5_Modelos_de_Clasificacion.ipynb

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    "colab": {
      "name": "5 Modelos de Clasificacion.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPLkfbXTrV58gboTH2N+RYu",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rpasquini/herramientas-econometricas-2022/blob/main/5_Modelos_de_Clasificacion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35RIk2hM17u2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3afe2ed-e08a-4ccc-feda-6d6a6b5eaebd"
      },
      "source": [
        "!pip install geopandas\n",
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting geopandas\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 7.3 MB/s \n",
            "\u001b[?25hCollecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 16.7 MB 412 kB/s \n",
            "\u001b[?25hCollecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 39.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (22.1.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2022.9.24)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gmLupim_U2m"
      },
      "source": [
        "### Extracto de Datos de Goytia, Heikkila, Pasquini (2020)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqpJ8QDJ2EPu"
      },
      "source": [
        "datosghp=pd.read_csv('https://raw.githubusercontent.com/rpasquini/urban-econometrics/master/data/formal%20premia%20data%20extract.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sswLtG3B2ucg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "cf9fc3f8-2839-47fa-9e9c-eb25d2ae8c4a"
      },
      "source": [
        "datosghp"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     hora_c  hora_f  bformal  dconurb1  dconurb2  dconurb3  dmore2000  \\\n",
              "0     14.30   14.55        0         0         0         0          1   \n",
              "1     13.30   13.55        0         0         0         0          1   \n",
              "2     14.25   14.50        0         0         0         0          1   \n",
              "3     11.05   11.30        0         0         0         0          1   \n",
              "4      9.35   10.00        0         0         0         0          1   \n",
              "..      ...     ...      ...       ...       ...       ...        ...   \n",
              "545   10.10   10.50        1         0         0         1          0   \n",
              "546   11.40   12.10        1         0         0         1          0   \n",
              "547   12.10   12.35        1         0         0         1          0   \n",
              "548   11.00   11.30        1         0         0         1          0   \n",
              "549    9.30   10.00        1         0         0         1          0   \n",
              "\n",
              "     dprop_soloviv  dprop_ocupant  dinquilinos  ...  m2  dconexionagua1  \\\n",
              "0                1              0            0  ...   2               0   \n",
              "1                0              0            1  ...   2               0   \n",
              "2                1              0            0  ...   2               0   \n",
              "3                1              0            0  ...   2               0   \n",
              "4                1              0            0  ...   1               0   \n",
              "..             ...            ...          ...  ...  ..             ...   \n",
              "545              0              0            0  ...   1               0   \n",
              "546              0              0            1  ...   1               0   \n",
              "547              0              0            0  ...   2               0   \n",
              "548              0              0            0  ...   2               0   \n",
              "549              0              1            0  ...   2               0   \n",
              "\n",
              "     dconexionagua2  dconexionagua3  dpagaelagua  dcloaca  delectricidad_med  \\\n",
              "0                 0               1          0.0        1                  1   \n",
              "1                 0               1          0.0        1                  1   \n",
              "2                 0               1          0.0        0                  0   \n",
              "3                 0               1          NaN        0                  0   \n",
              "4                 0               1          0.0        0                  0   \n",
              "..              ...             ...          ...      ...                ...   \n",
              "545               1               0          0.0        0                  1   \n",
              "546               1               0          0.0        0                  1   \n",
              "547               1               0          0.0        0                  1   \n",
              "548               1               0          0.0        0                  1   \n",
              "549               1               0          0.0        0                  1   \n",
              "\n",
              "     dconexiongas  alquiler2  ingresohogar2  \n",
              "0               0    12000.0        16500.0  \n",
              "1               0     2000.0        12000.0  \n",
              "2               0     3000.0        15000.0  \n",
              "3               0     6000.0         8000.0  \n",
              "4               0        NaN         6800.0  \n",
              "..            ...        ...            ...  \n",
              "545             0     7000.0        25000.0  \n",
              "546             1     5000.0        10000.0  \n",
              "547             1     5000.0        10000.0  \n",
              "548             1        NaN        14000.0  \n",
              "549             0     2000.0        10000.0  \n",
              "\n",
              "[550 rows x 33 columns]"
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              "      <th></th>\n",
              "      <th>hora_c</th>\n",
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              "      <td>3000.0</td>\n",
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              "      <th>3</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VL1q9uPd8zK7"
      },
      "source": [
        "# Estimación de un Modelo de Elección de \"Ubicación Residencial\" adaptado a una situación de vivir en la formalidad vs. la informalidad. \n",
        "\n",
        "Modelo Propuesto:\n",
        "\n",
        "$P(F=1)_i=\\frac{1}{1+e^{-Z_i}}$ \\\n",
        "donde \n",
        "\n",
        "$Z_i=\\beta_0+\\beta_1IngresoHogar_i+\\beta_1TamañoHogar_i+\\beta_2DSecundarioCompleto_i+\\beta_2DTerciarioCompleto_i+\\epsilon_i$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEtbHUPg8HHm"
      },
      "source": [
        "def logit(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion Logit\n",
        "  La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "  data es la base de datos\n",
        "  \"\"\"\n",
        "  datos=data[lista]\n",
        "  # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "  datos=datos.dropna()\n",
        "  Y=datos[lista[0]]\n",
        "  X=datos[lista[1:]]\n",
        "  X=sm.add_constant(X)\n",
        "  # selecciono el modelo Logit\n",
        "  modelo=sm.Logit(Y,X)\n",
        "  modeloajustado=modelo.fit()\n",
        "  print(modeloajustado.summary())\n",
        "  return modeloajustado"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logit(['bformal', 'ingresohogar2', 'npersonashogar','dedu_seccomp',\t'dedu_ter_o_mas'], datosghp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6FTEfmqRMrH",
        "outputId": "253a7a88-4269-4caf-8658-75ab16657e59"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.665327\n",
            "         Iterations 4\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                bformal   No. Observations:                  501\n",
            "Model:                          Logit   Df Residuals:                      496\n",
            "Method:                           MLE   Df Model:                            4\n",
            "Date:                Fri, 14 Oct 2022   Pseudo R-squ.:                 0.03451\n",
            "Time:                        16:13:16   Log-Likelihood:                -333.33\n",
            "converged:                       True   LL-Null:                       -345.24\n",
            "Covariance Type:            nonrobust   LLR p-value:                 8.645e-05\n",
            "==================================================================================\n",
            "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "const             -0.8477      0.267     -3.176      0.001      -1.371      -0.324\n",
            "ingresohogar2   3.727e-05   1.37e-05      2.713      0.007    1.03e-05    6.42e-05\n",
            "npersonashogar    -0.0141      0.047     -0.299      0.765      -0.106       0.078\n",
            "dedu_seccomp       0.6655      0.211      3.158      0.002       0.252       1.078\n",
            "dedu_ter_o_mas     0.6764      0.371      1.822      0.068      -0.051       1.404\n",
            "==================================================================================\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<statsmodels.discrete.discrete_model.BinaryResultsWrapper at 0x7f98d5af79d0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGBYmgxJ8j5G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f4bfa5e-d9d8-4f1d-be1a-b61cfc6d81c9"
      },
      "source": [
        "modeloajustado=logit(['bformal', 'ingresohogar2', 'npersonashogar','dedu_seccomp',\t'dedu_ter_o_mas'], datosghp)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.665327\n",
            "         Iterations 4\n",
            "                           Logit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                bformal   No. Observations:                  501\n",
            "Model:                          Logit   Df Residuals:                      496\n",
            "Method:                           MLE   Df Model:                            4\n",
            "Date:                Fri, 14 Oct 2022   Pseudo R-squ.:                 0.03451\n",
            "Time:                        16:19:18   Log-Likelihood:                -333.33\n",
            "converged:                       True   LL-Null:                       -345.24\n",
            "Covariance Type:            nonrobust   LLR p-value:                 8.645e-05\n",
            "==================================================================================\n",
            "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "const             -0.8477      0.267     -3.176      0.001      -1.371      -0.324\n",
            "ingresohogar2   3.727e-05   1.37e-05      2.713      0.007    1.03e-05    6.42e-05\n",
            "npersonashogar    -0.0141      0.047     -0.299      0.765      -0.106       0.078\n",
            "dedu_seccomp       0.6655      0.211      3.158      0.002       0.252       1.078\n",
            "dedu_ter_o_mas     0.6764      0.371      1.822      0.068      -0.051       1.404\n",
            "==================================================================================\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwztcxOnAHkh"
      },
      "source": [
        "¿Cuál es la probabilidad de que una familia de que una familia con cuatro integrantes, ingreso del hogar de $14000, y jefe con secundario incompleto viva en la formalidad? \\\n",
        "A mano:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXUlo8sgAczz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f8d2e8df-cfae-4910-ae0e-7610412bc435"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "z0=-0.8477+0.00003727*14000-0.0141*4+0*0.6655+0*0.67\n",
        "f0=1/(1+np.exp(-z0))\n",
        "f0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4055674633633202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxeefQkrCAPv"
      },
      "source": [
        "Usando el metodo predict\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r-yv44dBRlY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e43a1cab-eae8-43c4-ff0b-e7caaaab576d"
      },
      "source": [
        "# Ojo que el 1 es la dummy\n",
        "\n",
        "modeloajustado.predict([1,14000,4,0,0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.4056167])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modeloajustado.predict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjtPbaMJVKLs",
        "outputId": "3c59740c-4f52-4eac-db07-3dfeb6366614"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.43853956, 0.48469442, 0.38444106, 0.5312424 , 0.55819759,\n",
              "       0.4465139 , 0.37682056, 0.4465139 , 0.49400863, 0.5945843 ,\n",
              "       0.36695789, 0.42938369, 0.38777718, 0.33103353, 0.5077234 ,\n",
              "       0.5440064 , 0.41242098, 0.44200804, 0.4034186 , 0.53397274,\n",
              "       0.32175052, 0.39784774, 0.56243046, 0.35948156, 0.59752942,\n",
              "       0.34231386, 0.3248297 , 0.38444106, 0.36810844, 0.52195033,\n",
              "       0.32374511, 0.43283558, 0.5919377 , 0.54051285, 0.35624722,\n",
              "       0.35624722, 0.38228391, 0.55896363, 0.29984187, 0.64912131,\n",
              "       0.45819978, 0.33314339, 0.53907583, 0.4621883 , 0.37682056,\n",
              "       0.60090955, 0.3425019 , 0.41122206, 0.40103843, 0.50332699,\n",
              "       0.41463404, 0.62667389, 0.47532853, 0.34287813, 0.38777718,\n",
              "       0.39112386, 0.62953115, 0.60428009, 0.32382985, 0.40222794,\n",
              "       0.62079669, 0.40332546, 0.34138815, 0.54051285, 0.35302594,\n",
              "       0.51140636, 0.34232761, 0.37682056, 0.36044357, 0.50821347,\n",
              "       0.34605634, 0.43059683, 0.39666259, 0.39112386, 0.34455979,\n",
              "       0.48066626, 0.37023355, 0.62740045, 0.62015623, 0.43507707,\n",
              "       0.57604466, 0.32591617, 0.39448081, 0.43059683, 0.3393394 ,\n",
              "       0.49400863, 0.63818168, 0.63818168, 0.70903917, 0.66468767,\n",
              "       0.51336565, 0.42370893, 0.52299212, 0.64268327, 0.34981796,\n",
              "       0.38777718, 0.39666259, 0.41122206, 0.40222794, 0.57947824,\n",
              "       0.3572054 , 0.36369448, 0.34241475, 0.30975522, 0.44548218,\n",
              "       0.37023355, 0.39329913, 0.38444106, 0.36801842, 0.436294  ,\n",
              "       0.5485299 , 0.41463404, 0.56915575, 0.3412004 , 0.57604466,\n",
              "       0.41463404, 0.52546111, 0.44773746, 0.55549104, 0.4034186 ,\n",
              "       0.66983853, 0.44896167, 0.36589865, 0.36272872, 0.35624722,\n",
              "       0.3425019 , 0.51049396, 0.38111578, 0.40442395, 0.33415778,\n",
              "       0.38777718, 0.36695789, 0.3789658 , 0.35511291, 0.34662352,\n",
              "       0.35729427, 0.33971394, 0.4999394 , 0.38444106, 0.74385991,\n",
              "       0.41122206, 0.34774534, 0.436294  , 0.3648407 , 0.48925389,\n",
              "       0.35398022, 0.74326752, 0.62740045, 0.52299212, 0.7465324 ,\n",
              "       0.45470778, 0.42027589, 0.53000964, 0.56615375, 0.46396457,\n",
              "       0.34981796, 0.50376356, 0.43853956, 0.62740045, 0.39438836,\n",
              "       0.42938369, 0.63492544, 0.4999394 , 0.40222794, 0.55684223,\n",
              "       0.61315792, 0.38111578, 0.62337521, 0.61747778, 0.58853361,\n",
              "       0.35415725, 0.56512785, 0.50218638, 0.5919377 , 0.35076827,\n",
              "       0.46623475, 0.53000964, 0.39112386, 0.51140636, 0.55652183,\n",
              "       0.77228272, 0.43975863, 0.36330804, 0.35948156, 0.42027589,\n",
              "       0.41242098, 0.35948156, 0.5485299 , 0.68041893, 0.37023355,\n",
              "       0.4056167 , 0.52195033, 0.3279241 , 0.55336628, 0.62410434,\n",
              "       0.42148231, 0.37352119, 0.54290841, 0.62196118, 0.36369448,\n",
              "       0.56031262, 0.51277402, 0.56570105, 0.4056167 , 0.4056167 ,\n",
              "       0.4999394 , 0.46046598, 0.57604466, 0.3789658 , 0.53351413,\n",
              "       0.48606171, 0.67735063, 0.67535313, 0.39666259, 0.66358361,\n",
              "       0.45697128, 0.37023355, 0.42148231, 0.46046598, 0.62410434,\n",
              "       0.36810844, 0.40901448, 0.62337521, 0.41685055, 0.66399488,\n",
              "       0.51277402, 0.62196118, 0.36044357, 0.57079695, 0.73845978,\n",
              "       0.43975863, 0.45470778, 0.41685055, 0.41805422, 0.59752942,\n",
              "       0.53014022, 0.59884511, 0.57725299, 0.6068998 , 0.7096797 ,\n",
              "       0.5646749 , 0.47903299, 0.33619116, 0.5792896 , 0.35302594,\n",
              "       0.3393394 , 0.32591617, 0.43853956, 0.62123041, 0.29491359,\n",
              "       0.36272872, 0.32574613, 0.49779425, 0.33508747, 0.42027589,\n",
              "       0.34455979, 0.33103353, 0.53351413, 0.35302594, 0.39003784,\n",
              "       0.36695789, 0.4534808 , 0.41343321, 0.39884849, 0.3393394 ,\n",
              "       0.64603344, 0.55426854, 0.51843738, 0.34455979, 0.44200804,\n",
              "       0.37120765, 0.35094457, 0.40901448, 0.49980834, 0.35302594,\n",
              "       0.44200804, 0.37352119, 0.5485299 , 0.32993835, 0.33619116,\n",
              "       0.3425019 , 0.32901587, 0.5738146 , 0.31064869, 0.35834268,\n",
              "       0.53701531, 0.35425975, 0.52175719, 0.43283558, 0.35624722,\n",
              "       0.51993977, 0.47201257, 0.39112386, 0.34567846, 0.73431397,\n",
              "       0.32175052, 0.57947824, 0.71241245, 0.58853361, 0.51492261,\n",
              "       0.58632207, 0.40103843, 0.37682056, 0.5646749 , 0.52572761,\n",
              "       0.3425019 , 0.44896167, 0.50697635, 0.35415725, 0.52923566,\n",
              "       0.35302594, 0.35302594, 0.38111578, 0.46046598, 0.62079669,\n",
              "       0.33103353, 0.32246659, 0.35624722, 0.3778016 , 0.39547865,\n",
              "       0.33729665, 0.41805422, 0.37789258, 0.33204469, 0.58973169,\n",
              "       0.3921187 , 0.38561304, 0.63619331, 0.64716447, 0.53624278,\n",
              "       0.53455318, 0.38444106, 0.32283161, 0.33103353, 0.37994901,\n",
              "       0.5485299 , 0.37682056, 0.41463404, 0.4776671 , 0.59414   ,\n",
              "       0.32409752, 0.57872098, 0.54051285, 0.4056167 , 0.39448081,\n",
              "       0.39666259, 0.49870199, 0.52773595, 0.45697128, 0.35302594,\n",
              "       0.36810844, 0.39329913, 0.70319785, 0.46396457, 0.66043435,\n",
              "       0.35415725, 0.39112386, 0.5646749 , 0.78015624, 0.36158536,\n",
              "       0.77773272, 0.62410434, 0.55201301, 0.44896167, 0.44896167,\n",
              "       0.36695789, 0.50788898, 0.52923566, 0.57947824, 0.42714935,\n",
              "       0.34232761, 0.50674858, 0.5485299 , 0.41463404, 0.34138815,\n",
              "       0.62410434, 0.41463404, 0.50345804, 0.37245415, 0.34455979,\n",
              "       0.31814302, 0.43283558, 0.33823063, 0.4776671 , 0.39112386,\n",
              "       0.39221096, 0.30869795, 0.57947824, 0.37352119, 0.58214861,\n",
              "       0.33086213, 0.34981796, 0.5895444 , 0.49784786, 0.35624722,\n",
              "       0.58512103, 0.59972195, 0.38561304, 0.58290415, 0.30764269,\n",
              "       0.34736659, 0.39329913, 0.33195886, 0.66983853, 0.63323577,\n",
              "       0.39775503, 0.40003584, 0.35302594, 0.39884849, 0.55201301,\n",
              "       0.32683418, 0.53701531, 0.37352119, 0.35189628, 0.41685055,\n",
              "       0.34138815, 0.55549104, 0.43059683, 0.37023355, 0.56121198,\n",
              "       0.41463404, 0.53974115, 0.38444106, 0.38111578, 0.35939245,\n",
              "       0.47994429, 0.50912603, 0.37023355, 0.35302594, 0.39329913,\n",
              "       0.38994576, 0.77529017, 0.45122023, 0.33103353, 0.42148231,\n",
              "       0.3393394 , 0.46396457, 0.51049396, 0.58556832, 0.34774534,\n",
              "       0.43040706, 0.34567846, 0.39329913, 0.52422676, 0.3393394 ,\n",
              "       0.34662352, 0.42370893, 0.41685055, 0.32382985, 0.36263926,\n",
              "       0.36100722, 0.46046598, 0.57827163, 0.3393394 , 0.43059683,\n",
              "       0.33103353, 0.38777718, 0.55549104, 0.40003584, 0.35624722,\n",
              "       0.39329913, 0.5440064 , 0.53351413, 0.37023355, 0.31939921,\n",
              "       0.56915575, 0.56570105, 0.38111578, 0.36810844, 0.53351413,\n",
              "       0.52650218, 0.33305741, 0.59074158, 0.58512103, 0.38013139,\n",
              "       0.33103353, 0.48376348, 0.52071518, 0.35948156, 0.5485299 ,\n",
              "       0.37565897, 0.40222794, 0.37023355, 0.36810844, 0.41122206,\n",
              "       0.30675401, 0.66602771, 0.37682056, 0.53701531, 0.57649465,\n",
              "       0.37023355])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXDCCtmo-6j4"
      },
      "source": [
        "##  Computando los efectos marginales:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StTEGuW-8rFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dd04543-a666-4cf4-cf55-029862db0bfd"
      },
      "source": [
        "# average of the marginal effects in the data (default) \n",
        "logit_margeff = modeloajustado.get_margeff()\n",
        "print('Marginal effects: ')\n",
        "print(logit_margeff.summary())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Marginal effects: \n",
            "        Logit Marginal Effects       \n",
            "=====================================\n",
            "Dep. Variable:                bformal\n",
            "Method:                          dydx\n",
            "At:                           overall\n",
            "==================================================================================\n",
            "                    dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "ingresohogar2   8.809e-06   3.16e-06      2.792      0.005    2.62e-06     1.5e-05\n",
            "npersonashogar    -0.0033      0.011     -0.299      0.765      -0.025       0.018\n",
            "dedu_seccomp       0.1573      0.048      3.289      0.001       0.064       0.251\n",
            "dedu_ter_o_mas     0.1599      0.087      1.846      0.065      -0.010       0.330\n",
            "==================================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGwd0rYiERbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "72f7607d-597a-4593-e2f4-2f03f63debcf"
      },
      "source": [
        "# average of the marginal effects in the data (default) \n",
        "# Usa at='mean'\n",
        "probit_margeff = modeloajustado.get_margeff(at='mean')\n",
        "print('Marginal effects: ')\n",
        "print(probit_margeff.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Marginal effects: \n",
            "        Logit Marginal Effects       \n",
            "=====================================\n",
            "Dep. Variable:                bformal\n",
            "Method:                          dydx\n",
            "At:                              mean\n",
            "==================================================================================\n",
            "                    dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "ingresohogar2   9.241e-06   3.41e-06      2.712      0.007    2.56e-06    1.59e-05\n",
            "npersonashogar    -0.0035      0.012     -0.299      0.765      -0.026       0.019\n",
            "dedu_seccomp       0.1650      0.052      3.158      0.002       0.063       0.267\n",
            "dedu_ter_o_mas     0.1677      0.092      1.822      0.068      -0.013       0.348\n",
            "==================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datosghp.ingresohogar2.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "IAJnVbJbUlaR",
        "outputId": "ff410538-9604-443e-c7d2-a9cc250267dc"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f98d259dad0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQRTWlvXC6I2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "f795fde5-13ec-47ff-b6e2-134178fd3411"
      },
      "source": [
        "\n",
        "probit_margeff = modeloajustado.get_margeff(atexog={1:14000, 2:4, 3:0, 4:0})\n",
        "print('Marginal effects: ')\n",
        "print(probit_margeff.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Marginal effects: \n",
            "        Logit Marginal Effects       \n",
            "=====================================\n",
            "Dep. Variable:                bformal\n",
            "Method:                          dydx\n",
            "At:                           overall\n",
            "==================================================================================\n",
            "                    dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "ingresohogar2   8.987e-06   3.35e-06      2.680      0.007    2.41e-06    1.56e-05\n",
            "npersonashogar    -0.0034      0.011     -0.299      0.765      -0.026       0.019\n",
            "dedu_seccomp       0.1604      0.049      3.275      0.001       0.064       0.256\n",
            "dedu_ter_o_mas     0.1631      0.088      1.846      0.065      -0.010       0.336\n",
            "==================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLKrhp18_GUg"
      },
      "source": [
        "# Versión Probit\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIplSbtr3MSp"
      },
      "source": [
        "def probit(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion probit\n",
        "  La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "  data es la base de datos\n",
        "  \"\"\"\n",
        "  datos=data[lista]\n",
        "  # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "  datos=datos.dropna()\n",
        "  Y=datos[lista[0]]\n",
        "  X=datos[lista[1:]]\n",
        "  X=sm.add_constant(X)\n",
        "  modelo=sm.Probit(Y,X)\n",
        "  modeloajustado=modelo.fit()\n",
        "  print(modeloajustado.summary())\n",
        "  return modeloajustado"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7VCI9113m4k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "ad74cb6a-e897-46c4-b7cc-3517bc1a3221"
      },
      "source": [
        "modeloajustado=probit(['bformal', 'ingresohogar2', 'npersonashogar','dedu_seccomp',\t'dedu_ter_o_mas'], datosghp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.665310\n",
            "         Iterations 4\n",
            "                          Probit Regression Results                           \n",
            "==============================================================================\n",
            "Dep. Variable:                bformal   No. Observations:                  501\n",
            "Model:                         Probit   Df Residuals:                      496\n",
            "Method:                           MLE   Df Model:                            4\n",
            "Date:                Thu, 23 Jul 2020   Pseudo R-squ.:                 0.03453\n",
            "Time:                        18:28:14   Log-Likelihood:                -333.32\n",
            "converged:                       True   LL-Null:                       -345.24\n",
            "Covariance Type:            nonrobust   LLR p-value:                 8.578e-05\n",
            "==================================================================================\n",
            "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "const             -0.5269      0.164     -3.208      0.001      -0.849      -0.205\n",
            "ingresohogar2   2.306e-05   8.38e-06      2.752      0.006    6.63e-06    3.95e-05\n",
            "npersonashogar    -0.0086      0.029     -0.295      0.768      -0.066       0.048\n",
            "dedu_seccomp       0.4152      0.131      3.166      0.002       0.158       0.672\n",
            "dedu_ter_o_mas     0.4262      0.231      1.849      0.065      -0.026       0.878\n",
            "==================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fycg4Rrj7VJL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "885d98b2-935b-4c56-f41c-0ae9057ca788"
      },
      "source": [
        "probit_margeff = modeloajustado.get_margeff()\n",
        "print('Marginal effects: ')\n",
        "print(probit_margeff.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Marginal effects: \n",
            "       Probit Marginal Effects       \n",
            "=====================================\n",
            "Dep. Variable:                bformal\n",
            "Method:                          dydx\n",
            "At:                           overall\n",
            "==================================================================================\n",
            "                    dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------\n",
            "ingresohogar2   8.799e-06   3.12e-06      2.817      0.005    2.68e-06    1.49e-05\n",
            "npersonashogar    -0.0033      0.011     -0.295      0.768      -0.025       0.018\n",
            "dedu_seccomp       0.1584      0.048      3.269      0.001       0.063       0.253\n",
            "dedu_ter_o_mas     0.1626      0.087      1.869      0.062      -0.008       0.333\n",
            "==================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5PREOazjmUr"
      },
      "source": [
        "# Estimando un Modelo de Localización Residencial en la Ciudad\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KA0mbqQr6z-q"
      },
      "source": [
        "comunas=gpd.GeoDataFrame.from_file(\"https://github.com/rpasquini/econometrics_and_causality/blob/master/comunas.zip?raw=true\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7xVGF6limsA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "2c5ca1af-38b6-45fb-a67a-8aca5cf35710"
      },
      "source": [
        "comunas.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a7a807d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UI-PPXBEiyO5"
      },
      "source": [
        "dataeah=pd.read_csv(\"https://github.com/rpasquini/econometrics_and_causality/blob/master/eah2016.csv?raw=true\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJYnY01qjQ8m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "ebd7c67d-70a8-44dc-e4e3-5303547b390f"
      },
      "source": [
        "dataeah"
      ],
      "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>nhogar</th>\n",
              "      <th>miembro</th>\n",
              "      <th>comuna</th>\n",
              "      <th>dominio</th>\n",
              "      <th>edad</th>\n",
              "      <th>sexo</th>\n",
              "      <th>parentes_2</th>\n",
              "      <th>p5_2</th>\n",
              "      <th>p6_a</th>\n",
              "      <th>p6_b</th>\n",
              "      <th>estado</th>\n",
              "      <th>categori</th>\n",
              "      <th>t13</th>\n",
              "      <th>t14</th>\n",
              "      <th>t18</th>\n",
              "      <th>t28</th>\n",
              "      <th>t29</th>\n",
              "      <th>t29a</th>\n",
              "      <th>t30</th>\n",
              "      <th>sem_hs</th>\n",
              "      <th>t33</th>\n",
              "      <th>t34</th>\n",
              "      <th>t35</th>\n",
              "      <th>t37_cod_2</th>\n",
              "      <th>t37_coda_2</th>\n",
              "      <th>t38</th>\n",
              "      <th>t39</th>\n",
              "      <th>t40</th>\n",
              "      <th>t41_cod_2</th>\n",
              "      <th>t47</th>\n",
              "      <th>t48</th>\n",
              "      <th>t51_bis</th>\n",
              "      <th>i1</th>\n",
              "      <th>i4</th>\n",
              "      <th>i6_3</th>\n",
              "      <th>i10</th>\n",
              "      <th>i11</th>\n",
              "      <th>codioph</th>\n",
              "      <th>ioph_2</th>\n",
              "      <th>...</th>\n",
              "      <th>i3_8x_2</th>\n",
              "      <th>codi3_est</th>\n",
              "      <th>i3_est_2</th>\n",
              "      <th>codi3_otr</th>\n",
              "      <th>i3_otr_2</th>\n",
              "      <th>codnolab</th>\n",
              "      <th>ingnolab_2</th>\n",
              "      <th>coding</th>\n",
              "      <th>ingtot_2</th>\n",
              "      <th>codi_tot</th>\n",
              "      <th>itfb_2</th>\n",
              "      <th>ipcfb_2</th>\n",
              "      <th>e2</th>\n",
              "      <th>e4</th>\n",
              "      <th>e6</th>\n",
              "      <th>e8</th>\n",
              "      <th>e12</th>\n",
              "      <th>e13</th>\n",
              "      <th>e14</th>\n",
              "      <th>edad_30</th>\n",
              "      <th>nivel</th>\n",
              "      <th>aesc</th>\n",
              "      <th>m1</th>\n",
              "      <th>m1_2</th>\n",
              "      <th>m2_anio</th>\n",
              "      <th>m3_anio</th>\n",
              "      <th>tipcob2_2</th>\n",
              "      <th>sn1_1</th>\n",
              "      <th>sn1_7</th>\n",
              "      <th>sn1_2</th>\n",
              "      <th>sn1_3</th>\n",
              "      <th>sn1_4</th>\n",
              "      <th>sn1_5</th>\n",
              "      <th>s8</th>\n",
              "      <th>sn14</th>\n",
              "      <th>sn16</th>\n",
              "      <th>s28</th>\n",
              "      <th>s29</th>\n",
              "      <th>fexp</th>\n",
              "      <th>sexotexto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>18</td>\n",
              "      <td>mujer</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>ocupado</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9700</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1200</td>\n",
              "      <td>...</td>\n",
              "      <td>3000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3000</td>\n",
              "      <td>1</td>\n",
              "      <td>4380</td>\n",
              "      <td>1</td>\n",
              "      <td>4380</td>\n",
              "      <td>4380</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>No corresponde</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Excelente</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>973</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>18</td>\n",
              "      <td>varon</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>inactivo</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>9000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9000</td>\n",
              "      <td>1</td>\n",
              "      <td>9000</td>\n",
              "      <td>1</td>\n",
              "      <td>9000</td>\n",
              "      <td>9000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>No corresponde</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Excelente</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>varon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>inquilinatos hoteles inmuebles tomados</td>\n",
              "      <td>18</td>\n",
              "      <td>mujer</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>ocupado</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9700</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1650</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>780</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>780</td>\n",
              "      <td>1</td>\n",
              "      <td>4830</td>\n",
              "      <td>1</td>\n",
              "      <td>4830</td>\n",
              "      <td>4830</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>No corresponde</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Excelente</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>223</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>18</td>\n",
              "      <td>varon</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>inactivo</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>15000</td>\n",
              "      <td>9</td>\n",
              "      <td>15000</td>\n",
              "      <td>9</td>\n",
              "      <td>15000</td>\n",
              "      <td>15000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>No corresponde</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Excelente</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>421</td>\n",
              "      <td>varon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>18</td>\n",
              "      <td>varon</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>inactivo</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>15000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>15000</td>\n",
              "      <td>1</td>\n",
              "      <td>15000</td>\n",
              "      <td>1</td>\n",
              "      <td>15000</td>\n",
              "      <td>15000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>No corresponde</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Bueno</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>199</td>\n",
              "      <td>varon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14507</th>\n",
              "      <td>5943</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>45</td>\n",
              "      <td>mujer</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ocupado</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9700</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>57314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6300</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6300</td>\n",
              "      <td>9</td>\n",
              "      <td>4900</td>\n",
              "      <td>4900</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Secundario/ medio común</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2012</td>\n",
              "      <td>2012</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Muy bueno</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>242</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14508</th>\n",
              "      <td>5944</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>99</td>\n",
              "      <td>mujer</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>inactivo</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>5000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>21000</td>\n",
              "      <td>9</td>\n",
              "      <td>21000</td>\n",
              "      <td>9</td>\n",
              "      <td>21000</td>\n",
              "      <td>21000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Terciario/ superior no universitario</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Excelente</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>112</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14509</th>\n",
              "      <td>5944</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>55</td>\n",
              "      <td>mujer</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ocupado</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9700</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10000</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10000</td>\n",
              "      <td>9</td>\n",
              "      <td>21000</td>\n",
              "      <td>21000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Primario común</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>54</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>2011</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Regular</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>112</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14510</th>\n",
              "      <td>5945</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>102</td>\n",
              "      <td>varon</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>inactivo</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>7500</td>\n",
              "      <td>9</td>\n",
              "      <td>7500</td>\n",
              "      <td>9</td>\n",
              "      <td>7500</td>\n",
              "      <td>7500</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Secundario/ medio común</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Bueno</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>185</td>\n",
              "      <td>varon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14511</th>\n",
              "      <td>5945</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>resto ciudad</td>\n",
              "      <td>72</td>\n",
              "      <td>mujer</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>ocupado</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>9700</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6000</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3800</td>\n",
              "      <td>1</td>\n",
              "      <td>9800</td>\n",
              "      <td>9</td>\n",
              "      <td>7500</td>\n",
              "      <td>7500</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Secundario/ medio común</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1983</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Regular</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>185</td>\n",
              "      <td>mujer</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14512 rows × 93 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  nhogar  miembro  comuna  ... s28  s29 fexp  sexotexto\n",
              "0         1       1        1      13  ...   2    0  973      mujer\n",
              "1         2       1        1       1  ...   0    0  115      varon\n",
              "2         3       1        1       6  ...   2    0  223      mujer\n",
              "3         4       1        1      14  ...   0    0  421      varon\n",
              "4         5       1        1       2  ...   0    0  199      varon\n",
              "...     ...     ...      ...     ...  ...  ..  ...  ...        ...\n",
              "14507  5943       1        2      10  ...   1    5  242      mujer\n",
              "14508  5944       1        1       1  ...   1    6  112      mujer\n",
              "14509  5944       1        2       1  ...   1    4  112      mujer\n",
              "14510  5945       1        1      13  ...   0    0  185      varon\n",
              "14511  5945       1        2      13  ...   1    4  185      mujer\n",
              "\n",
              "[14512 rows x 93 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQvI22MSll2I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "cb01b53b-054b-454f-ffef-7315953b67db"
      },
      "source": [
        "dataeah.ingtot_2.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a77bc56a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yaow9-oWjtxc"
      },
      "source": [
        "def mnlogit(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion Multinomial Logit\n",
        "  La primera variable de la lista es la dependiente, el resto son las explicativas\n",
        "  data es la base de datos\n",
        "  \"\"\"\n",
        "  datos=data[lista]\n",
        "  # el metodo dropna() me permite eliminar las filas que tienen algun valor missing\n",
        "  datos=datos.dropna()\n",
        "  Y=datos[lista[0]]\n",
        "  X=datos[lista[1:]]\n",
        "  X=sm.add_constant(X)\n",
        "  modelo=sm.MNLogit(Y,X)\n",
        "  modeloajustado=modelo.fit()\n",
        "  print(modeloajustado.summary())\n",
        "  return modeloajustado"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DbfWUZdj8xJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3bf26ccb-d6bc-4e8c-f9a9-2d8db39f6649"
      },
      "source": [
        "modeloajustado=mnlogit(['comuna', 'ingtot_2', 'edad'], dataeah)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 2.620393\n",
            "         Iterations 6\n",
            "                          MNLogit Regression Results                          \n",
            "==============================================================================\n",
            "Dep. Variable:                 comuna   No. Observations:                14512\n",
            "Model:                        MNLogit   Df Residuals:                    14470\n",
            "Method:                           MLE   Df Model:                           28\n",
            "Date:                Thu, 23 Jul 2020   Pseudo R-squ.:                 0.01219\n",
            "Time:                        21:28:04   Log-Likelihood:                -38027.\n",
            "converged:                       True   LL-Null:                       -38496.\n",
            "Covariance Type:            nonrobust   LLR p-value:                1.638e-179\n",
            "==============================================================================\n",
            "  comuna=2       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.0518      0.088    -12.010      0.000      -1.223      -0.880\n",
            "ingtot_2    3.162e-05   3.28e-06      9.634      0.000    2.52e-05    3.81e-05\n",
            "edad           0.0023      0.002      1.134      0.257      -0.002       0.006\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=3       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.5778      0.077     -7.535      0.000      -0.728      -0.427\n",
            "ingtot_2    4.127e-06    3.8e-06      1.085      0.278   -3.33e-06    1.16e-05\n",
            "edad           0.0060      0.002      3.234      0.001       0.002       0.010\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=4       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          0.0184      0.068      0.271      0.786      -0.114       0.151\n",
            "ingtot_2   -2.286e-05   4.19e-06     -5.457      0.000   -3.11e-05   -1.46e-05\n",
            "edad           0.0035      0.002      2.004      0.045    7.61e-05       0.007\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=5       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.0609      0.088    -12.091      0.000      -1.233      -0.889\n",
            "ingtot_2    2.027e-05   3.63e-06      5.579      0.000    1.32e-05    2.74e-05\n",
            "edad           0.0056      0.002      2.708      0.007       0.002       0.010\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=6       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.0415      0.087    -12.040      0.000      -1.211      -0.872\n",
            "ingtot_2    1.856e-05   3.63e-06      5.113      0.000    1.14e-05    2.57e-05\n",
            "edad           0.0070      0.002      3.460      0.001       0.003       0.011\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=7       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.3476      0.074     -4.711      0.000      -0.492      -0.203\n",
            "ingtot_2   -6.473e-06    4.1e-06     -1.580      0.114   -1.45e-05    1.56e-06\n",
            "edad           0.0031      0.002      1.687      0.092      -0.001       0.007\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=8       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          0.2924      0.065      4.514      0.000       0.165       0.419\n",
            "ingtot_2   -3.834e-05   4.47e-06     -8.569      0.000   -4.71e-05   -2.96e-05\n",
            "edad           0.0010      0.002      0.584      0.559      -0.002       0.004\n",
            "------------------------------------------------------------------------------\n",
            "  comuna=9       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.7789      0.083     -9.365      0.000      -0.942      -0.616\n",
            "ingtot_2   -1.837e-05   4.95e-06     -3.713      0.000   -2.81e-05   -8.68e-06\n",
            "edad           0.0074      0.002      3.616      0.000       0.003       0.011\n",
            "------------------------------------------------------------------------------\n",
            " comuna=10       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.2346      0.094    -13.159      0.000      -1.419      -1.051\n",
            "ingtot_2   -6.904e-06   4.87e-06     -1.417      0.157   -1.65e-05    2.65e-06\n",
            "edad           0.0108      0.002      5.004      0.000       0.007       0.015\n",
            "------------------------------------------------------------------------------\n",
            " comuna=11       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.7892      0.113    -15.773      0.000      -2.012      -1.567\n",
            "ingtot_2   -6.745e-07   5.36e-06     -0.126      0.900   -1.12e-05    9.84e-06\n",
            "edad           0.0127      0.003      5.077      0.000       0.008       0.018\n",
            "------------------------------------------------------------------------------\n",
            " comuna=12       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.9248      0.085    -10.829      0.000      -1.092      -0.757\n",
            "ingtot_2    1.106e-05   3.94e-06      2.810      0.005    3.34e-06    1.88e-05\n",
            "edad           0.0051      0.002      2.516      0.012       0.001       0.009\n",
            "------------------------------------------------------------------------------\n",
            " comuna=13       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.1936      0.090    -13.307      0.000      -1.369      -1.018\n",
            "ingtot_2    2.818e-05   3.36e-06      8.382      0.000    2.16e-05    3.48e-05\n",
            "edad           0.0065      0.002      3.143      0.002       0.002       0.010\n",
            "------------------------------------------------------------------------------\n",
            " comuna=14       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.0281      0.087    -11.787      0.000      -1.199      -0.857\n",
            "ingtot_2    3.307e-05   3.25e-06     10.165      0.000    2.67e-05    3.94e-05\n",
            "edad           0.0014      0.002      0.686      0.492      -0.003       0.005\n",
            "------------------------------------------------------------------------------\n",
            " comuna=15       coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.0139      0.087    -11.597      0.000      -1.185      -0.843\n",
            "ingtot_2     9.54e-06   4.03e-06      2.365      0.018    1.63e-06    1.74e-05\n",
            "edad           0.0064      0.002      3.116      0.002       0.002       0.010\n",
            "==============================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xDvr4GYkZwC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5dfe8c5e-8b42-4db9-8741-9e17bcaa5cc5"
      },
      "source": [
        "modeloajustado.predict([1,20000,60])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.09499062, 0.07188986, 0.08289212, 0.07545277, 0.06886398,\n",
              "        0.07374537, 0.07111894, 0.06279312, 0.04695586, 0.04613311,\n",
              "        0.03362737, 0.0639365 , 0.07455247, 0.07169329, 0.06135463]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    }
  ]
}