad</th>\n",
              "      <th>AreaOperativa</th>\n",
              "      <th>Departamento</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>...</th>\n",
              "      <th>FechaSuspension</th>\n",
              "      <th>IdParametro</th>\n",
              "      <th>Etiqueta</th>\n",
              "      <th>DescripcionSerie</th>\n",
              "      <th>Frecuencia</th>\n",
              "      <th>Fecha</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Grado</th>\n",
              "      <th>Calificador</th>\n",
              "      <th>NivelAprobacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23195502</td>\n",
              "      <td>AEROPUERTO PALONEGRO [23195502]</td>\n",
              "      <td>7.121472</td>\n",
              "      <td>-73.184528</td>\n",
              "      <td>1189</td>\n",
              "      <td>SinÃ³ptica Principal</td>\n",
              "      <td>INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...</td>\n",
              "      <td>Area Operativa 08 - Santanderes-Arauca</td>\n",
              "      <td>Santander</td>\n",
              "      <td>Lebrija</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PRECIPITACION</td>\n",
              "      <td>PTPM_CON</td>\n",
              "      <td>DÃ­a pluviomÃ©trico</td>\n",
              "      <td>Diaria</td>\n",
              "      <td>20/08/2020 0:00</td>\n",
              "      <td>4.9</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23195502</td>\n",
              "      <td>AEROPUERTO PALONEGRO [23195502]</td>\n",
              "      <td>7.121472</td>\n",
              "      <td>-73.184528</td>\n",
              "      <td>1189</td>\n",
              "      <td>SinÃ³ptica Principal</td>\n",
              "      <td>INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...</td>\n",
              "      <td>Area Operativa 08 - Santanderes-Arauca</td>\n",
              "      <td>Santander</td>\n",
              "      <td>Lebrija</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PRECIPITACION</td>\n",
              "      <td>PTPM_CON</td>\n",
              "      <td>DÃ­a pluviomÃ©trico</td>\n",
              "      <td>Diaria</td>\n",
              "      <td>21/08/2020 0:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23195502</td>\n",
              "      <td>AEROPUERTO PALONEGRO [23195502]</td>\n",
              "      <td>7.121472</td>\n",
              "      <td>-73.184528</td>\n",
              "      <td>1189</td>\n",
              "      <td>SinÃ³ptica Principal</td>\n",
              "      <td>INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...</td>\n",
              "      <td>Area Operativa 08 - Santanderes-Arauca</td>\n",
              "      <td>Santander</td>\n",
              "      <td>Lebrija</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PRECIPITACION</td>\n",
              "      <td>PTPM_CON</td>\n",
              "      <td>DÃ­a pluviomÃ©trico</td>\n",
              "      <td>Diaria</td>\n",
              "      <td>22/08/2020 0:00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23195502</td>\n",
              "      <td>AEROPUERTO PALONEGRO [23195502]</td>\n",
              "      <td>7.121472</td>\n",
              "      <td>-73.184528</td>\n",
              "      <td>1189</td>\n",
              "      <td>SinÃ³ptica Principal</td>\n",
              "      <td>INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...</td>\n",
              "      <td>Area Operativa 08 - Santanderes-Arauca</td>\n",
              "      <td>Santander</td>\n",
              "      <td>Lebrija</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PRECIPITACION</td>\n",
              "      <td>PTPM_CON</td>\n",
              "      <td>DÃ­a pluviomÃ©trico</td>\n",
              "      <td>Diaria</td>\n",
              "      <td>23/08/2020 0:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23195502</td>\n",
              "      <td>AEROPUERTO PALONEGRO [23195502]</td>\n",
              "      <td>7.121472</td>\n",
              "      <td>-73.184528</td>\n",
              "      <td>1189</td>\n",
              "      <td>SinÃ³ptica Principal</td>\n",
              "      <td>INSTITUTO DE HIDROLOGIA METEOROLOGIA Y ESTUDIO...</td>\n",
              "      <td>Area Operativa 08 - Santanderes-Arauca</td>\n",
              "      <td>Santander</td>\n",
              "      <td>Lebrija</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PRECIPITACION</td>\n",
              "      <td>PTPM_CON</td>\n",
              "      <td>DÃ­a pluviomÃ©trico</td>\n",
              "      <td>Diaria</td>\n",
              "      <td>24/08/2020 0:00</td>\n",
              "      <td>0.6</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c8c44a1-ff42-4070-8a9e-747b464b8181')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5c8c44a1-ff42-4070-8a9e-747b464b8181 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5c8c44a1-ff42-4070-8a9e-747b464b8181');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f0009638-40bd-472d-a9fe-16115912e074\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0009638-40bd-472d-a9fe-16115912e074')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0009638-40bd-472d-a9fe-16115912e074 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rf6RxqA5jgUL",
        "outputId": "cc455ff0-6ae6-4ef8-9b09-2f598b140dec"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 33827 entries, 0 to 33826\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   CodigoEstacion    33827 non-null  int64  \n",
            " 1   NombreEstacion    33827 non-null  object \n",
            " 2   Latitud           33827 non-null  float64\n",
            " 3   Longitud          33827 non-null  float64\n",
            " 4   Altitud           33827 non-null  int64  \n",
            " 5   Categoria         33827 non-null  object \n",
            " 6   Entidad           33827 non-null  object \n",
            " 7   AreaOperativa     33827 non-null  object \n",
            " 8   Departamento      33827 non-null  object \n",
            " 9   Municipio         33827 non-null  object \n",
            " 10  FechaInstalacion  33827 non-null  object \n",
            " 11  FechaSuspension   0 non-null      float64\n",
            " 12  IdParametro       33827 non-null  object \n",
            " 13  Etiqueta          33827 non-null  object \n",
            " 14  DescripcionSerie  33827 non-null  object \n",
            " 15  Frecuencia        33827 non-null  object \n",
            " 16  Fecha             33827 non-null  object \n",
            " 17  Valor             33827 non-null  float64\n",
            " 18  Grado             33827 non-null  int64  \n",
            " 19  Calificador       0 non-null      float64\n",
            " 20  NivelAprobacion   33827 non-null  int64  \n",
            "dtypes: float64(5), int64(4), object(12)\n",
            "memory usage: 5.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(df.describe(), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dYa7gORujl_s",
        "outputId": "dc1d5c9a-3101-4e6d-f2c9-abd8b4e3ba0f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       CodigoEstacion   Latitud  Longitud   Altitud  FechaSuspension  \\\n",
              "count        33827.00  33827.00  33827.00  33827.00              0.0   \n",
              "mean      23991119.11      6.78    -73.30   1158.67              NaN   \n",
              "std        2212999.61      0.49      0.40    958.22              NaN   \n",
              "min       23110060.00      5.76    -74.33     10.00              NaN   \n",
              "25%       23190210.00      6.36    -73.56    171.00              NaN   \n",
              "50%       24010650.00      6.79    -73.24    950.00              NaN   \n",
              "75%       24030320.00      7.20    -72.99   1814.00              NaN   \n",
              "max       37015020.00      7.78    -72.56   3824.00              NaN   \n",
              "\n",
              "          Valor    Grado  Calificador  NivelAprobacion  \n",
              "count  33827.00  33827.0          0.0         33827.00  \n",
              "mean       5.58     50.0          NaN          1065.22  \n",
              "std       13.24      0.0          NaN           149.23  \n",
              "min        0.00     50.0          NaN           900.00  \n",
              "25%        0.00     50.0          NaN           900.00  \n",
              "50%        0.00     50.0          NaN          1200.00  \n",
              "75%        5.00     50.0          NaN          1200.00  \n",
              "max      162.00     50.0          NaN          1200.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59d21722-f6f8-4985-a1a1-95f7f24250b2\" class=\"colab-df-container\">\n",
              "    <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>CodigoEstacion</th>\n",
              "      <th>Latitud</th>\n",
              "      <th>Longitud</th>\n",
              "      <th>Altitud</th>\n",
              "      <th>FechaSuspension</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Grado</th>\n",
              "      <th>Calificador</th>\n",
              "      <th>NivelAprobacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>33827.00</td>\n",
              "      <td>33827.00</td>\n",
              "      <td>33827.00</td>\n",
              "      <td>33827.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33827.00</td>\n",
              "      <td>33827.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33827.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23991119.11</td>\n",
              "      <td>6.78</td>\n",
              "      <td>-73.30</td>\n",
              "      <td>1158.67</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.58</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1065.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2212999.61</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.40</td>\n",
              "      <td>958.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>149.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>23110060.00</td>\n",
              "      <td>5.76</td>\n",
              "      <td>-74.33</td>\n",
              "      <td>10.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23190210.00</td>\n",
              "      <td>6.36</td>\n",
              "      <td>-73.56</td>\n",
              "      <td>171.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>900.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>24010650.00</td>\n",
              "      <td>6.79</td>\n",
              "      <td>-73.24</td>\n",
              "      <td>950.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24030320.00</td>\n",
              "      <td>7.20</td>\n",
              "      <td>-72.99</td>\n",
              "      <td>1814.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>37015020.00</td>\n",
              "      <td>7.78</td>\n",
              "      <td>-72.56</td>\n",
              "      <td>3824.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59d21722-f6f8-4985-a1a1-95f7f24250b2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59d21722-f6f8-4985-a1a1-95f7f24250b2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59d21722-f6f8-4985-a1a1-95f7f24250b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ac2d83e4-30e4-4552-8f58-5217ad9e32d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac2d83e4-30e4-4552-8f58-5217ad9e32d4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ac2d83e4-30e4-4552-8f58-5217ad9e32d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdGdhGYzjdVE",
        "outputId": "c5df3a05-f4b1-4510-a23c-fc0d5ddfe337"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CodigoEstacion', 'NombreEstacion', 'Latitud', 'Longitud', 'Altitud',\n",
              "       'Categoria', 'Entidad', 'AreaOperativa', 'Departamento', 'Municipio',\n",
              "       'FechaInstalacion', 'FechaSuspension', 'IdParametro', 'Etiqueta',\n",
              "       'DescripcionSerie', 'Frecuencia', 'Fecha', 'Valor', 'Grado',\n",
              "       'Calificador', 'NivelAprobacion'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Subgrupo"
      ],
      "metadata": {
        "id": "RlNZXYoIkLG7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a considerar solo una estación para generar el modelo de regresión Logística, tomaremos la correspondiente a Bucaramanga.\n",
        "\n",
        "```python\n",
        "'BUCARAMANGA IDEAM [23190830]'\n",
        "```\n",
        "Las columnas de interés correspondes a `'Fecha'` y `'Valor'`.\n"
      ],
      "metadata": {
        "id": "sYAhmVIDkXIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['NombreEstacion'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drxmmjRNjfWd",
        "outputId": "a90724c9-c110-48c2-adf8-1aa074f1cbfa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AEROPUERTO PALONEGRO [23195502]',\n",
              "       'AEROPUERTO YARIGUIES [23155030]', 'AGUASCLARAS [24060040]',\n",
              "       'ALBANIA [23125060]', 'ALBANIA [24050110]',\n",
              "       'ASERRIO EL [24030850]', 'BARAYA [24030630]', 'BERLIN [37015020]',\n",
              "       'BOLIVAR [24010640]', 'BRISAS LAS HACIENDA [24065010]',\n",
              "       'BUCARAMANGA IDEAM [23190830]', 'CACHIRI  - AUT [23195200]',\n",
              "       'CAMPO CAPOTE [23125040]', 'CAPITANEJO [24035260]',\n",
              "       'CARARE [23125050]', 'CARCASI [24030320]', 'CARMEN EL [23145020]',\n",
              "       'CENTRO EL [23155040]', 'CEPITA [24030300]', 'CERRITO [24030210]',\n",
              "       'CHARALA [24025050]', 'CHIMA [24015260]', 'CHUCURI [23130010]',\n",
              "       'CIMITARRA [23125120]', 'CONFINES [24010230]',\n",
              "       'COQUERA LA [23180120]', 'COROMORO [24020120]',\n",
              "       'CUCHARO EL [24025020]', 'CURITI 2 [24020130]',\n",
              "       'DOS BOCAS HACIENDA [23140070]', 'EL PARAMO - AUT [24030680]',\n",
              "       'ELOY VALENZUELA [23180080]', 'ENCINO [24020040]',\n",
              "       'ESCUELA AGRICOLA MOGOTES [24025040]', 'ESTABLO EL [23180050]',\n",
              "       'FLORESTA LA [23190590]', 'FUENTE LA [24050100]',\n",
              "       'GALVICIA LA [23190400]', 'GAMBITA [24015280]', 'GUACA [24030750]',\n",
              "       'GUAVATA [24010820]', 'JESUS MARIA [24010210]',\n",
              "       'LAGUNA LA [23190260]', 'LAJA LA [24015250]',\n",
              "       'LANDAZURI [23125130]', 'LLANO DE PALMAS [23190350]',\n",
              "       'LLANO GRANDE  - AUT [23195110]', 'MACARAVITA [24030290]',\n",
              "       'MAGARA [23190210]', 'MALAGA 2 [24030950]',\n",
              "       'MAMONAL EL HACIENDA [24020150]', 'MATAJIRA [23190340]',\n",
              "       'MESA LA [24060050]', 'MOLAGAVITA [24030340]',\n",
              "       'NARANJO EL [23190440]', 'OIBA [24010240]', 'OLIVAL [24010650]',\n",
              "       'PADILLA [23110060]', 'PALMA LA [24010850]',\n",
              "       'PALMAR EL [24055040]', 'PALMAS [23190380]',\n",
              "       'PALO GORDO [23190280]', 'PANTANO EL [23190600]',\n",
              "       'PATURIA [23180110]', 'PAVAS LAS [24020220]', 'PAYOA 5 [24060080]',\n",
              "       'PICACHO EL [23190300]', 'PIEDECUESTA GRANJA [23190700]',\n",
              "       'PLAYON EL [23190140]', 'PORTACHUELO [23190360]',\n",
              "       'PORTILLO EL [37010060]', 'PORVENIR EL [23180040]',\n",
              "       'PRADERA LA [23120220]', 'PROVINCIA [23190320]',\n",
              "       'PUEBLO VIEJO [24020230]', 'PUENTE FERROCARRIL [23147020]',\n",
              "       'PUERTO ARAUJO ALERTAS [23120200]', 'PUERTO WILCHES [23180020]',\n",
              "       'PUTANA LA [24050070]', 'REMOLINO [24040060]',\n",
              "       'SABANA DE TORRES [23180070]', 'SAN ANDRES [24030270]',\n",
              "       'SAN RAFAEL [23190560]', 'SAN VICENTE [24050060]',\n",
              "       'SANTA ISABEL [24040050]', 'SANTUARIO EL [24011070]',\n",
              "       'SIMACOTA [24010660]', 'SITIO NUEVO [23180100]',\n",
              "       'SUAITA [24010760]', 'SUCRE [24010670]', 'SUSA [24030370]',\n",
              "       'TABETA [37010050]', 'TONA  - AUT [23190130]',\n",
              "       'TOPE EL [24030330]', 'VALLE DE SAN JOSE [24020080]',\n",
              "       'VERDE LA [23120010]', 'VETAS-EL POZO [23190450]',\n",
              "       'VILLA LEIVA [23185010]', 'VIVERO SURATA  - AUT [23195090]',\n",
              "       'VOLCAN EL [24035400]', 'ZAPATOCA [24055030]'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "estacion = 'BUCARAMANGA IDEAM [23190830]'\n",
        "df_Bucaramanga = df[['Fecha', 'Valor']][df['NombreEstacion'] == estacion].copy().reset_index(drop=True)\n",
        "df_Bucaramanga['Fecha'] =  pd.to_datetime(df_Bucaramanga['Fecha'], dayfirst=True)\n",
        "df_Bucaramanga.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wRZMoLJsk4Ih",
        "outputId": "7372796c-2261-4ec2-a371-28c21c40689e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Fecha  Valor\n",
              "0 2020-01-01    0.0\n",
              "1 2020-01-02    0.0\n",
              "2 2020-01-03    0.0\n",
              "3 2020-01-04    0.0\n",
              "4 2020-01-05    0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bcc4da05-aa19-4d56-a214-0917ca881580\" class=\"colab-df-container\">\n",
              "    <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>Fecha</th>\n",
              "      <th>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcc4da05-aa19-4d56-a214-0917ca881580')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bcc4da05-aa19-4d56-a214-0917ca881580 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bcc4da05-aa19-4d56-a214-0917ca881580');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-793f062f-3b7e-4a6c-bce7-d745b1e2f017\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-793f062f-3b7e-4a6c-bce7-d745b1e2f017')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-793f062f-3b7e-4a6c-bce7-d745b1e2f017 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_Bucaramanga.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwprlXwvl4MS",
        "outputId": "3072e3f7-2b6c-4a4b-b247-2f0355a94dd3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 363 entries, 0 to 362\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   Fecha   363 non-null    datetime64[ns]\n",
            " 1   Valor   363 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 5.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_Bucaramanga.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_vW-t3i3mXF4",
        "outputId": "546d2fb7-4560-4ab3-cdbc-8c20a4efd449"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Valor\n",
              "count  363.000000\n",
              "mean     3.229477\n",
              "std      8.518474\n",
              "min      0.000000\n",
              "25%      0.000000\n",
              "50%      0.000000\n",
              "75%      2.750000\n",
              "max     86.900000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4d9fda1-64a4-4025-af54-748d282fbfad\" class=\"colab-df-container\">\n",
              "    <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>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>363.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.229477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.518474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>86.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4d9fda1-64a4-4025-af54-748d282fbfad')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d4d9fda1-64a4-4025-af54-748d282fbfad button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d4d9fda1-64a4-4025-af54-748d282fbfad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-594e8930-ef84-4431-84c7-bc0bcacaa55f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-594e8930-ef84-4431-84c7-bc0bcacaa55f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-594e8930-ef84-4431-84c7-bc0bcacaa55f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparación Previa\n",
        "\n",
        "Antes de proceder con el entrenamiento del modelo, vamos a tener en cuenta las siguientes consideraciones:\n",
        "\n",
        "1. **Periodo de Evaluación:** Se seleccionarán los datos correspondientes a los 7 días anteriores al día de interés para realizar la evaluación del modelo. *Variables independientes*\n",
        "\n",
        "2. **Variable de Interés - Precipitación:** Se incorporará la variable binaria que indica la presencia o ausencia de precipitación. Se asignará el valor `0` cuando no haya ocurrido lluvia y `1` cuando se registre precipitación. *Variable dependiente*"
      ],
      "metadata": {
        "id": "yIjJ_qEfmeAY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Bucaramanga['Llovio'] = 0\n",
        "df_Bucaramanga['Llovio'][df_Bucaramanga['Valor'] != 0] = 1\n",
        "df_Bucaramanga.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "zMD-JOx-nXRJ",
        "outputId": "1d5e79fa-fe36-4314-fac0-4e6b122c05bd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-cbc935475b83>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_Bucaramanga['Llovio'][df_Bucaramanga['Valor'] != 0] = 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Valor      Llovio\n",
              "count  363.000000  363.000000\n",
              "mean     3.229477    0.451791\n",
              "std      8.518474    0.498357\n",
              "min      0.000000    0.000000\n",
              "25%      0.000000    0.000000\n",
              "50%      0.000000    0.000000\n",
              "75%      2.750000    1.000000\n",
              "max     86.900000    1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96b44d1f-82d2-42be-871f-4705a7c3cf35\" class=\"colab-df-container\">\n",
              "    <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>Valor</th>\n",
              "      <th>Llovio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>363.000000</td>\n",
              "      <td>363.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.229477</td>\n",
              "      <td>0.451791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.518474</td>\n",
              "      <td>0.498357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.750000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>86.900000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96b44d1f-82d2-42be-871f-4705a7c3cf35')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96b44d1f-82d2-42be-871f-4705a7c3cf35 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-96b44d1f-82d2-42be-871f-4705a7c3cf35');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9ed650e-4faa-4b63-abfd-b22f8898050e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9ed650e-4faa-4b63-abfd-b22f8898050e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9ed650e-4faa-4b63-abfd-b22f8898050e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gráfico"
      ],
      "metadata": {
        "id": "neufJKKPoMK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax1 = plt.subplots(figsize=(15,5))\n",
        "\n",
        "ax1.plot(df_Bucaramanga['Fecha'],\n",
        "         df_Bucaramanga['Valor'],\n",
        "         c='blue',\n",
        "         ls='--')\n",
        "ax1.tick_params(axis='y', labelcolor= 'b')\n",
        "ax1.set_ylabel('Precipitación [mm]')\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "mp = ax2.scatter(df_Bucaramanga['Fecha'],\n",
        "                 df_Bucaramanga['Llovio']*100,\n",
        "                 s= 5,\n",
        "                 c=df_Bucaramanga['Llovio']*100,\n",
        "                 cmap='YlGnBu',\n",
        "                 vmin=0,\n",
        "                 vmax=100)\n",
        "ax2.set_ylabel('Probabilidad [%]')\n",
        "ax2.grid(ls='--', color='grey')\n",
        "\n",
        "plt.colorbar(mp, ax=ax2, shrink=0.75)\n",
        "plt.suptitle('Precipitación Bucaramanga 2020')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "zTN8azBSoNuA",
        "outputId": "9148e05c-d1ff-4856-ff65-c605b7997300"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DF 7 días"
      ],
      "metadata": {
        "id": "pB25YfRMubD6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a generar un DataFrame para entrenar el modelo, considerando los 7 días previos con el objetivo de evaluar si puede o no llover el día de \"hoy\" `D0`. La estructura de ejemplo sería la siguiente:\n",
        "\n",
        "<center>\n",
        "\n",
        "| Fecha Interes | D-7 | D-6 | D-5 | D-4 | D-3 | D-2 | D-1 | D0 |\n",
        "|---------------|-----|-----|-----|-----|-----|-----|-----|----|\n",
        "| 08-01-2020    | 0   | 0   | 0   | 0   | 0   | 1   | 0   | 1  |\n",
        "| 09-01-2020    | 1   | 0   | 0   | 0   | 0   | 1   | 0   | 1  |\n",
        "| 10-01-2020    | 1   | 0   | 0   | 0   | 0   | 1   | 0   | 0  |\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cTaEvttxurKH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Bucaramanga.head(27)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "URz4UsyFxB_m",
        "outputId": "7d362005-3667-477d-914e-5022651f849c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Fecha  Valor  Llovio\n",
              "0  2020-01-01    0.0       0\n",
              "1  2020-01-02    0.0       0\n",
              "2  2020-01-03    0.0       0\n",
              "3  2020-01-04    0.0       0\n",
              "4  2020-01-05    0.0       0\n",
              "5  2020-01-06    0.0       0\n",
              "6  2020-01-07    0.0       0\n",
              "7  2020-01-08    0.0       0\n",
              "8  2020-01-09    0.0       0\n",
              "9  2020-01-10    0.0       0\n",
              "10 2020-01-11    0.0       0\n",
              "11 2020-01-12    0.0       0\n",
              "12 2020-01-13    0.0       0\n",
              "13 2020-01-14    0.0       0\n",
              "14 2020-01-15    0.0       0\n",
              "15 2020-01-16    0.0       0\n",
              "16 2020-01-17    0.0       0\n",
              "17 2020-01-18    0.0       0\n",
              "18 2020-01-19    0.9       1\n",
              "19 2020-01-20   20.7       1\n",
              "20 2020-01-24   10.4       1\n",
              "21 2020-01-25    0.0       0\n",
              "22 2020-01-26   29.3       1\n",
              "23 2020-01-27    1.4       1\n",
              "24 2020-01-28    1.8       1\n",
              "25 2020-01-29   15.5       1\n",
              "26 2020-01-30    0.0       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-267ad0a0-86e1-4490-bf82-7438ffd2509a\" class=\"colab-df-container\">\n",
              "    <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>Fecha</th>\n",
              "      <th>Valor</th>\n",
              "      <th>Llovio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-01-10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2020-01-18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2020-01-19</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2020-01-20</td>\n",
              "      <td>20.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>10.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>29.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>15.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-267ad0a0-86e1-4490-bf82-7438ffd2509a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-267ad0a0-86e1-4490-bf82-7438ffd2509a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-267ad0a0-86e1-4490-bf82-7438ffd2509a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e06ca970-2baf-4456-be4b-dfb13bc4ea92\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e06ca970-2baf-4456-be4b-dfb13bc4ea92')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e06ca970-2baf-4456-be4b-dfb13bc4ea92 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# La columna fecha solo va a ser de referencia para cuando queramos de nuevo graficar\n",
        "columnasDF = ['Fecha D0', 'D-7', 'D-6', 'D-5', 'D-4', 'D-3', 'D-2', 'D-1', 'D0']\n",
        "fechaD0 = []\n",
        "matrizPrecipitacion =[]\n",
        "matrizPrecipitacion\n",
        "\n",
        "for i in range(7, len(df_Bucaramanga)):\n",
        "  dias = list(df_Bucaramanga['Llovio'].loc[i-7:i].values)\n",
        "  #print(dias)\n",
        "  matrizPrecipitacion.append(dias)\n",
        "  fechaD0.append(str(df_Bucaramanga['Fecha'].loc[i]))\n",
        "\n",
        "\n",
        "matrizPrecipitacion = np.array(matrizPrecipitacion)\n",
        "fechaD0 = np.array(fechaD0).reshape(-1,1)\n",
        "\n",
        "df_RL = pd.DataFrame(np.concatenate((fechaD0, matrizPrecipitacion), axis=1),\n",
        "                     columns=columnasDF)\n",
        "df_RL['Fecha D0'] = pd.to_datetime(df_RL['Fecha D0'], yearfirst=True)\n",
        "df_RL[['D-7', 'D-6', 'D-5', 'D-4', 'D-3', 'D-2', 'D-1', 'D0']].dtypes\n",
        "df_RL"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8fp-7g3XudJs",
        "outputId": "29262fb9-9dcf-4fb9-ddb8-f8b64e0b3330"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fecha D0 D-7 D-6 D-5 D-4 D-3 D-2 D-1 D0\n",
              "0   2020-01-08   0   0   0   0   0   0   0  0\n",
              "1   2020-01-09   0   0   0   0   0   0   0  0\n",
              "2   2020-01-10   0   0   0   0   0   0   0  0\n",
              "3   2020-01-11   0   0   0   0   0   0   0  0\n",
              "4   2020-01-12   0   0   0   0   0   0   0  0\n",
              "..         ...  ..  ..  ..  ..  ..  ..  .. ..\n",
              "351 2020-12-27   0   0   0   0   0   0   0  1\n",
              "352 2020-12-28   0   0   0   0   0   0   1  0\n",
              "353 2020-12-29   0   0   0   0   0   1   0  0\n",
              "354 2020-12-30   0   0   0   0   1   0   0  1\n",
              "355 2020-12-31   0   0   0   1   0   0   1  1\n",
              "\n",
              "[356 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-353642d1-3f7d-4848-8194-e7dc9f3dd467\" class=\"colab-df-container\">\n",
              "    <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>Fecha D0</th>\n",
              "      <th>D-7</th>\n",
              "      <th>D-6</th>\n",
              "      <th>D-5</th>\n",
              "      <th>D-4</th>\n",
              "      <th>D-3</th>\n",
              "      <th>D-2</th>\n",
              "      <th>D-1</th>\n",
              "      <th>D0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>356 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-353642d1-3f7d-4848-8194-e7dc9f3dd467')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-353642d1-3f7d-4848-8194-e7dc9f3dd467 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-353642d1-3f7d-4848-8194-e7dc9f3dd467');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d93b45d4-193d-4995-855d-971324dfb5db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d93b45d4-193d-4995-855d-971324dfb5db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d93b45d4-193d-4995-855d-971324dfb5db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4f38a680-d125-43f6-b3fc-e148c8424db8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_RL')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4f38a680-d125-43f6-b3fc-e148c8424db8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_RL');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_RL.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "op4MsyWIDdh8",
        "outputId": "4418249a-26e4-4250-e90a-8c4bdb22ee25"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 356 entries, 0 to 355\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Fecha D0  356 non-null    datetime64[ns]\n",
            " 1   D-7       356 non-null    object        \n",
            " 2   D-6       356 non-null    object        \n",
            " 3   D-5       356 non-null    object        \n",
            " 4   D-4       356 non-null    object        \n",
            " 5   D-3       356 non-null    object        \n",
            " 6   D-2       356 non-null    object        \n",
            " 7   D-1       356 non-null    object        \n",
            " 8   D0        356 non-null    object        \n",
            "dtypes: datetime64[ns](1), object(8)\n",
            "memory usage: 25.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Nos regresa cono object\n",
        "# cambiemos a int\n",
        "df_RL[['D-7', 'D-6', 'D-5', 'D-4', 'D-3', 'D-2', 'D-1', 'D0']] = df_RL[['D-7', 'D-6', 'D-5', 'D-4', 'D-3', 'D-2', 'D-1', 'D0']].astype(int, copy=False)"
      ],
      "metadata": {
        "id": "PluABuqeCjFM"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_RL.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7PR-HCX3Puv",
        "outputId": "9e00ddb8-01f5-4f87-d7da-c934debcc4db"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 356 entries, 0 to 355\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Fecha D0  356 non-null    datetime64[ns]\n",
            " 1   D-7       356 non-null    int64         \n",
            " 2   D-6       356 non-null    int64         \n",
            " 3   D-5       356 non-null    int64         \n",
            " 4   D-4       356 non-null    int64         \n",
            " 5   D-3       356 non-null    int64         \n",
            " 6   D-2       356 non-null    int64         \n",
            " 7   D-1       356 non-null    int64         \n",
            " 8   D0        356 non-null    int64         \n",
            "dtypes: datetime64[ns](1), int64(8)\n",
            "memory usage: 25.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "NGGjD5873tDX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para este modelo no vamos a modifivar ningun parametro adicional.\n",
        "Para realizar el cálculo de la probabilidad de lluvia en Bucaramanga, emplearemos la clase [`LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) proporcionada por la biblioteca `Scikit-Learn`."
      ],
      "metadata": {
        "id": "qHA3Gxaj6Pwl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_RL['D0'].to_numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bpmpTaT66IT",
        "outputId": "849369f4-e64e-4e40-86c6-2e07262f5190"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creamos el modelo de Regresión Logística\n",
        "modelo_regresion_logistica = LogisticRegression()\n",
        "\n",
        "# Entrenar el modelo\n",
        "modelo_regresion_logistica.fit(df_RL.iloc[:,1:8].to_numpy(), # Extraemos como matriz los 7 días\n",
        "                               df_RL['D0'].to_numpy()) # Columna de interes o D0\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "z9DYnV-r3EMC",
        "outputId": "85c497e8-1eb4-4865-93df-9963a7ab4aec"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Puntaje\n",
        "modelo_regresion_logistica.score(df_RL.iloc[:, 1:8].to_numpy(),\n",
        "                                 df_RL['D0'].to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IonvBKIN7mHS",
        "outputId": "db83f030-9f27-4d73-e58f-4675718105dc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6601123595505618"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para predecir la etiqueta 0 o 1\n",
        "modelo_regresion_logistica.predict(df_RL.iloc[:,1:8].to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1Olkg-r7TOs",
        "outputId": "463cee26-82da-488a-b025-31456432749a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_RL['Predicho'] = modelo_regresion_logistica.predict(df_RL.iloc[:,1:8].to_numpy())"
      ],
      "metadata": {
        "id": "mXppbyuWAJ8_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Para conocer la probabilidad de cada clase\n",
        "modelo_regresion_logistica.predict_proba(df_RL.iloc[:,1:8].to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ouvagLRl7-dT",
        "outputId": "dc825193-80a6-4d7f-fe7a-97f6ed37c014"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.56323461, 0.43676539],\n",
              "       [0.31900192, 0.68099808],\n",
              "       [0.30771956, 0.69228044],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.36503015, 0.63496985],\n",
              "       [0.66368656, 0.33631344],\n",
              "       [0.57759882, 0.42240118],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.67951553, 0.32048447],\n",
              "       [0.71113503, 0.28886497],\n",
              "       [0.65558899, 0.34441101],\n",
              "       [0.6643831 , 0.3356169 ],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.59648104, 0.40351896],\n",
              "       [0.60893723, 0.39106277],\n",
              "       [0.65558899, 0.34441101],\n",
              "       [0.6643831 , 0.3356169 ],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.52553967, 0.47446033],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.59648104, 0.40351896],\n",
              "       [0.32612303, 0.67387697],\n",
              "       [0.32883284, 0.67116716],\n",
              "       [0.28746681, 0.71253319],\n",
              "       [0.36503015, 0.63496985],\n",
              "       [0.66368656, 0.33631344],\n",
              "       [0.29823937, 0.70176063],\n",
              "       [0.69335971, 0.30664029],\n",
              "       [0.71248909, 0.28751091],\n",
              "       [0.7382727 , 0.2617273 ],\n",
              "       [0.77526415, 0.22473585],\n",
              "       [0.6716105 , 0.3283895 ],\n",
              "       [0.52553967, 0.47446033],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.35751019, 0.64248981],\n",
              "       [0.35751019, 0.64248981],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.73192683, 0.26807317],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.65118786, 0.34881214],\n",
              "       [0.64092266, 0.35907734],\n",
              "       [0.66290968, 0.33709032],\n",
              "       [0.40410307, 0.59589693],\n",
              "       [0.34486487, 0.65513513],\n",
              "       [0.42073909, 0.57926091],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.5552017 , 0.4447983 ],\n",
              "       [0.29823937, 0.70176063],\n",
              "       [0.69335971, 0.30664029],\n",
              "       [0.71248909, 0.28751091],\n",
              "       [0.7382727 , 0.2617273 ],\n",
              "       [0.51740749, 0.48259251],\n",
              "       [0.62876706, 0.37123294],\n",
              "       [0.73835322, 0.26164678],\n",
              "       [0.37934433, 0.62065567],\n",
              "       [0.64374941, 0.35625059],\n",
              "       [0.61822887, 0.38177113],\n",
              "       [0.49143178, 0.50856822],\n",
              "       [0.64374941, 0.35625059],\n",
              "       [0.33479374, 0.66520626],\n",
              "       [0.72026907, 0.27973093],\n",
              "       [0.63338855, 0.36661145],\n",
              "       [0.35671156, 0.64328844],\n",
              "       [0.47031273, 0.52968727],\n",
              "       [0.29418807, 0.70581193],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.5552017 , 0.4447983 ],\n",
              "       [0.57759882, 0.42240118],\n",
              "       [0.73192683, 0.26807317],\n",
              "       [0.49308158, 0.50691842],\n",
              "       [0.72809662, 0.27190338],\n",
              "       [0.73835322, 0.26164678],\n",
              "       [0.66290968, 0.33709032],\n",
              "       [0.685728  , 0.314272  ],\n",
              "       [0.38861403, 0.61138597],\n",
              "       [0.74693288, 0.25306712],\n",
              "       [0.64092266, 0.35907734],\n",
              "       [0.66290968, 0.33709032],\n",
              "       [0.40410307, 0.59589693],\n",
              "       [0.62876706, 0.37123294],\n",
              "       [0.73835322, 0.26164678],\n",
              "       [0.66290968, 0.33709032],\n",
              "       [0.685728  , 0.314272  ],\n",
              "       [0.6716105 , 0.3283895 ],\n",
              "       [0.52553967, 0.47446033],\n",
              "       [0.65118786, 0.34881214],\n",
              "       [0.3568072 , 0.6431928 ],\n",
              "       [0.61957408, 0.38042592],\n",
              "       [0.34936343, 0.65063657],\n",
              "       [0.31470521, 0.68529479],\n",
              "       [0.69344831, 0.30655169],\n",
              "       [0.5855292 , 0.4144708 ],\n",
              "       [0.48328978, 0.51671022],\n",
              "       [0.62112866, 0.37887134],\n",
              "       [0.7191185 , 0.2808815 ],\n",
              "       [0.75663962, 0.24336038],\n",
              "       [0.685728  , 0.314272  ],\n",
              "       [0.38861403, 0.61138597],\n",
              "       [0.74693288, 0.25306712],\n",
              "       [0.64092266, 0.35907734],\n",
              "       [0.37934433, 0.62065567],\n",
              "       [0.3596359 , 0.6403641 ],\n",
              "       [0.29418807, 0.70581193],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.40187947, 0.59812053],\n",
              "       [0.69335971, 0.30664029],\n",
              "       [0.71248909, 0.28751091],\n",
              "       [0.7382727 , 0.2617273 ],\n",
              "       [0.77526415, 0.22473585],\n",
              "       [0.38861403, 0.61138597],\n",
              "       [0.47843853, 0.52156147],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.56323461, 0.43676539],\n",
              "       [0.31900192, 0.68099808],\n",
              "       [0.58851205, 0.41148795],\n",
              "       [0.71248909, 0.28751091],\n",
              "       [0.46714496, 0.53285504],\n",
              "       [0.47031273, 0.52968727],\n",
              "       [0.29418807, 0.70581193],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.73192683, 0.26807317],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.65118786, 0.34881214],\n",
              "       [0.3568072 , 0.6431928 ],\n",
              "       [0.61957408, 0.38042592],\n",
              "       [0.63338855, 0.36661145],\n",
              "       [0.35671156, 0.64328844],\n",
              "       [0.47031273, 0.52968727],\n",
              "       [0.57285042, 0.42714958],\n",
              "       [0.71113503, 0.28886497],\n",
              "       [0.65558899, 0.34441101],\n",
              "       [0.38089967, 0.61910033],\n",
              "       [0.45422033, 0.54577967],\n",
              "       [0.42073909, 0.57926091],\n",
              "       [0.56323461, 0.43676539],\n",
              "       [0.31900192, 0.68099808],\n",
              "       [0.30771956, 0.69228044],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.64908562, 0.35091438],\n",
              "       [0.70439464, 0.29560536],\n",
              "       [0.63329177, 0.36670823],\n",
              "       [0.49308158, 0.50691842],\n",
              "       [0.72809662, 0.27190338],\n",
              "       [0.46724869, 0.53275131],\n",
              "       [0.33606511, 0.66393489],\n",
              "       [0.30780835, 0.69219165],\n",
              "       [0.53916125, 0.46083875],\n",
              "       [0.42548256, 0.57451744],\n",
              "       [0.30771956, 0.69228044],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.36503015, 0.63496985],\n",
              "       [0.38016368, 0.61983632],\n",
              "       [0.53105422, 0.46894578],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.73192683, 0.26807317],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.52553967, 0.47446033],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.40187947, 0.59812053],\n",
              "       [0.41271659, 0.58728341],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.36503015, 0.63496985],\n",
              "       [0.66368656, 0.33631344],\n",
              "       [0.57759882, 0.42240118],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.3972172 , 0.6027828 ],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.5552017 , 0.4447983 ],\n",
              "       [0.29823937, 0.70176063],\n",
              "       [0.41271659, 0.58728341],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.36503015, 0.63496985],\n",
              "       [0.38016368, 0.61983632],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.35751019, 0.64248981],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.73192683, 0.26807317],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.53105422, 0.46894578],\n",
              "       [0.40187947, 0.59812053],\n",
              "       [0.41271659, 0.58728341],\n",
              "       [0.38944084, 0.61055916],\n",
              "       [0.64908562, 0.35091438],\n",
              "       [0.70439464, 0.29560536],\n",
              "       [0.34926871, 0.65073129],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.67951553, 0.32048447],\n",
              "       [0.71113503, 0.28886497],\n",
              "       [0.37170211, 0.62829789],\n",
              "       [0.62112866, 0.37887134],\n",
              "       [0.44311569, 0.55688431],\n",
              "       [0.44452353, 0.55547647],\n",
              "       [0.58861297, 0.41138703],\n",
              "       [0.30510465, 0.69489535],\n",
              "       [0.43649167, 0.56350833],\n",
              "       [0.56485773, 0.43514227],\n",
              "       [0.40973752, 0.59026248],\n",
              "       [0.713656  , 0.286344  ],\n",
              "       [0.61050796, 0.38949204],\n",
              "       [0.7382727 , 0.2617273 ],\n",
              "       [0.51740749, 0.48259251],\n",
              "       [0.62876706, 0.37123294],\n",
              "       [0.73835322, 0.26164678],\n",
              "       [0.66290968, 0.33709032],\n",
              "       [0.40410307, 0.59589693],\n",
              "       [0.34486487, 0.65513513],\n",
              "       [0.42073909, 0.57926091],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.3972172 , 0.6027828 ],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.53105422, 0.46894578],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.3972172 , 0.6027828 ],\n",
              "       [0.67092153, 0.32907847],\n",
              "       [0.60114964, 0.39885036],\n",
              "       [0.63329177, 0.36670823],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.36717502, 0.63282498],\n",
              "       [0.3147951 , 0.6852049 ],\n",
              "       [0.28611667, 0.71388333],\n",
              "       [0.5552017 , 0.4447983 ],\n",
              "       [0.57759882, 0.42240118],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.446154  , 0.553846  ],\n",
              "       [0.3972172 , 0.6027828 ],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.26033214, 0.73966786],\n",
              "       [0.35751019, 0.64248981],\n",
              "       [0.35751019, 0.64248981],\n",
              "       [0.64162701, 0.35837299],\n",
              "       [0.40187947, 0.59812053],\n",
              "       [0.69335971, 0.30664029],\n",
              "       [0.71248909, 0.28751091],\n",
              "       [0.46714496, 0.53285504],\n",
              "       [0.47031273, 0.52968727],\n",
              "       [0.29418807, 0.70581193],\n",
              "       [0.38787248, 0.61212752],\n",
              "       [0.27950679, 0.72049321],\n",
              "       [0.53105422, 0.46894578],\n",
              "       [0.68373282, 0.31626718],\n",
              "       [0.45904215, 0.54095785],\n",
              "       [0.72159707, 0.27840293],\n",
              "       [0.7191185 , 0.2808815 ],\n",
              "       [0.75663962, 0.24336038],\n",
              "       [0.685728  , 0.314272  ],\n",
              "       [0.38861403, 0.61138597],\n",
              "       [0.47843853, 0.52156147],\n",
              "       [0.59648104, 0.40351896],\n",
              "       [0.32612303, 0.67387697],\n",
              "       [0.32883284, 0.67116716],\n",
              "       [0.28746681, 0.71253319],\n",
              "       [0.64908562, 0.35091438],\n",
              "       [0.70439464, 0.29560536],\n",
              "       [0.63329177, 0.36670823],\n",
              "       [0.75785301, 0.24214699],\n",
              "       [0.76378321, 0.23621679],\n",
              "       [0.78089123, 0.21910877],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.69270834, 0.30729166],\n",
              "       [0.41197465, 0.58802535],\n",
              "       [0.65118786, 0.34881214],\n",
              "       [0.64092266, 0.35907734],\n",
              "       [0.37934433, 0.62065567]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_RL['Probabilidad'] = np.round(100*modelo_regresion_logistica.predict_proba(df_RL.iloc[:,1:8].to_numpy())[:, 1], 2)\n",
        "df_RL.head(27)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "7PeDICkN8YlS",
        "outputId": "b3c44cca-ea64-43ff-b898-dbb902727676"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Fecha D0  D-7  D-6  D-5  D-4  D-3  D-2  D-1  D0  Predicho  Probabilidad\n",
              "0  2020-01-08    0    0    0    0    0    0    0   0         0         30.73\n",
              "1  2020-01-09    0    0    0    0    0    0    0   0         0         30.73\n",
              "2  2020-01-10    0    0    0    0    0    0    0   0         0         30.73\n",
              "3  2020-01-11    0    0    0    0    0    0    0   0         0         30.73\n",
              "4  2020-01-12    0    0    0    0    0    0    0   0         0         30.73\n",
              "5  2020-01-13    0    0    0    0    0    0    0   0         0         30.73\n",
              "6  2020-01-14    0    0    0    0    0    0    0   0         0         30.73\n",
              "7  2020-01-15    0    0    0    0    0    0    0   0         0         30.73\n",
              "8  2020-01-16    0    0    0    0    0    0    0   0         0         30.73\n",
              "9  2020-01-17    0    0    0    0    0    0    0   0         0         30.73\n",
              "10 2020-01-18    0    0    0    0    0    0    0   0         0         30.73\n",
              "11 2020-01-19    0    0    0    0    0    0    0   1         0         30.73\n",
              "12 2020-01-20    0    0    0    0    0    0    1   1         1         58.80\n",
              "13 2020-01-24    0    0    0    0    0    1    1   1         1         63.28\n",
              "14 2020-01-25    0    0    0    0    1    1    1   0         1         68.52\n",
              "15 2020-01-26    0    0    0    1    1    1    0   1         0         43.68\n",
              "16 2020-01-27    0    0    1    1    1    0    1   1         1         68.10\n",
              "17 2020-01-28    0    1    1    1    0    1    1   1         1         69.23\n",
              "18 2020-01-29    1    1    1    0    1    1    1   1         1         61.06\n",
              "19 2020-01-30    1    1    0    1    1    1    1   0         1         63.50\n",
              "20 2020-01-31    1    0    1    1    1    1    0   0         0         33.63\n",
              "21 2020-02-01    0    1    1    1    1    0    0   1         0         42.24\n",
              "22 2020-02-02    1    1    1    1    0    0    1   1         1         54.10\n",
              "23 2020-02-03    1    1    1    0    0    1    1   0         1         55.38\n",
              "24 2020-02-04    1    1    0    0    1    1    0   0         0         32.05\n",
              "25 2020-02-05    1    0    0    1    1    0    0   0         0         28.89\n",
              "26 2020-02-06    0    0    1    1    0    0    0   0         0         34.44"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93785c71-aee7-4f3e-a5ba-8403e71ede3c\" class=\"colab-df-container\">\n",
              "    <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>Fecha D0</th>\n",
              "      <th>D-7</th>\n",
              "      <th>D-6</th>\n",
              "      <th>D-5</th>\n",
              "      <th>D-4</th>\n",
              "      <th>D-3</th>\n",
              "      <th>D-2</th>\n",
              "      <th>D-1</th>\n",
              "      <th>D0</th>\n",
              "      <th>Predicho</th>\n",
              "      <th>Probabilidad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>30.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>63.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>68.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>43.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>68.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>69.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>61.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>63.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>42.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <td>1</td>\n",
              "      <td>54.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>55.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020-02-04</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93785c71-aee7-4f3e-a5ba-8403e71ede3c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-93785c71-aee7-4f3e-a5ba-8403e71ede3c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-93785c71-aee7-4f3e-a5ba-8403e71ede3c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2497ed1c-c887-4f30-8a79-b693b1e143ff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2497ed1c-c887-4f30-8a79-b693b1e143ff')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2497ed1c-c887-4f30-8a79-b693b1e143ff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gráficas"
      ],
      "metadata": {
        "id": "Fpi1wcHR9YYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax1 = plt.subplots(figsize=(15,5))\n",
        "\n",
        "ax1.plot(df_Bucaramanga['Fecha'],\n",
        "         df_Bucaramanga['Valor'],\n",
        "         c='blue',\n",
        "         ls='--')\n",
        "ax1.tick_params(axis='y', labelcolor= 'b')\n",
        "ax1.set_ylabel('Precipitación [mm]')\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "mp = ax2.scatter(df_RL['Fecha D0'],\n",
        "                 df_RL['Probabilidad'],\n",
        "                 s= 5,\n",
        "                 c=df_RL['Probabilidad'],\n",
        "                 cmap='YlGnBu',\n",
        "                 vmin=0,\n",
        "                 vmax=100)\n",
        "ax2.set_ylabel('Probabilidad [%]')\n",
        "ax2.grid(ls='--', color='grey')\n",
        "\n",
        "plt.colorbar(mp, ax=ax2, shrink=0.75)\n",
        "plt.suptitle('Precipitación Bucaramanga 2020')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "9LiIa5tb9dwa",
        "outputId": "110aa77f-f4e0-46bd-c4e5-9d96b75863c1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para analizar los resultados, vamos a generar una matriz de confusión en la cual compararemos las predicciones con los datos reales.\n",
        "\n",
        "- True Positive (TP): es el número de datos que son correctamente predichos como positivos. Predice correctamente como `1`.\n",
        "- True Negative (TN): es el número de datos que son correctamente predichos como negativos. Predice correctamente como `0`.\n",
        "- False Positive (FP): es el número de datos que son incorrectamente predichos como positivos. Predice `1`, pero en realidad es `0`.\n",
        "- False Negative (FN): es el número de datos que son incorrectamente predichos como negativos. Predice `0`, pero en realidad es `1`."
      ],
      "metadata": {
        "id": "p_1OKust_DGI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tn, fp, fn, tp = confusion_matrix(df_RL['D0'], df_RL['Predicho']).ravel()\n",
        "\n",
        "print(\"True Negative:\", tn)\n",
        "print(\"False Positive:\", fp)\n",
        "print(\"False Negative:\", fn)\n",
        "print(\"True Positive:\", tp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PV5_VGR2_BWg",
        "outputId": "b00b9f63-2245-4c29-bb59-3fbeb2aafaaf"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Negative: 135\n",
            "False Positive: 57\n",
            "False Negative: 64\n",
            "True Positive: 100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(df_RL['D0'], df_RL['Predicho'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h02qsW-cAWQ-",
        "outputId": "5b26e4d3-1d36-4ded-f812-1004feddb3c7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[135,  57],\n",
              "       [ 64, 100]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_regresion_logistica.classes_ # Las etiquetas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFMTEIdiBXXP",
        "outputId": "fbb4bede-33ec-45f2-880a-8e32d23b80fb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_regresion_logistica.coef_ # Coeficientes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcqCOKAAE1CC",
        "outputId": "fc89e3cc-6823-49d0-a149-bb0cb4fbf7b4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.45805588,  0.09733358,  0.03258953,  0.13652389,  0.23344072,\n",
              "         0.18854704,  1.16861995]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_regresion_logistica.intercept_ # Intercepto"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNXOESaOJ_Ie",
        "outputId": "28551f05-3242-4d2e-cc58-82959de8bfd5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.81281172])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aún no habíamos analizado la ecuación de la regresión logística, que se expresa de la siguiente manera:\n",
        "\n",
        "$$\n",
        "\\begin{equation}\n",
        "p(x) = \\frac{ 1 }{ 1+e^{-(\\beta_0+\\beta_1x)} }\n",
        "\\end{equation}\n",
        "$$\n",
        "\n",
        "\n",
        "Donde,\n",
        "\n",
        "- $ p(x) $ es la probabilidad de que la variable dependiente $ Y $ sea igual a 1, dado un valor específico de $ x $.\n",
        "- $ \\beta_0 $ es el intercepto o término constante.\n",
        "- $ \\beta_1 $ es el coeficiente asociado a la variable independiente $ x $.\n",
        "- $ e $ es la base del logaritmo natural (aproximadamente 2.71828).\n",
        "\n"
      ],
      "metadata": {
        "id": "sAzyFF3fIdwP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(df_RL['D0'], df_RL['Predicho']),\n",
        "                              display_labels=modelo_regresion_logistica.classes_)\n",
        "\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "WHXYjuoiBerl",
        "outputId": "b564e6cd-ee9f-4563-8b84-8d9103a33f0b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Total días que si llovieron: ', df_RL['D0'].sum())\n",
        "print(64 + 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sp4LNiqICLEV",
        "outputId": "586c5c5b-a06a-429f-c091-0120e0b7d629"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total días que si llovieron:  164\n",
            "164\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Observaciones\n",
        "\n",
        "En este Notebook se exploró de manera sencilla el uso de regresión logística para clasificar (`0` o `1`) y evaluar la probabilidad de lluvia en Bucaramanga, considerando los 7 días previos a la fecha de interés. Sin embargo, surgen preguntas importantes que podrían enriquecer el análisis:\n",
        "\n",
        "1. **Consideración de la cantidad de milímetros de lluvia:** ¿Cómo afectaría el modelo si en lugar de simplemente clasificar la presencia o ausencia de lluvia, consideramos la cantidad de milímetros de lluvia? ¿Podría proporcionar una perspectiva más detallada sobre la intensidad de la lluvia?\n",
        "\n",
        "2. **Uso de una única estación:** Dado que solo se utilizó una estación meteorológica, ¿cómo se vería afectado el modelo si se incorporaran datos de múltiples estaciones? ¿Mejoraría la generalización del modelo al considerar diversas ubicaciones?\n",
        "\n",
        "3. **Ajuste en el número de días previos:** ¿Qué sucede si se aumenta o disminuye la cantidad de días previos considerados para la predicción? ¿Cómo impactaría en la capacidad del modelo para anticipar eventos de lluvia?\n",
        "\n",
        "4. **Consideración de otros parámetros:** ¿Existen otros parámetros que podrían enriquecer la predicción? ¿La inclusión de variables como el mes del año, la presencia de fenómenos climáticos como El Niño o La Niña, o incluso eventos estacionales afectaría significativamente el rendimiento del modelo?\n",
        "\n",
        "Estas preguntas adicionales pueden abrir nuevas perspectivas y mejorar la capacidad predictiva del modelo, ofreciendo una exploración más profunda y completa de los factores que influyen en la probabilidad de lluvia en Bucaramanga.\n",
        "\n",
        "---\n",
        "El método de `Logistic Regression` tiene también sus limitaciones:\n",
        "\n",
        "1. **Suposición de Linealidad:** La regresión logística asume una relación lineal entre las variables independientes y la probabilidad logarítmica de la variable dependiente. Esta suposición puede no ser adecuada si la verdadera relación es no lineal.\n",
        "\n",
        "2. **Sensibilidad a Outliers:** La presencia de valores atípicos en los datos puede influir significativamente en los resultados de la regresión logística, afectando su robustez y precisión.\n",
        "\n",
        "3. **Dependencia de las Características Seleccionadas:** El rendimiento del modelo puede depender en gran medida de las características seleccionadas para la predicción. La elección de variables incorrectas o irrelevantes puede afectar negativamente la capacidad del modelo para generalizar a nuevos datos.\n",
        "\n",
        "4. **Suposición de Independencia:** La regresión logística asume la independencia entre las observaciones, lo que significa que los errores para diferentes observaciones no deben estar correlacionados. Esta suposición puede no cumplirse en situaciones donde los datos están correlacionados en el tiempo o en el espacio.\n",
        "\n",
        "5. **Número de Observaciones:** La regresión logística puede ser sensible al número de observaciones en comparación con el número de parámetros. En situaciones de \"pocos eventos por variable,\" donde hay pocos casos positivos en relación con el número de variables independientes, el modelo puede tener dificultades para converger.\n",
        "\n",
        "6. **Manejo de Variables Categóricas:** Aunque es adecuada para variables binarias, la regresión logística puede requerir técnicas adicionales para manejar variables categóricas con muchas categorías.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "pH3BD5KjF9fS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fin"
      ],
      "metadata": {
        "id": "KcSgE8q7fHgp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "1YUin4Fnei4i"
      },
      "outputs": [],
      "source": []
    }
  ]
}