Desaparecidos_unseen.ipynb

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  "metadata": {
    "colab": {
      "name": "Desaparecidos_unseen.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alruizzo/missing_persons/blob/main/Desaparecidos_unseen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46CyE9mzsfBL"
      },
      "source": [
        "# **Proyecto de Aplicación Especialización en Estadística Aplicada**\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9YJynalneYf"
      },
      "source": [
        "## **Factores de predicción de la aparición de personas mayores reportadas como desaparecidas**\n",
        "---\n",
        "\n",
        "![](https://www.infobae.com/new-resizer/b8r9oLLmBqIgVSsie5UGwM1nvm4=/1200x900/filters:format(jpg):quality(85)//arc-anglerfish-arc2-prod-infobae.s3.amazonaws.com/public/UFQOZRZCURB4BOIRTLM3IT2HCM.jpg)\n",
        "\n",
        "**Por: Adriana Lucía Ruiz Rizzo**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IptGnPDks71G"
      },
      "source": [
        "### **1 Importar los paquetes requeridos**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bJgZhBMr0AL"
      },
      "source": [
        "# Manejo de datos\n",
        "import pandas as pd             \n",
        "import numpy as np\n",
        "\n",
        "# Manejo de gráficos\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Preprocesamiento\n",
        "from collections import Counter\n",
        "from datetime import datetime\n",
        "\n",
        "# Manejo de modelos\n",
        "from scipy import stats\n",
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEaLRTD-ugHK"
      },
      "source": [
        "### **2 Cargar datos (unseen)**\n",
        "---\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l1ByFOfuxTM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "outputId": "4fd9004c-ee20-4e15-a327-76a0baded576"
      },
      "source": [
        "datos = pd.read_csv(\"https://drive.google.com/uc?id=1SI8CV0NT54-RrYTsknPo9RAfe4IZ4hK7\")\n",
        "datos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Estado de la desaparición</th>\n",
              "      <th>Clasificación de la desaparición</th>\n",
              "      <th>Fecha de la desaparición</th>\n",
              "      <th>Edad al momento de la desaparición</th>\n",
              "      <th>Sexo del desaparecido</th>\n",
              "      <th>País de nacimiento del desaparecido</th>\n",
              "      <th>Ancestro racial del desaparecido</th>\n",
              "      <th>Estado civil del desaparecido</th>\n",
              "      <th>Escolaridad del desaparecido</th>\n",
              "      <th>Factor de vulnerabilidad del desaparecido</th>\n",
              "      <th>País donde ocurre la desaparición</th>\n",
              "      <th>Municipio donde ocurre la desaparición DANE</th>\n",
              "      <th>Departamento donde ocurre la desaparición DANE</th>\n",
              "      <th>Contexto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2006-12-29T00:00:00.000</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica secundaria o secundaria baja</td>\n",
              "      <td>Persona adicta a una droga natural o sintética</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2006-12-26T00:00:00.000</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica secundaria o secundaria baja</td>\n",
              "      <td>Persona adicta a una droga natural o sintética</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2006-12-29T00:00:00.000</td>\n",
              "      <td>13.0</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica secundaria o secundaria baja</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Aparecio Muerto</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2006-12-31T00:00:00.000</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Blanco</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Persona adicta a una droga natural o sintética</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2006-12-29T00:00:00.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación técnica profesional y tecnológica</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164762</th>\n",
              "      <td>164763</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Desaparición Presuntamente Forzada</td>\n",
              "      <td>2004-06-01T00:00:00.000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación inicial y educación preescolar</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Medellín</td>\n",
              "      <td>Antioquia</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164763</th>\n",
              "      <td>164764</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2021-09-18T00:00:00.000</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164764</th>\n",
              "      <td>164765</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Desaparición Presuntamente Forzada</td>\n",
              "      <td>1985-04-16T00:00:00.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Blanco</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación inicial y educación preescolar</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Riohacha</td>\n",
              "      <td>La Guajira</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164765</th>\n",
              "      <td>164766</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2021-09-22T00:00:00.000</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Mestizo</td>\n",
              "      <td>Unión libre</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Dos Quebradas</td>\n",
              "      <td>Risaralda</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164766</th>\n",
              "      <td>164767</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Sin Información</td>\n",
              "      <td>2021-09-18T00:00:00.000</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Indigena</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica secundaria o secundaria baja</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>Villavicencio</td>\n",
              "      <td>Meta</td>\n",
              "      <td>Desaparición</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>164767 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID  ...      Contexto\n",
              "0            1  ...  Desaparición\n",
              "1            2  ...  Desaparición\n",
              "2            3  ...  Desaparición\n",
              "3            4  ...  Desaparición\n",
              "4            5  ...  Desaparición\n",
              "...        ...  ...           ...\n",
              "164762  164763  ...  Desaparición\n",
              "164763  164764  ...  Desaparición\n",
              "164764  164765  ...  Desaparición\n",
              "164765  164766  ...  Desaparición\n",
              "164766  164767  ...  Desaparición\n",
              "\n",
              "[164767 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JA4h8ZPvz9rX"
      },
      "source": [
        "### **3 Revisar los datos**\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laE_gtg-0Fp3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "924246d8-6266-461f-d399-6e04f05456a7"
      },
      "source": [
        "# Tipo de datos\n",
        "datos.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 164767 entries, 0 to 164766\n",
            "Data columns (total 15 columns):\n",
            " #   Column                                          Non-Null Count   Dtype  \n",
            "---  ------                                          --------------   -----  \n",
            " 0   ID                                              164767 non-null  int64  \n",
            " 1   Estado de la desaparición                       164767 non-null  object \n",
            " 2   Clasificación de la desaparición                164767 non-null  object \n",
            " 3   Fecha de la desaparición                        164767 non-null  object \n",
            " 4   Edad al momento de la desaparición              164767 non-null  float64\n",
            " 5   Sexo del desaparecido                           164767 non-null  object \n",
            " 6   País de nacimiento del desaparecido             164767 non-null  object \n",
            " 7   Ancestro racial del desaparecido                164767 non-null  object \n",
            " 8   Estado civil del desaparecido                   164767 non-null  object \n",
            " 9   Escolaridad del desaparecido                    164767 non-null  object \n",
            " 10  Factor de vulnerabilidad del desaparecido       164767 non-null  object \n",
            " 11  País donde ocurre la desaparición               164767 non-null  object \n",
            " 12  Municipio donde ocurre la desaparición DANE     164767 non-null  object \n",
            " 13  Departamento donde ocurre la desaparición DANE  164767 non-null  object \n",
            " 14  Contexto                                        164767 non-null  object \n",
            "dtypes: float64(1), int64(1), object(13)\n",
            "memory usage: 18.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNhOge-EDs0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5a97244-458d-4a46-baa3-b25989ac4040"
      },
      "source": [
        "# Listar variables para ajustar sus nombres\n",
        "datos.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Estado de la desaparición', 'Clasificación de la desaparición',\n",
              "       'Fecha de la desaparición', 'Edad al momento de la desaparición',\n",
              "       'Sexo del desaparecido', 'País de nacimiento del desaparecido',\n",
              "       'Ancestro racial del desaparecido', 'Estado civil del desaparecido',\n",
              "       'Escolaridad del desaparecido',\n",
              "       'Factor de vulnerabilidad del desaparecido',\n",
              "       'País donde ocurre la desaparición',\n",
              "       'Municipio donde ocurre la desaparición DANE',\n",
              "       'Departamento donde ocurre la desaparición DANE', 'Contexto'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v5vPiDSEMu5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b0713e1-0a53-4b70-94ae-0e40785c4f4d"
      },
      "source": [
        "# Simplificar (y cambiar) los nombres de las variables\n",
        "datos.columns = ['ID', 'status', 'clasificacion',\n",
        "       'fecha', 'edad', 'sexo', 'pais_nac', 'ancestro', 'estado_civil',\n",
        "       'escolaridad', 'vulnerabilidad', 'pais_desap',\n",
        "       'munic_desap_DANE', 'dept_desap_DANE', 'contexto']\n",
        "datos.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'status', 'clasificacion', 'fecha', 'edad', 'sexo', 'pais_nac',\n",
              "       'ancestro', 'estado_civil', 'escolaridad', 'vulnerabilidad',\n",
              "       'pais_desap', 'munic_desap_DANE', 'dept_desap_DANE', 'contexto'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCFB6_fj05gl"
      },
      "source": [
        "### **4 Eliminar registros sin información de las variables más relevantes**\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fS5TOnuLxli"
      },
      "source": [
        "#### **a. Edad**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evkH123FKG31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e09b96c-a392-4eb4-f66c-5bf9a273ab9f"
      },
      "source": [
        "# Revisar valores únicos de edad para hacerse a una idea de los ajustes necesarios\n",
        "datos.edad.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.100e+01, 2.400e+01, 1.300e+01, 5.000e+01, 1.700e+01, 4.100e+01,\n",
              "       7.100e+01, 2.500e+01, 2.000e+01, 1.500e+01, 5.400e+01, 4.000e+01,\n",
              "       2.900e+01, 2.800e+01, 1.400e+01, 5.500e+01, 1.900e+01, 2.700e+01,\n",
              "       3.900e+01, 4.700e+01, 4.500e+01, 3.600e+01, 8.700e+01, 4.400e+01,\n",
              "       4.300e+01, 5.600e+01, 1.100e+01, 3.100e+01, 2.200e+01, 6.300e+01,\n",
              "       4.800e+01, 1.800e+01, 1.600e+01, 3.800e+01, 5.800e+01, 3.500e+01,\n",
              "       7.000e+01, 2.300e+01, 3.300e+01, 3.000e+01, 6.200e+01, 4.200e+01,\n",
              "       0.000e+00, 5.200e+01, 1.200e+01, 7.200e+01, 7.400e+01, 7.000e+00,\n",
              "       2.600e+01, 9.000e+00, 3.200e+01, 4.600e+01, 3.400e+01, 5.100e+01,\n",
              "       8.000e+00, 5.300e+01, 7.900e+01, 7.600e+01, 4.900e+01, 5.000e+00,\n",
              "       5.700e+01, 1.000e+01, 6.500e+01, 7.800e+01, 6.900e+01, 8.400e+01,\n",
              "       8.300e+01, 6.000e+00, 6.400e+01, 7.300e+01, 8.200e+01, 5.900e+01,\n",
              "       6.800e+01, 6.700e+01, 3.700e+01, 7.500e+01, 7.700e+01, 6.000e+01,\n",
              "       8.000e+01, 8.800e+01, 1.083e+00, 8.600e+01, 6.600e+01, 2.000e+00,\n",
              "       8.500e+01, 8.300e-02, 9.400e+01, 4.000e+00, 1.250e+00, 5.830e-01,\n",
              "       3.330e-01, 8.100e+01, 8.900e+01, 3.000e+00, 6.660e-01, 6.100e+01,\n",
              "       1.660e-01, 2.500e-01, 1.500e+00, 9.160e-01, 4.160e-01, 1.000e+00,\n",
              "       9.100e+01, 5.000e-01, 9.000e+01, 9.500e+01, 9.900e+01, 1.010e+02,\n",
              "       1.030e+02, 1.666e+00, 9.600e+01, 9.300e+01, 9.800e+01, 9.200e+01,\n",
              "       9.700e+01, 8.330e-01, 7.500e-01, 1.583e+00, 1.040e+02, 1.333e+00,\n",
              "       1.833e+00, 1.000e+02, 1.416e+00, 1.750e+00, 1.916e+00, 1.166e+00,\n",
              "       1.100e+02, 2.750e+00, 1.120e+02, 1.020e+02, 2.083e+00, 1.070e+02,\n",
              "       2.916e+00, 1.080e+02, 3.333e+00, 4.333e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1Sep4hyLB_w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdc5148c-5d3e-4906-ceb1-e8cbc42da0f8"
      },
      "source": [
        "# Seleccionar registros con valores de edad (determinar cuántos son faltantes)\n",
        "InfoEdad = datos[\"edad\"] != 'Sin información'\n",
        "len(datos[InfoEdad])                          # n = 202 no tienen info de edad"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "164767"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyyAQNxc6ldK"
      },
      "source": [
        "#### **b. Fecha de desaparición**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kwpu15UkNH-8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aacb600e-607b-4412-c531-8bee9b665cf4"
      },
      "source": [
        "# Revisar valores únicos de fecha para hacerse una idea de cómo deberían ajustarse\n",
        "datos.fecha.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2006-12-29T00:00:00.000', '2006-12-26T00:00:00.000',\n",
              "       '2006-12-31T00:00:00.000', ..., '1984-02-19T00:00:00.000',\n",
              "       '2021-09-28T00:00:00.000', '2021-09-29T00:00:00.000'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyMDoH1_TEZy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1155e08d-fe1e-4eea-beb1-e34b72b553f1"
      },
      "source": [
        "# Seleccionar registros con valores de fecha (determinar cuántos son faltantes)\n",
        "InfoFecha = datos[\"fecha\"] != 'Sin información'\n",
        "len(datos[InfoFecha])                         # n = 129 no tienen info de año"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "164767"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ar3j1JHPC3wd"
      },
      "source": [
        "#### **c. Clasificación de la desaparición**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqVWKN6cCytL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "420d8084-669b-4161-f715-54be846c6e5e"
      },
      "source": [
        "# Seleccionar registros que no corresponden a desaparición presuntamente forzada\n",
        "InfoClasif = datos.clasificacion != 'Desaparición Presuntamente Forzada' \n",
        "len(datos[InfoClasif])                       # n = 32403 tienen clasificación\n",
        "                                             # presuntamente forzada"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131981"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVNBiUbHOBqA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e507d3f4-abe4-4e90-a171-a877048dbb55"
      },
      "source": [
        "# Revisar valores únicos de clasificación de la desaparición\n",
        "datos.clasificacion.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sin Información', 'Desaparición Presuntamente Forzada'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbWlvowlwjq7"
      },
      "source": [
        "#### **Filtro**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQ7RBzW9wse4"
      },
      "source": [
        "- Se eliminan los datos sin información en las variables más importantes (edad y fecha de desaparición) y aquellos con presunta clasificación de desaparición como forzada."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-RzsMr8Laxf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dad1731a-4a07-4223-e550-a5874993c510"
      },
      "source": [
        "# Hasta aquí podemos filtrar aquellos registros seleccionados anteriormente\n",
        "datos = datos[InfoEdad & InfoFecha & InfoClasif]\n",
        "len(datos)                                      # n = 129776"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131981"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl07aMhcxLiX"
      },
      "source": [
        "- Se eliminan las variables redundantes o que no se necesitan en el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDuXfYSYPAk_"
      },
      "source": [
        "# Eliminar variables que no se necesitan o son redundantes con otras variables\n",
        "datos = datos.drop(['contexto',         # redundante con clasificacion\n",
        "            'clasificacion',    # sólo tiene una clase\n",
        "            'pais_nac',         # no se considera relevante\n",
        "            'ancestro'],        # no se considera relevante\n",
        "           1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wzY5MqR4IUS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d45ce88-65c0-4795-f4d5-caef8d46daef"
      },
      "source": [
        "# Primero, se ajusta el tipo de las variable\n",
        "datos['edad'] = datos.edad.astype('float').astype('int')\n",
        "filtro_edad = datos['edad'] >= 60\n",
        "filtro_status = datos[\"status\"] != 'Aparecio Muerto'\n",
        "datos = datos[filtro_edad & filtro_status]\n",
        "len(datos)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7841"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jll0ltXeDowr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "outputId": "8a2dff00-8b4e-4fed-ce42-cd9e4054c3a6"
      },
      "source": [
        "# Dejamos sólo las desapariciones ocurridas en Colombia, ya que es menos\n",
        "# probable que conozcamos con certeza el desenlace de la desaparición\n",
        "# cuando ésta ocurrió en otro país y eliminamos después esa columna\n",
        "desap_col = datos[\"pais_desap\"] == 'Colombia'\n",
        "datos = datos[desap_col]\n",
        "datos = datos.drop(\"pais_desap\", 1)\n",
        "len(datos)\n",
        "datos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>status</th>\n",
              "      <th>fecha</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>estado_civil</th>\n",
              "      <th>escolaridad</th>\n",
              "      <th>vulnerabilidad</th>\n",
              "      <th>munic_desap_DANE</th>\n",
              "      <th>dept_desap_DANE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2006-12-12T00:00:00.000</td>\n",
              "      <td>87</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Otro</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>46</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2006-12-25T00:00:00.000</td>\n",
              "      <td>63</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>68</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2006-12-02T00:00:00.000</td>\n",
              "      <td>70</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Casado (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>87</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>2007-01-04T00:00:00.000</td>\n",
              "      <td>62</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Soledad</td>\n",
              "      <td>Atlántico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>108</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2007-01-01T00:00:00.000</td>\n",
              "      <td>74</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Soltero (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Santiago de Cali</td>\n",
              "      <td>Valle Del Cauca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164623</th>\n",
              "      <td>164624</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2021-09-21T00:00:00.000</td>\n",
              "      <td>67</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Casado (a)</td>\n",
              "      <td>Educación media o secundaria alta</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Venecia</td>\n",
              "      <td>Antioquia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164658</th>\n",
              "      <td>164659</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>1989-01-01T00:00:00.000</td>\n",
              "      <td>61</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Casado (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Buenaventura</td>\n",
              "      <td>Valle Del Cauca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164702</th>\n",
              "      <td>164703</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2021-09-26T00:00:00.000</td>\n",
              "      <td>64</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Separado (a), Divorciado (a)</td>\n",
              "      <td>Sin escolaridad</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164729</th>\n",
              "      <td>164730</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>2021-09-26T00:00:00.000</td>\n",
              "      <td>63</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Sin información</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Soacha</td>\n",
              "      <td>Cundinamarca</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164747</th>\n",
              "      <td>164748</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>2021-09-28T00:00:00.000</td>\n",
              "      <td>70</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>Separado (a), Divorciado (a)</td>\n",
              "      <td>Educación básica primaria</td>\n",
              "      <td>Ninguno</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7793 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID         status  ...  munic_desap_DANE  dept_desap_DANE\n",
              "33          34   Desaparecido  ...       Bogotá D.C.       Bogotá D.C\n",
              "45          46   Desaparecido  ...       Bogotá D.C.       Bogotá D.C\n",
              "67          68   Desaparecido  ...       Bogotá D.C.       Bogotá D.C\n",
              "86          87  Aparecio Vivo  ...           Soledad        Atlántico\n",
              "107        108   Desaparecido  ...  Santiago de Cali  Valle Del Cauca\n",
              "...        ...            ...  ...               ...              ...\n",
              "164623  164624   Desaparecido  ...           Venecia        Antioquia\n",
              "164658  164659   Desaparecido  ...      Buenaventura  Valle Del Cauca\n",
              "164702  164703   Desaparecido  ...       Bogotá D.C.       Bogotá D.C\n",
              "164729  164730  Aparecio Vivo  ...            Soacha     Cundinamarca\n",
              "164747  164748   Desaparecido  ...       Bogotá D.C.       Bogotá D.C\n",
              "\n",
              "[7793 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NI4MmMZGSzrq"
      },
      "source": [
        "### **5 Transformación de variables**\n",
        "--- "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McWP1l7XQdi2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f43b9c6e-7c69-4f37-a785-5af0d06e1c1a"
      },
      "source": [
        "# Primero, se verifican los tipos de variable para determinar cuáles hay que\n",
        "# transformar y a qué tipo\n",
        "datos.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7793 entries, 33 to 164747\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   ID                7793 non-null   int64 \n",
            " 1   status            7793 non-null   object\n",
            " 2   fecha             7793 non-null   object\n",
            " 3   edad              7793 non-null   int64 \n",
            " 4   sexo              7793 non-null   object\n",
            " 5   estado_civil      7793 non-null   object\n",
            " 6   escolaridad       7793 non-null   object\n",
            " 7   vulnerabilidad    7793 non-null   object\n",
            " 8   munic_desap_DANE  7793 non-null   object\n",
            " 9   dept_desap_DANE   7793 non-null   object\n",
            "dtypes: int64(2), object(8)\n",
            "memory usage: 669.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eJZrPcq05WM"
      },
      "source": [
        "#### **a. ID**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6GQZhMn1DUh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "002f6a0f-b6b4-4006-b34e-f0725e5c3cca"
      },
      "source": [
        "datos['ID'] = datos.ID.astype('category')\n",
        "datos.ID.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[34, 46, 68, 87, 108, ..., 164624, 164659, 164703, 164730, 164748]\n",
              "Length: 7793\n",
              "Categories (7793, int64): [34, 46, 68, 87, ..., 164659, 164703, 164730, 164748]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vMZUkv8Du4eC"
      },
      "source": [
        "#### **b. Status**\n",
        "Variable de salida."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFNnE02ju_eh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b91205d-40b1-4176-8c18-809927064a2c"
      },
      "source": [
        "datos['status'] = pd.Categorical(datos.status, categories = [\"Aparecio Vivo\",\n",
        "                                                             \"Desaparecido\"],\n",
        "                                 ordered = False)\n",
        "#datos['status'] = datos.status.astype('category')\n",
        "datos.status.value_counts()            # 5166 con status desaparecido y\n",
        "                                       # 2689 con status de aparecido vivo\n",
        "datos.status.cat.categories"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Aparecio Vivo', 'Desaparecido'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCKiHEqH1_9F"
      },
      "source": [
        "#### **c. Fecha**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e965GZ-R2Hnr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6365f004-e6ad-45ed-f407-dd668fd8edc6"
      },
      "source": [
        "datos['fecha'] = pd.to_datetime(datos.fecha, format = '%Y-%m-%d')\n",
        "datos.fecha"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33       2006-12-12\n",
              "45       2006-12-25\n",
              "67       2006-12-02\n",
              "86       2007-01-04\n",
              "107      2007-01-01\n",
              "            ...    \n",
              "164623   2021-09-21\n",
              "164658   1989-01-01\n",
              "164702   2021-09-26\n",
              "164729   2021-09-26\n",
              "164747   2021-09-28\n",
              "Name: fecha, Length: 7793, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2qXE4em4ULX"
      },
      "source": [
        "# Número de días desde el reporte de la desaparición: utilizaremos esta variable,\n",
        "# ya que muchas observaciones tendrán menos de un año desde la desaparición\n",
        "datos['dias'] = datetime.strptime('2021-11-30', '%Y-%m-%d') - datos.fecha\n",
        "datos['dias'] = datos.dias.astype('string').str.replace(' days', '').astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq3X-KCi7XBc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "187d701c-8cb2-462e-9a2b-791ca498e3c6"
      },
      "source": [
        "# Sacamos dos variables de la variable de fecha: año y mes, para poder analizar\n",
        "# su contribución al modelo predictivo por separado.\n",
        "datos['mes'] = datos.fecha.dt.month        \n",
        "datos['año'] = datos.fecha.dt.year         \n",
        "datos.año.value_counts(), datos.mes.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2019    483\n",
              " 2018    462\n",
              " 2012    449\n",
              " 2015    440\n",
              " 2013    428\n",
              "        ... \n",
              " 1937      1\n",
              " 1954      1\n",
              " 1963      1\n",
              " 1967      1\n",
              " 1950      1\n",
              " Name: año, Length: 67, dtype: int64, 1     959\n",
              " 12    683\n",
              " 8     647\n",
              " 7     642\n",
              " 11    632\n",
              " 4     620\n",
              " 10    619\n",
              " 6     617\n",
              " 5     610\n",
              " 3     609\n",
              " 9     584\n",
              " 2     571\n",
              " Name: mes, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oSqx3GKPDvh",
        "outputId": "d18d366e-1fe4-4f2c-ccc5-d138b95b2d39"
      },
      "source": [
        "filtro = (datos['año'] == 2021) & (datos['mes'] >= 7)\n",
        "datos = datos[filtro]\n",
        "len(datos) #93"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMsLWG9aL3gJ"
      },
      "source": [
        "# Podemos eliminar la columna de fecha\n",
        "datos = datos.drop(columns = \"fecha\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-20fAx_EI1c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e8178ed-1eae-4451-bc35-75bc8fcd7bd6"
      },
      "source": [
        "# Volvemos categórica la variable mes\n",
        "datos.mes = datos.mes.astype('category')\n",
        "datos.mes.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7    44\n",
              "9    26\n",
              "8    23\n",
              "Name: mes, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pt6Gq4bv5MHY"
      },
      "source": [
        "#### **d. Sexo**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iy0wgbOq1-fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "743bc558-dfdc-4a1a-e176-3fac6a435393"
      },
      "source": [
        "datos['sexo'] = datos.sexo.astype('category')\n",
        "datos.sexo.value_counts()         # Se observa un enorme disbalance entre hom-\n",
        "                                  # bres y mujeres, ya que hay casi 4x más\n",
        "                                  # hombres que mujeres."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hombre    72\n",
              "Mujer     21\n",
              "Name: sexo, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kz6Val2euQZC"
      },
      "source": [
        "#### **e. Estado civil**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NRLYSUZymDt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acb7f0e0-a849-4171-f484-7462f1c81896"
      },
      "source": [
        "# Aquí podemos tener varias opciones, así que verificamos primero qué valores\n",
        "# únicos hay\n",
        "datos.estado_civil.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sin información', 'Casado (a)', 'Soltero (a)', 'Unión libre',\n",
              "       'Separado (a), Divorciado (a)', 'Viudo (a)'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwBghkDWyuFq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02a76e51-aafd-4260-e358-934654d4242f"
      },
      "source": [
        "# No necesitamos todas las categorías, pues algunas se pueden solapar, como\n",
        "# por ejemplo \"unión libre\" y \"casado\" (relación actual) o \"separado\" y \n",
        "# \"viudo\" (relación pasada), las cuales cumplen la misma función para la idea\n",
        "# que se tiene sobre el rol de esta variable en el pronóstico\n",
        "datos['relacion'] = datos['estado_civil']\\\n",
        ".replace(['Unión libre', 'Casado (a)',\\\n",
        "          'Separado (a), Divorciado (a)', 'Viudo (a)',\\\n",
        "          'Soltero (a)'],\\\n",
        "         ['actual', 'actual', 'pasada', 'pasada', 'ninguna'])\n",
        "datos.relacion.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "actual             37\n",
              "ninguna            29\n",
              "pasada             21\n",
              "Sin información     6\n",
              "Name: relacion, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyhWG0j-MGtp"
      },
      "source": [
        "# Eliminamos la columna de estado civil\n",
        "datos = datos.drop(columns = \"estado_civil\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLH-RMq19qd0"
      },
      "source": [
        "# Volvemos NaN los registros sin información\n",
        "datos.relacion = datos.relacion.replace(\"Sin información\", np.NaN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IBT319_-ozB"
      },
      "source": [
        "# Volvemos categoría esta variable\n",
        "datos.relacion = datos.relacion.astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Y70Xp498RCy"
      },
      "source": [
        "#### **f. Escolaridad**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K67mnSEyITfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44e1d57e-a766-48e0-85b4-5f3d80e10c5d"
      },
      "source": [
        "# Primero, verificamos qué niveles están comprendidos en esta variable\n",
        "datos['escolaridad'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Educación inicial y educación preescolar',\n",
              "       'Educación media o secundaria alta', 'Educación básica primaria',\n",
              "       'Educación básica secundaria o secundaria baja', 'Sin escolaridad',\n",
              "       'Sin información', 'Educación técnica profesional y tecnológica'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3YRfJKMIs42"
      },
      "source": [
        "# Como los niveles educativos tienen una duración más o menos conocida, conver-\n",
        "# tiremos esta variable a cuantitativa, de tal manera que refleje los años de\n",
        "# educación, ya que así puede ser más útil para el modelo. Como es una escala\n",
        "# aproximada (porque no sabemos exactamente los años de escolaridad), utiliza-\n",
        "# remos saltos de 2.5 años, para que todos los niveles queden con la misma\n",
        "# distancia y debido a que un nivel se cimienta sobre el anterior.\n",
        "datos['escolaridad'] = datos.escolaridad.replace({'Sin escolaridad':0})\\\n",
        "                                   .replace({'Educación inicial y educación preescolar':2.5})\\\n",
        "                                   .replace({'Educación básica primaria':5})\\\n",
        "                                   .replace({'Educación básica secundaria o secundaria baja':7.5})\\\n",
        "                                   .replace({'Educación media o secundaria alta':10})\\\n",
        "                                   .replace({'Educación técnica profesional y tecnológica':12.5})\\\n",
        "                                   .replace({'Universitario':15})\\\n",
        "                                   .replace({'Especialización, Maestría o equivalente':17.5})\\\n",
        "                                   .replace({'Doctorado o equivalente':20})\\"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKJaKtsb8Rs8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7119b0f-c1b1-4180-e4e4-983af456b7e9"
      },
      "source": [
        "# Verificamos la frecuencia valores en esta variable\n",
        "datos['escolaridad'].value_counts()        # Hay 2063 registros sin información"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5                  35\n",
              "10                 15\n",
              "Sin información    14\n",
              "2.5                10\n",
              "12.5                8\n",
              "0                   6\n",
              "7.5                 5\n",
              "Name: escolaridad, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiD8inLz5Fdu"
      },
      "source": [
        "# Volvemos NaN los registros sin información\n",
        "datos.escolaridad = datos.escolaridad.replace(\"Sin información\", np.NaN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jV76t0vpLZ0a"
      },
      "source": [
        "#### **g. Factor de vulnerabilidad**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5TA7BcGXGFt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3098fb94-3aea-4243-e67a-3912cc76ef5e"
      },
      "source": [
        "# Al igual que con la variable anterior, verificamos qué niveles están\n",
        "# comprendidos en esta variable\n",
        "datos['vulnerabilidad'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ninguno', 'Sin información', 'Discapacitados (as)',\n",
              "       'Persona adicta a una droga natural o sintética',\n",
              "       'Persona habitante de la calle'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-isM7xDXuJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efac57da-4868-4620-f064-e63bf420b63d"
      },
      "source": [
        "# Como hay tantos y tan variados niveles, ahora verificamos cuántos valores\n",
        "# hay en cada uno para encontrar una manera de convertirlos\n",
        "datos['vulnerabilidad'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ninguno                                           81\n",
              "Sin información                                    6\n",
              "Discapacitados (as)                                3\n",
              "Persona adicta a una droga natural o sintética     2\n",
              "Persona habitante de la calle                      1\n",
              "Name: vulnerabilidad, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_o117XEkY1QE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c8d8396-f4fa-47ee-e548-8a0b8aad6842"
      },
      "source": [
        "# Ya que hay números pequeños en algunos niveles de la variable, será mejor\n",
        "# categorizarla en 2 niveles: sin (0) y con (1) algún factor de vulnerabilidad\n",
        "filtro = (datos.vulnerabilidad != 'Ninguno') &\\\n",
        "(datos.vulnerabilidad != 'Sin información')\n",
        "datos.loc[filtro, 'vulnerabilidad'] = 'Sí'\n",
        "datos.loc[(datos.vulnerabilidad == 'Ninguno'), 'vulnerabilidad'] = 'No'\n",
        "datos['vulnerabilidad'].value_counts()     # Hay 1460 registros sin información"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No                 81\n",
              "Sí                  6\n",
              "Sin información     6\n",
              "Name: vulnerabilidad, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_d_C0SQ9xHA"
      },
      "source": [
        "# Volvemos NaN los registros sin información\n",
        "datos.vulnerabilidad = datos.vulnerabilidad.replace(\"Sin información\", np.NaN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EbkAdtg958C"
      },
      "source": [
        "# Volvemos categoría esta variable\n",
        "datos.vulnerabilidad = datos.vulnerabilidad.astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHt5bY9kLdFV"
      },
      "source": [
        "#### **h. Lugar (municipio y departamento) de desaparición**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eqZafl0XQVa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebb39271-a961-44f0-a77c-13ce9e330043"
      },
      "source": [
        "# Revisamos primero los datos de esta variable: tenemos 635 datos únicos\n",
        "len(datos.munic_desap_DANE.unique())      # == 635\n",
        "sorted(datos.munic_desap_DANE.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Barranquilla',\n",
              " 'Bogotá D.C.',\n",
              " 'Bucaramanga',\n",
              " 'Calamar',\n",
              " 'Cartago',\n",
              " 'Cúcuta',\n",
              " 'Facatativá',\n",
              " 'Ibagué',\n",
              " 'Jamundí',\n",
              " 'La Plata',\n",
              " 'Leticia',\n",
              " 'Macheta',\n",
              " 'Manizales',\n",
              " 'Medellín',\n",
              " 'Neiva',\n",
              " 'Pasto',\n",
              " 'Pereira',\n",
              " 'Rionegro',\n",
              " 'San Cayetano',\n",
              " 'Santa Marta',\n",
              " 'Santiago de Cali',\n",
              " 'Sibaté',\n",
              " 'Soacha',\n",
              " 'Sogamoso',\n",
              " 'Solita',\n",
              " 'Tunja',\n",
              " 'Venecia',\n",
              " 'Villa de Leyva']"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPXr8xaMrR6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "be8bd89d-a67f-4160-86f0-cc14a4353250"
      },
      "source": [
        "# Utilizaremos una base de datos creada a partir de web-scraping\n",
        "# de la página de Wikipedia\n",
        "# (https://es.wikipedia.org/wiki/Municipios_de_Colombia) y sus respectivos\n",
        "# anexos (por ejemplo: https://es.wikipedia.org/wiki/Anexo:Municipios_de_Huila)\n",
        "# gracias a la colaboración con https://github.com/virtualmarioe:\n",
        "municipios = pd.read_csv(\"https://drive.google.com/uc?id=1MWGzeFSmTUhuXLO3dNf-6YmhW-ajpVai\")\n",
        "municipios"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>index</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Departamento</th>\n",
              "      <th>Habitantes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>El Encanto</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>4884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>La Chorrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>3933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>La Pedrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>5125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>La Victoria</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>Leticia</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>41639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1689</th>\n",
              "      <td>1689</td>\n",
              "      <td>15</td>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>258287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690</th>\n",
              "      <td>1690</td>\n",
              "      <td>16</td>\n",
              "      <td>La Candelaria</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>24088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1691</th>\n",
              "      <td>1691</td>\n",
              "      <td>17</td>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>374246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1692</th>\n",
              "      <td>1692</td>\n",
              "      <td>18</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>733859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1693</th>\n",
              "      <td>1693</td>\n",
              "      <td>19</td>\n",
              "      <td>Sumapaz</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>6531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1694 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  index           Municipio Departamento Habitantes\n",
              "0              0      0          El Encanto     Amazonas       4884\n",
              "1              1      1         La Chorrera     Amazonas       3933\n",
              "2              2      2          La Pedrera     Amazonas       5125\n",
              "3              3      3         La Victoria     Amazonas       1115\n",
              "4              4      4             Leticia     Amazonas      41639\n",
              "...          ...    ...                 ...          ...        ...\n",
              "1689        1689     15       Puente Aranda       Bogotá     258287\n",
              "1690        1690     16       La Candelaria       Bogotá      24088\n",
              "1691        1691     17  Rafael Uribe Uribe       Bogotá     374246\n",
              "1692        1692     18      Ciudad Bolívar       Bogotá     733859\n",
              "1693        1693     19             Sumapaz       Bogotá       6531\n",
              "\n",
              "[1694 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBMe2H1OxQoL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c67e8d48-399b-42e8-b228-52629287dc2f"
      },
      "source": [
        "# Limpiamos la base de datos\n",
        "municipios = municipios.drop(columns=['Unnamed: 0', 'index'])\n",
        "municipios"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Departamento</th>\n",
              "      <th>Habitantes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>El Encanto</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>4884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>La Chorrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>3933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>La Pedrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>5125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>La Victoria</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Leticia</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>41639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1689</th>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>258287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690</th>\n",
              "      <td>La Candelaria</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>24088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1691</th>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>374246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1692</th>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>733859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1693</th>\n",
              "      <td>Sumapaz</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>6531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1694 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Municipio Departamento Habitantes\n",
              "0             El Encanto     Amazonas       4884\n",
              "1            La Chorrera     Amazonas       3933\n",
              "2             La Pedrera     Amazonas       5125\n",
              "3            La Victoria     Amazonas       1115\n",
              "4                Leticia     Amazonas      41639\n",
              "...                  ...          ...        ...\n",
              "1689       Puente Aranda       Bogotá     258287\n",
              "1690       La Candelaria       Bogotá      24088\n",
              "1691  Rafael Uribe Uribe       Bogotá     374246\n",
              "1692      Ciudad Bolívar       Bogotá     733859\n",
              "1693             Sumapaz       Bogotá       6531\n",
              "\n",
              "[1694 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lWk84lVzZ1g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3c5401ca-3aee-4a1e-9bcf-491e872a223a"
      },
      "source": [
        "# Hay un espacio en la columna de habitantes, por lo cual debemos eliminarlo\n",
        "municipios.Habitantes[0]            # Lo visualizamos: '4\\xa0884' \n",
        "municipios.Habitantes = municipios.Habitantes.replace(u'\\xa0', '', regex = True)\\\n",
        ".replace('\\.', '', regex = True)\\\n",
        ".replace('\\D', '', regex = True)  # Para borrar espacios donde no hay dígitos\n",
        "municipios"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Departamento</th>\n",
              "      <th>Habitantes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>El Encanto</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>4884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>La Chorrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>3933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>La Pedrera</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>5125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>La Victoria</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Leticia</td>\n",
              "      <td>Amazonas</td>\n",
              "      <td>41639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1689</th>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>258287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690</th>\n",
              "      <td>La Candelaria</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>24088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1691</th>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>374246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1692</th>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>733859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1693</th>\n",
              "      <td>Sumapaz</td>\n",
              "      <td>Bogotá</td>\n",
              "      <td>6531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1694 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Municipio Departamento Habitantes\n",
              "0             El Encanto     Amazonas       4884\n",
              "1            La Chorrera     Amazonas       3933\n",
              "2             La Pedrera     Amazonas       5125\n",
              "3            La Victoria     Amazonas       1115\n",
              "4                Leticia     Amazonas      41639\n",
              "...                  ...          ...        ...\n",
              "1689       Puente Aranda       Bogotá     258287\n",
              "1690       La Candelaria       Bogotá      24088\n",
              "1691  Rafael Uribe Uribe       Bogotá     374246\n",
              "1692      Ciudad Bolívar       Bogotá     733859\n",
              "1693             Sumapaz       Bogotá       6531\n",
              "\n",
              "[1694 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME5fIu1yguSk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d538cf8-9c3d-4445-b2ca-693750c21e47"
      },
      "source": [
        "# Necesitamos obtener la suma total de Bogotá, D.C., ya que está por localidades\n",
        "# pero al intentar convertir a entero la columna hay errores debido a valores\n",
        "# faltantes. Verificamos cuáles son éstos:\n",
        "print(municipios[municipios['Habitantes'].isnull()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Municipio Departamento Habitantes\n",
            "892  Altamira        Huila        NaN\n",
            "893    Garzón        Huila        NaN\n",
            "894   Gigante        Huila        NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYGb6dDShY-_"
      },
      "source": [
        "# Los buscamos manualmente por Google para agregar los valores:\n",
        "municipios.Habitantes[municipios.Municipio == \"Altamira\"] = 4293 # Total 2015\n",
        "municipios.Habitantes[municipios.Municipio == \"Garzón\"] = 96296  # Total 2019\n",
        "municipios.Habitantes[municipios.Municipio == \"Gigante\"] = 34410 # Total 2020"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6jfowqeiJ0j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f104c639-214b-449a-cf29-55f3d4ea801d"
      },
      "source": [
        "# Ahora intentamos nuevamente convertir a entero dicha columna\n",
        "municipios.Habitantes = municipios.Habitantes.astype(int)\n",
        "municipios.Habitantes.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.694000e+03\n",
              "mean     4.052021e+04\n",
              "std      1.258991e+05\n",
              "min      3.740000e+02\n",
              "25%      7.876500e+03\n",
              "50%      1.482300e+04\n",
              "75%      2.773700e+04\n",
              "max      2.457680e+06\n",
              "Name: Habitantes, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5DhdIiHS2UH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c2abcc4-5536-4bea-a3c4-689ceec75b3d"
      },
      "source": [
        "# Añadimos \"Bogotá D.C.\" y su total a la base de datos de municipios\n",
        "total_bog = np.sum(municipios.Habitantes[municipios.Departamento == 'Bogotá'])\n",
        "Bogota = {'Municipio': 'Bogotá D.C.', 'Departamento': 'Bogotá D.C',\n",
        "          'Habitantes': total_bog}\n",
        "municipios = municipios.append(Bogota, ignore_index = True)\n",
        "\n",
        "# Eliminamos las localidades de Bogotá, ya que no las necesitamos\n",
        "municipios = municipios.drop(labels = municipios.index[(municipios.Departamento == \"Bogotá\") &\n",
        "                                                     (municipios.Municipio != \"Bogotá, D.C.\")])\n",
        "municipios.Habitantes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          4884\n",
              "1          3933\n",
              "2          5125\n",
              "3          1115\n",
              "4         41639\n",
              "         ...   \n",
              "1670      37740\n",
              "1671      15886\n",
              "1672      16000\n",
              "1673       4076\n",
              "1694    8076734\n",
              "Name: Habitantes, Length: 1675, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIW_gjmFpdNp"
      },
      "source": [
        "# Crear columna de \"municipio\"\n",
        "datos.insert(8, column = \"municipio\", value = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ai7_1gO7nmO1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "8f0621ab-bf0c-4adc-cee1-e464bfba0202"
      },
      "source": [
        "# Añadimos la información sobre número de habitantes a la columna \"municipio\"\n",
        "for fila in municipios.index:\n",
        "  condicion = (municipios.Municipio[fila] == datos.munic_desap_DANE) &\\\n",
        "  (municipios.Departamento[fila][0:6] == datos.dept_desap_DANE.str.slice(0, 6))\n",
        "  datos.loc[condicion, \"municipio\"] = municipios.Habitantes[fila]\n",
        "datos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>status</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>escolaridad</th>\n",
              "      <th>vulnerabilidad</th>\n",
              "      <th>munic_desap_DANE</th>\n",
              "      <th>dept_desap_DANE</th>\n",
              "      <th>municipio</th>\n",
              "      <th>dias</th>\n",
              "      <th>mes</th>\n",
              "      <th>año</th>\n",
              "      <th>relacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>162460</th>\n",
              "      <td>162461</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>69</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>San Cayetano</td>\n",
              "      <td>Cundinamarca</td>\n",
              "      <td>5348</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162471</th>\n",
              "      <td>162472</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>73</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162483</th>\n",
              "      <td>162484</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>91</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162497</th>\n",
              "      <td>162498</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>76</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>7.5</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>149</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>ninguna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162501</th>\n",
              "      <td>162502</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>61</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164604</th>\n",
              "      <td>164605</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>71</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>Manizales</td>\n",
              "      <td>Caldas</td>\n",
              "      <td>397488</td>\n",
              "      <td>69</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164623</th>\n",
              "      <td>164624</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>67</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Venecia</td>\n",
              "      <td>Antioquia</td>\n",
              "      <td>13231</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164702</th>\n",
              "      <td>164703</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>64</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164729</th>\n",
              "      <td>164730</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>63</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Soacha</td>\n",
              "      <td>Cundinamarca</td>\n",
              "      <td>522442</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164747</th>\n",
              "      <td>164748</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>70</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>63</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID         status  edad    sexo  ...  dias mes   año relacion\n",
              "162460  162461  Aparecio Vivo    69  Hombre  ...   152   7  2021      NaN\n",
              "162471  162472   Desaparecido    73  Hombre  ...   150   7  2021   actual\n",
              "162483  162484  Aparecio Vivo    91  Hombre  ...   150   7  2021   actual\n",
              "162497  162498   Desaparecido    76  Hombre  ...   149   7  2021  ninguna\n",
              "162501  162502  Aparecio Vivo    61  Hombre  ...   152   7  2021   actual\n",
              "...        ...            ...   ...     ...  ...   ...  ..   ...      ...\n",
              "164604  164605   Desaparecido    71  Hombre  ...    69   9  2021   pasada\n",
              "164623  164624   Desaparecido    67   Mujer  ...    70   9  2021   actual\n",
              "164702  164703   Desaparecido    64   Mujer  ...    65   9  2021   pasada\n",
              "164729  164730  Aparecio Vivo    63   Mujer  ...    65   9  2021      NaN\n",
              "164747  164748   Desaparecido    70  Hombre  ...    63   9  2021   pasada\n",
              "\n",
              "[93 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caB5aSmEqizr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42eabf8d-a0bb-43f7-abb4-ccd6525febe5"
      },
      "source": [
        "# Identificamos valores que no hubieran sido encontrados para agregar los\n",
        "# valores de manera manual (27 valores únicos)\n",
        "no_encontrados = set(datos.loc[((datos.municipio == 0) \\\n",
        "           &(datos.munic_desap_DANE != 'Sin información')), \"munic_desap_DANE\"])\n",
        "no_encontrados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Macheta', 'Santiago de Cali'}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFaTDUeJurmL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dfe760e-9b31-4e96-a0e2-9edd4c690e2e"
      },
      "source": [
        "# Notamos que algunos de ellos tienen errores de ortografía o discrepancia con\n",
        "# como aparecen en Wikipedia. Los reemplazamos primero:\n",
        "# Verificamos aquí:\n",
        "# confirmar departamento en caso de municipios homónimos\n",
        "datos.dept_desap_DANE[datos.munic_desap_DANE == 'Santiago de Cali']\n",
        "# verificar si está en la base de datos de municipios\n",
        "municipios.Departamento[municipios.Municipio == 'Cali']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1631    Valle del Cauca\n",
              "Name: Departamento, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAIaFOWP-8cO"
      },
      "source": [
        "# Identificados por cambiar\n",
        "datos.loc[(datos.munic_desap_DANE == 'Macheta'),\\\n",
        "          \"munic_desap_DANE\"] = \"Machetá\"\n",
        "datos.loc[(datos.munic_desap_DANE == 'Santiago de Cali'),\\\n",
        "          \"munic_desap_DANE\"] = \"Cali\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYZqYvzOAN_i"
      },
      "source": [
        "# Nuevamente, creamos el set de no encontrados\n",
        "no_encontrados = datos[(datos.municipio == 0) \\\n",
        "           & (datos.munic_desap_DANE != 'Sin información')]\n",
        "no_encontrados = no_encontrados[[\"munic_desap_DANE\", \"dept_desap_DANE\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-ZMamJhFUuh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "dd46dc51-3c61-4b16-935b-1a67d46fd353"
      },
      "source": [
        "# Rellenamos la base de datos con la información faltante:\n",
        "for fila in municipios.index:\n",
        "  condicion = (municipios.Municipio[fila] == datos.munic_desap_DANE) &\\\n",
        "  (municipios.Departamento[fila] == datos.dept_desap_DANE)\n",
        "  datos.loc[condicion, \"municipio\"] = municipios.Habitantes[fila]\n",
        "datos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>status</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>escolaridad</th>\n",
              "      <th>vulnerabilidad</th>\n",
              "      <th>munic_desap_DANE</th>\n",
              "      <th>dept_desap_DANE</th>\n",
              "      <th>municipio</th>\n",
              "      <th>dias</th>\n",
              "      <th>mes</th>\n",
              "      <th>año</th>\n",
              "      <th>relacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>162460</th>\n",
              "      <td>162461</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>69</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>San Cayetano</td>\n",
              "      <td>Cundinamarca</td>\n",
              "      <td>5348</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162471</th>\n",
              "      <td>162472</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>73</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162483</th>\n",
              "      <td>162484</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>91</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162497</th>\n",
              "      <td>162498</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>76</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>7.5</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>149</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>ninguna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162501</th>\n",
              "      <td>162502</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>61</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164604</th>\n",
              "      <td>164605</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>71</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>Manizales</td>\n",
              "      <td>Caldas</td>\n",
              "      <td>397488</td>\n",
              "      <td>69</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164623</th>\n",
              "      <td>164624</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>67</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Venecia</td>\n",
              "      <td>Antioquia</td>\n",
              "      <td>13231</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164702</th>\n",
              "      <td>164703</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>64</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164729</th>\n",
              "      <td>164730</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>63</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Soacha</td>\n",
              "      <td>Cundinamarca</td>\n",
              "      <td>522442</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164747</th>\n",
              "      <td>164748</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>70</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Bogotá D.C.</td>\n",
              "      <td>Bogotá D.C</td>\n",
              "      <td>8076734</td>\n",
              "      <td>63</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID         status  edad    sexo  ...  dias mes   año relacion\n",
              "162460  162461  Aparecio Vivo    69  Hombre  ...   152   7  2021      NaN\n",
              "162471  162472   Desaparecido    73  Hombre  ...   150   7  2021   actual\n",
              "162483  162484  Aparecio Vivo    91  Hombre  ...   150   7  2021   actual\n",
              "162497  162498   Desaparecido    76  Hombre  ...   149   7  2021  ninguna\n",
              "162501  162502  Aparecio Vivo    61  Hombre  ...   152   7  2021   actual\n",
              "...        ...            ...   ...     ...  ...   ...  ..   ...      ...\n",
              "164604  164605   Desaparecido    71  Hombre  ...    69   9  2021   pasada\n",
              "164623  164624   Desaparecido    67   Mujer  ...    70   9  2021   actual\n",
              "164702  164703   Desaparecido    64   Mujer  ...    65   9  2021   pasada\n",
              "164729  164730  Aparecio Vivo    63   Mujer  ...    65   9  2021      NaN\n",
              "164747  164748   Desaparecido    70  Hombre  ...    63   9  2021   pasada\n",
              "\n",
              "[93 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6gAUt5CGENA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7602c679-9868-4386-94e3-80f0693290b0"
      },
      "source": [
        "# Verificamos por última vez que todos los municipios tengan datos\n",
        "datos.munic_desap_DANE[datos.municipio.isna() == True]      # serie vacía"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: munic_desap_DANE, dtype: object)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxYx_RofGLuy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "1cb15090-3701-40d6-b443-9a6c1ce9774c"
      },
      "source": [
        "# Eliminamos las columnas de municipio y departamento\n",
        "datos = datos.drop(columns = [\"munic_desap_DANE\", \"dept_desap_DANE\"])\n",
        "datos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>status</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>escolaridad</th>\n",
              "      <th>vulnerabilidad</th>\n",
              "      <th>municipio</th>\n",
              "      <th>dias</th>\n",
              "      <th>mes</th>\n",
              "      <th>año</th>\n",
              "      <th>relacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>162460</th>\n",
              "      <td>162461</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>69</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>5348</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162471</th>\n",
              "      <td>162472</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>73</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162483</th>\n",
              "      <td>162484</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>91</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>150</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162497</th>\n",
              "      <td>162498</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>76</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>7.5</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>149</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>ninguna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162501</th>\n",
              "      <td>162502</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>61</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164604</th>\n",
              "      <td>164605</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>71</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>2.5</td>\n",
              "      <td>No</td>\n",
              "      <td>397488</td>\n",
              "      <td>69</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164623</th>\n",
              "      <td>164624</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>67</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>10.0</td>\n",
              "      <td>No</td>\n",
              "      <td>13231</td>\n",
              "      <td>70</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164702</th>\n",
              "      <td>164703</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>64</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164729</th>\n",
              "      <td>164730</td>\n",
              "      <td>Aparecio Vivo</td>\n",
              "      <td>63</td>\n",
              "      <td>Mujer</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>522442</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164747</th>\n",
              "      <td>164748</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>70</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>5.0</td>\n",
              "      <td>No</td>\n",
              "      <td>8076734</td>\n",
              "      <td>63</td>\n",
              "      <td>9</td>\n",
              "      <td>2021</td>\n",
              "      <td>pasada</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID         status  edad    sexo  ...  dias mes   año  relacion\n",
              "162460  162461  Aparecio Vivo    69  Hombre  ...   152   7  2021       NaN\n",
              "162471  162472   Desaparecido    73  Hombre  ...   150   7  2021    actual\n",
              "162483  162484  Aparecio Vivo    91  Hombre  ...   150   7  2021    actual\n",
              "162497  162498   Desaparecido    76  Hombre  ...   149   7  2021   ninguna\n",
              "162501  162502  Aparecio Vivo    61  Hombre  ...   152   7  2021    actual\n",
              "...        ...            ...   ...     ...  ...   ...  ..   ...       ...\n",
              "164604  164605   Desaparecido    71  Hombre  ...    69   9  2021    pasada\n",
              "164623  164624   Desaparecido    67   Mujer  ...    70   9  2021    actual\n",
              "164702  164703   Desaparecido    64   Mujer  ...    65   9  2021    pasada\n",
              "164729  164730  Aparecio Vivo    63   Mujer  ...    65   9  2021       NaN\n",
              "164747  164748   Desaparecido    70  Hombre  ...    63   9  2021    pasada\n",
              "\n",
              "[93 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN08JiDs9lbF"
      },
      "source": [
        "# Volvemos NaN los registros que tienen un valor de 0 en esta variable\n",
        "datos.municipio = datos.municipio.replace(0, np.NaN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72K9HbLE74SP"
      },
      "source": [
        "### **6 Análisis descriptivo exploratorio**\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bYXwO04bBtF"
      },
      "source": [
        "#### **a. Tablas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Dxjx339_R8f6",
        "outputId": "397fff76-1566-4d40-9d16-8d1f937de4a5"
      },
      "source": [
        "# Revisamos los descriptivos de las variables cuantitativas\n",
        "datos.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>edad</th>\n",
              "      <th>escolaridad</th>\n",
              "      <th>municipio</th>\n",
              "      <th>dias</th>\n",
              "      <th>año</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>93.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>9.000000e+01</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>93.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>6.170886</td>\n",
              "      <td>4.754289e+06</td>\n",
              "      <td>114.258065</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.311397</td>\n",
              "      <td>3.509042</td>\n",
              "      <td>3.769760e+06</td>\n",
              "      <td>29.100240</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.348000e+03</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.161218e+05</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>68.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.076734e+06</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>73.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.076734e+06</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>92.000000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>8.076734e+06</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>2021.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            edad  escolaridad     municipio        dias     año\n",
              "count  93.000000    79.000000  9.000000e+01   93.000000    93.0\n",
              "mean   69.000000     6.170886  4.754289e+06  114.258065  2021.0\n",
              "std     7.311397     3.509042  3.769760e+06   29.100240     0.0\n",
              "min    60.000000     0.000000  5.348000e+03   63.000000  2021.0\n",
              "25%    63.000000     5.000000  4.161218e+05   89.000000  2021.0\n",
              "50%    68.000000     5.000000  8.076734e+06  117.000000  2021.0\n",
              "75%    73.000000    10.000000  8.076734e+06  142.000000  2021.0\n",
              "max    92.000000    12.500000  8.076734e+06  152.000000  2021.0"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "k8o_FglbVOnI",
        "outputId": "3f5210a6-9b34-4466-ecd4-e4ac05d29938"
      },
      "source": [
        "# Revisamos los descriptivos de las variables cualitativas (volvemos de tipo\n",
        "# categoría la variable \"factor de vulnerabilidad\", ya que no se toma como tal)\n",
        "datos.describe(include = 'category')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>status</th>\n",
              "      <th>sexo</th>\n",
              "      <th>vulnerabilidad</th>\n",
              "      <th>mes</th>\n",
              "      <th>relacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>87</td>\n",
              "      <td>93</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>93</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>164748</td>\n",
              "      <td>Desaparecido</td>\n",
              "      <td>Hombre</td>\n",
              "      <td>No</td>\n",
              "      <td>7</td>\n",
              "      <td>actual</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>63</td>\n",
              "      <td>72</td>\n",
              "      <td>81</td>\n",
              "      <td>44</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID        status    sexo vulnerabilidad  mes relacion\n",
              "count       93            93      93             87   93       87\n",
              "unique      93             2       2              2    3        3\n",
              "top     164748  Desaparecido  Hombre             No    7   actual\n",
              "freq         1            63      72             81   44       37"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKSdNkHowN4d",
        "outputId": "11d20f9c-3496-4684-a0f0-dd3065495c90"
      },
      "source": [
        "# Porcentaje de datos faltantes\n",
        "porc_faltantes = datos.isnull().sum() * 100 / len(datos)\n",
        "porc_faltantes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                 0.000000\n",
              "status             0.000000\n",
              "edad               0.000000\n",
              "sexo               0.000000\n",
              "escolaridad       15.053763\n",
              "vulnerabilidad     6.451613\n",
              "municipio          3.225806\n",
              "dias               0.000000\n",
              "mes                0.000000\n",
              "año                0.000000\n",
              "relacion           6.451613\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3eXKfw4GRmO"
      },
      "source": [
        "# Sacamos la columna de mes\n",
        "datos = datos.drop('mes', 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpPPfF3gd6Zo"
      },
      "source": [
        "# Sacamos la columnda de año\n",
        "datos = datos.drop('año', 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wy2p-jP6RKl"
      },
      "source": [
        "# Creamos una variable de salida numérica\n",
        "datos['aparecio'] = np.where(datos['status'] == 'Aparecio Vivo', 1, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tc-9BO5VSZgE"
      },
      "source": [
        "# Reseteamos el índice\n",
        "datos = datos.reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaBWOwJUcrhV",
        "outputId": "f2f922de-92f7-43d8-8448-1691bf70a470"
      },
      "source": [
        "# Montamos Google Drive para guardar el archivo\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCxPLXXQdDy0"
      },
      "source": [
        "# Guardamos el archivo\n",
        "#datos.to_csv(\"/content/gdrive/My Drive/Proyecto/unseen.csv\", index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWL2uNNnecV_"
      },
      "source": [
        "#### **b. Gráficos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "az8SjQHlF5Ql",
        "outputId": "4c9abc8f-022f-4a08-fa30-27a79d4258cb"
      },
      "source": [
        "# Verificamos la distribución\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.histplot(datos, x = \"status\", stat = \"percent\", hue = \"status\",\n",
        "             palette = \"BuPu\", shrink = 0.3, legend = False)\n",
        "plt.xlabel(\"Status\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "plt.title(\"Histograma por status\", size = 18)\n",
        "plt.xticks(size = 13)\n",
        "plt.yticks(size = 13)\n",
        "#plt.savefig('hist_status.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0., 10., 20., 30., 40., 50., 60., 70., 80.]),\n",
              " <a list of 9 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "PeKFbZGHKPj5",
        "outputId": "f1f5fdd2-85fd-4238-bbc8-5bc2d4e86d37"
      },
      "source": [
        "# Histograma de edad por status\n",
        "sns.histplot(data = datos, x = 'edad', stat = 'percent', hue = 'status',\n",
        "                palette = \"BuPu\", multiple = \"stack\")\n",
        "plt.xlabel(\"Edad\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "#plt.title(\"Histograma de edad por status\", size = 18)\n",
        "plt.legend(title = \"Estatus\", bbox_to_anchor = (1.4, 1),\n",
        "           labels = datos.status.unique())\n",
        "plt.xticks(size = 13)\n",
        "plt.yticks(size = 13)\n",
        "#plt.savefig('hist_edad.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0.,  5., 10., 15., 20., 25., 30.]),\n",
              " <a list of 7 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "jCdmKQrOtL-S",
        "outputId": "90445caa-f7ce-4f5b-e62b-2a92c3f241f1"
      },
      "source": [
        "# Gráfico de violines de la distribución por edad de los aparecidos divi-\n",
        "# didos por sexo\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.violinplot(x = \"sexo\", y = \"edad\", hue = \"status\",\n",
        "                    data = datos, palette = \"BuPu\", split = True,\n",
        "               saturation = 0.8, inner = \"box\",\n",
        "               order = (\"Mujer\", \"Hombre\"))\n",
        "plt.xlabel(\"Sexo\", size = 15)\n",
        "plt.ylabel(\"Edad\", size = 15)\n",
        "plt.legend(title = \"Status\", bbox_to_anchor = (1.4, 1))\n",
        "plt.title(\"Distribución de edad por sexo y status\", size = 18)\n",
        "plt.xticks(size = 13)\n",
        "plt.yticks(size = 13)\n",
        "#plt.savefig('edad_sexo_status.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 40.,  50.,  60.,  70.,  80.,  90., 100., 110.]),\n",
              " <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "ZlRK-iW7iC-P",
        "outputId": "ac0a5455-eba6-4049-dd72-736e91d543cd"
      },
      "source": [
        "# Gráfico de la distribución por escolaridad\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.boxplot(data = datos, x = \"status\", y = \"escolaridad\", palette = \"BuPu\",\n",
        "            linewidth = 0.7, width = 0.5)\n",
        "plt.xlabel(\"Status\", size = 15)\n",
        "plt.ylabel(\"Escolaridad\", size = 15)\n",
        "plt.title(\"Años de escolaridad por status\", size = 16)\n",
        "plt.yticks(size = 13)\n",
        "plt.xticks(size = 13)\n",
        "#plt.savefig('status_educación.png', dpi = 300, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "wwT8331lfQ3V",
        "outputId": "6e450bb2-ef3a-4483-a040-0c0563e9a1dd"
      },
      "source": [
        "# Status desaparición por estado civil\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.histplot(datos, x = \"relacion\", hue = \"status\", multiple = \"stack\",\n",
        "                 stat = \"percent\", palette = \"BuPu\", shrink = 0.5)\n",
        "plt.xlabel(\"Relación\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "plt.legend(title = \"Status\", bbox_to_anchor = (1.4, 1),\n",
        "           labels = datos.status.unique())\n",
        "plt.title(\"Relación conyugal por status de desaparición\", size = 18)\n",
        "plt.yticks(size = 13)\n",
        "plt.xticks(size = 13)\n",
        "#plt.savefig('sexo_estado_civil.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1, 2], <a list of 3 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "clbJYIt2OmkP",
        "outputId": "e07d63eb-2320-4b52-e501-8eb4476483f8"
      },
      "source": [
        "# Status desaparición por vulnerabilidad\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.histplot(datos, x = \"vulnerabilidad\", hue = \"status\", multiple = \"stack\",\n",
        "                 stat = \"percent\", palette = \"BuPu\", shrink = 0.3)\n",
        "plt.xlabel(\"Vulnerabilidad\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "plt.legend(title = \"Status\", bbox_to_anchor = (1.4, 1),\n",
        "           labels = datos.status.unique())\n",
        "plt.title(\"Vulnerabilidad por status de desaparición\", size = 18)\n",
        "plt.yticks(size = 13)\n",
        "plt.xticks(size = 13)\n",
        "#plt.savefig('vulnerabilidad.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1], <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiBxq2kyYx-5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "8f5f36a2-3176-4c04-f96a-094e025bd779"
      },
      "source": [
        "# Tamaño del municipio\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.histplot(data = datos, x = 'municipio', stat = 'percent', hue = 'status',\n",
        "                palette = \"BuPu\", multiple = \"stack\")\n",
        "plt.xlabel(\"Tamaño del municipio (millones hab.)\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "plt.legend(title = \"Estatus\",\n",
        "           labels = datos.status.unique(), bbox_to_anchor = (1, 1))\n",
        "#plt.title(\"Histograma de municipio y status de desaparición\", size = 18)\n",
        "plt.xticks(size = 8)\n",
        "plt.yticks(size = 13)\n",
        "#plt.savefig('hist_municipio.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0., 10., 20., 30., 40., 50., 60.]),\n",
              " <a list of 7 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "Kq7BWmdy5Lh1",
        "outputId": "70c69a8d-af56-485e-f212-6b58e8a8e5ca"
      },
      "source": [
        "# Finalmente, miramos la distribución por año\n",
        "sns.set(style = \"darkgrid\")\n",
        "sns.histplot(data = datos, x = 'dias', stat = 'percent', hue = 'status',\n",
        "                palette = \"BuPu\", multiple = \"stack\")\n",
        "plt.xlabel(\"Número de días desde el reporte\", size = 15)\n",
        "plt.ylabel(\"Porcentaje\", size = 15)\n",
        "plt.title(\"Distribución de días de desaparición por status\", size = 18)\n",
        "plt.legend(title = \"Status\", bbox_to_anchor = (1.4, 1),\n",
        "           labels = datos.status.unique())\n",
        "plt.xticks(size = 13)\n",
        "plt.yticks(size = 13)\n",
        "#plt.savefig('hist_años.png', dpi = 200, figsize = (8, 11),\n",
        "#            bbox_inches = 'tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0.,  5., 10., 15., 20., 25., 30., 35.]),\n",
              " <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-YMtGHSnPcsB"
      },
      "source": [
        "### Predicción datos no vistos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8qebpA0Pbds",
        "outputId": "4fc3b6d9-0349-4014-81a4-99952a8447d6"
      },
      "source": [
        "# Cargar el modelo desde Google Drive\n",
        "filename = '/content/gdrive/My Drive/Proyecto/finalized_lgbm_model.sav'\n",
        "with open(filename, 'rb') as file:  \n",
        "    pickled_lgbm_model = pickle.load(file)\n",
        "pickled_lgbm_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:333: UserWarning: Trying to unpickle estimator LabelEncoder from version 0.23.2 when using version 1.0.1. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
            "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(random_state=2021, silent='warn')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    }
  ]
}