CEF.ipynb

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      "name": "CEF.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNHlg9zi12YyR/gx6qyUG0B",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rpasquini/herramientas-econometricas-2022/blob/main/CEF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Introducción\n",
        "En este notebook introduciremos algunas de las principales librerías con las que vamos a estar trabajando. Nuestro objetivo inicial es estimar un modelo de regresión simple utilizando datos de alquileres de 2019.\n",
        "\n"
      ],
      "metadata": {
        "id": "ME0820dDfgLT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Donde estamos\n",
        "\n",
        "Nuestro ambiente de trabajo ahora es un *notebook* que permite correr codigo, procesar datos, imprimir gráficos, etc. Estos notebooks en general se conocen como \"Jupyter notebooks\" y tienen como particularidad que el  entorno visual en el que trabajan vive en el navegador. En este momento además, el notebook con el que trabajan \"vive en la nube\" de Google Colaboratory. Utiliza recursos de la nube de Google para procesar los datos, etc. Para trabajar con nuestros propios datos tendremos que hacerlos accesibles a Google Colab (subiendo los datos al Colab o subiendo los datos a otro servidor al cual las librerias que usemos puedan acceder)\n",
        "\n",
        "Comentarios relacionados:\n",
        "\n",
        "- Como dijimos el notebook es solo un entorno visual para trabajar con Python. Proponen celdas de texto, y celdas de codigo. Las de codigo son ejecutables, y muestran los resultados a continuacion. \n",
        "\n",
        "- Trabajar en la nube \"Colab\" es conveniente para una clase. También es posible (y a veces necesario) trabajar con un Jupyter Notebook instalado \"localmente\" en sus computadoras.\n",
        "\n",
        "- El ambiente virtual provisto por Google Colab va a borrar todo el contenido que se aloje en la memoria virtual después de un rato de inactividad. Lo unico que se mantiene es lo que está escrito en el notebook (el codigo y el texto explicativo) y los resultados de la ejecucion de celdas. Esto implica que el notebook debe contener todo el codigo desde la carga de los datos hasta su resultado final para que pueda ser reproducido en cualquier momento.  "
      ],
      "metadata": {
        "id": "gvhiY0nmfj_E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Librerías con las que vamos a trabajar\n",
        "\n",
        "Vamos a arrancar con estas:\n",
        "\n",
        "- Pandas: Una de las principales librerías para trabajar y manipular datos en Python en forma tabular (llamados **dataframes** y usualmente abreviados **df**) \n",
        "- Geopandas: Una libreria que busca ser similar a Pandas pero adaptada adicionalmente a  datos con una dimensión geográfica. A los objetos de geopandas los llamamos **geodataframes**.\n",
        "- StatsModels: Una librería para procesamientos estadísticos.\n",
        "\n",
        "\n",
        "Al respecto del uso de librerías, además debemos tener en cuenta:\n",
        "\n",
        "- En general para usar las librerías tendremos que  instalarlas (si no están previamente instaladas) e importarlas al ambiente de trabajo en el que estemos (siempre).\n",
        "- Los Notebooks de Colab vienen con algunas librerías preinstaladas. Solo será necesario importarlas. Para las que no, será necesario instalarlas ( usando un comando de instalacion llamado *pip*).\n",
        "- Importamos una libreria con el comando *import* seguido del nombre de la libreria. Desde ahi en mas el nombre de la libreria contendrá el contenido de la misma. Además, como parte del import podemos usar \"as\" para definir otro nombre con el que la llamaremos de ahi en mas, por ejemplo:\n",
        "```\n",
        "  import pandas as pd\n",
        "```\n",
        "importará pandas como *pd*.\n",
        "\n"
      ],
      "metadata": {
        "id": "vcsCswnYfqaX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Python\n",
        "Mostramos a continuacion algunas funcionalidades básicas de Python."
      ],
      "metadata": {
        "id": "6JjXbmoZhtDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=10\n",
        "b=30\n",
        "a+b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pGEXZWYh5s6",
        "outputId": "a0c827d8-e190-4ca1-eb1a-ba761718240c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=a+b\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Oz3K2V_iGCt",
        "outputId": "a92a6b60-900b-4283-81fa-ecf6bc380d97"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKbRffkNpDKO"
      },
      "source": [
        "# Una lista\n",
        "listanumeros=[1,2,5]\n",
        "listapalabras=[\"clase\", \"econometria\",\"python\"]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "listanumeros[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMmHc-vu9QHx",
        "outputId": "a9e55561-06b5-4d1d-d926-dc39689b4678"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(listanumeros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brfXk7d6iYdP",
        "outputId": "2e1d44c1-c051-4dec-f847-6948fd025e51"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(listanumeros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hRVI0tCibzc",
        "outputId": "7a41a4bb-c0fe-40ac-a0f7-5664b39dacbf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "listapalabras[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "itpxAR9xieKG",
        "outputId": "2eeba412-8296-415e-f0d5-b15edaa7038f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'python'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Un diccionario\n",
        "midir={'alpha':100, 'gamma':340}\n",
        "midir['alpha']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a44OAoqtiiDZ",
        "outputId": "a7a22928-62a0-4f2a-9ace-dd2b396586af"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importamos libreria pandas y los datos"
      ],
      "metadata": {
        "id": "63ABn7OPizdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "689AU07X58xi"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVDJr3CT6RIf",
        "outputId": "7b93aaaa-b6bd-4ac9-bffc-051b93840b59"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'pandas' from '/usr/local/lib/python3.7/dist-packages/pandas/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('https://github.com/rpasquini/herramientas-econometricas/blob/main/datos/eah2016_individual.csv?raw=true')"
      ],
      "metadata": {
        "id": "MycKDbkz6Zwr"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lF2RjBkX7yzz",
        "outputId": "7be8591f-af6a-4733-93bf-49875c5dedc8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'nhogar', 'miembro', 'comuna', 'dominio', 'edad', 'sexo',\n",
              "       'parentes_2', 'p5_2', 'p6_a', 'p6_b', 'estado', 'categori', 't13',\n",
              "       't14', 't18', 't28', 't29', 't29a', 't30', 'sem_hs', 't33', 't34',\n",
              "       't35', 't37_cod_2', 't37_coda_2', 't38', 't39', 't40', 't41_cod_2',\n",
              "       't47', 't48', 't51_bis', 'i1', 'i4', 'i6_3', 'i10', 'i11', 'codioph',\n",
              "       'ioph_2', 'codioph_neto', 'ioph_neto_2', 'codios', 'ios_2', 'codioa',\n",
              "       'ioa_2', 'codlab', 'inglab_2', 'codi3_1x', 'i3_1x_2', 'codi3_2x',\n",
              "       'i3_2x_2', 'codi3_8x', 'i3_8x_2', 'codi3_est', 'i3_est_2', 'codi3_otr',\n",
              "       'i3_otr_2', 'codnolab', 'ingnolab_2', 'coding', 'ingtot_2', 'codi_tot',\n",
              "       'itfb_2', 'ipcfb_2', 'e2', 'e4', 'e6', 'e8', 'e12', 'e13', 'e14',\n",
              "       'edad_30', 'nivel', 'aesc', 'm1', 'm1_2', 'm2_anio', 'm3_anio',\n",
              "       'tipcob2_2', 'sn1_1', 'sn1_7', 'sn1_2', 'sn1_3', 'sn1_4', 'sn1_5', 's8',\n",
              "       'sn14', 'sn16', 's28', 's29', 'fexp', 'sexotexto', 'inmigrante',\n",
              "       'logingreso', 'dsex'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shwD8IUm8tEg",
        "outputId": "2a0f9387-d66b-4d2c-d005-603d950e927c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96"
            ]
          },
          "metadata": {},
          "execution_count": 14
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    {
      "cell_type": "code",
      "source": [
        "df"
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 696
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        "id": "jfGX0I3J9EQD",
        "outputId": "af8cadec-9d6a-43a7-c8b5-4f7ca2c41d0a"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  nhogar  miembro  comuna                                 dominio  \\\n",
              "0         1       1        1      13                            resto ciudad   \n",
              "1         2       1        1       1                            resto ciudad   \n",
              "2         3       1        1       6  inquilinatos hoteles inmuebles tomados   \n",
              "3         4       1        1      14                            resto ciudad   \n",
              "4         5       1        1       2                            resto ciudad   \n",
              "...     ...     ...      ...     ...                                     ...   \n",
              "14507  5943       1        2      10                            resto ciudad   \n",
              "14508  5944       1        1       1                            resto ciudad   \n",
              "14509  5944       1        2       1                            resto ciudad   \n",
              "14510  5945       1        1      13                            resto ciudad   \n",
              "14511  5945       1        2      13                            resto ciudad   \n",
              "\n",
              "       edad   sexo  parentes_2  p5_2  p6_a  ...  s8 sn14       sn16  s28  s29  \\\n",
              "0        18  mujer           1     6    95  ...   2    0  Excelente    2    0   \n",
              "1        18  varon           1     6    95  ...   2    0  Excelente    0    0   \n",
              "2        18  mujer           1     6    95  ...   2    0  Excelente    2    0   \n",
              "3        18  varon           1     6    95  ...   2    0  Excelente    0    0   \n",
              "4        18  varon           1     6    95  ...   2    0      Bueno    0    0   \n",
              "...     ...    ...         ...   ...   ...  ...  ..  ...        ...  ...  ...   \n",
              "14507    45  mujer           9     3     0  ...   2    0  Muy bueno    1    5   \n",
              "14508    99  mujer           1     4     0  ...   2    0  Excelente    1    6   \n",
              "14509    55  mujer           9     2     0  ...   2    0    Regular    1    4   \n",
              "14510   102  varon           1     4     0  ...   2    0      Bueno    0    0   \n",
              "14511    72  mujer           9     5     0  ...   2    0    Regular    1    4   \n",
              "\n",
              "       fexp  sexotexto  inmigrante  logingreso  dsex  \n",
              "0       973      mujer  nativo arg    8.384804     1  \n",
              "1       115      varon  inmigrante    9.104980     0  \n",
              "2       223      mujer  inmigrante    8.482602     1  \n",
              "3       421      varon  nativo arg    9.615806     0  \n",
              "4       199      varon  nativo arg    9.615806     0  \n",
              "...     ...        ...         ...         ...   ...  \n",
              "14507   242      mujer  inmigrante    8.748305     1  \n",
              "14508   112      mujer  nativo arg    9.952278     1  \n",
              "14509   112      mujer  inmigrante    9.210340     1  \n",
              "14510   185      varon  nativo arg    8.922658     0  \n",
              "14511   185      mujer  inmigrante    9.190138     1  \n",
              "\n",
              "[14512 rows x 96 columns]"
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              "      <th>fexp</th>\n",
              "      <th>sexotexto</th>\n",
              "      <th>inmigrante</th>\n",
              "      <th>logingreso</th>\n",
              "      <th>dsex</th>\n",
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              "      <td>mujer</td>\n",
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              "      <td>varon</td>\n",
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              "      <td>2</td>\n",
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              "      <td>223</td>\n",
              "      <td>mujer</td>\n",
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              "      <td>8.482602</td>\n",
              "      <td>1</td>\n",
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              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.615806</td>\n",
              "      <td>0</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>...</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",
              "      <td>nativo arg</td>\n",
              "      <td>9.615806</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>...</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",
              "      <td>inmigrante</td>\n",
              "      <td>8.748305</td>\n",
              "      <td>1</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>...</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",
              "      <td>nativo arg</td>\n",
              "      <td>9.952278</td>\n",
              "      <td>1</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>...</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",
              "      <td>inmigrante</td>\n",
              "      <td>9.210340</td>\n",
              "      <td>1</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>...</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",
              "      <td>nativo arg</td>\n",
              "      <td>8.922658</td>\n",
              "      <td>0</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>...</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",
              "      <td>inmigrante</td>\n",
              "      <td>9.190138</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14512 rows × 96 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21bbea15-d878-40ce-aefe-4e10ca1636b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21bbea15-d878-40ce-aefe-4e10ca1636b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21bbea15-d878-40ce-aefe-4e10ca1636b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"edad\"]+df[\"p5_2\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KET9T9Gd812k",
        "outputId": "0e968530-b0f3-4b38-eafb-f4a867cefffc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         24\n",
              "1         24\n",
              "2         24\n",
              "3         24\n",
              "4         24\n",
              "        ... \n",
              "14507     48\n",
              "14508    103\n",
              "14509     57\n",
              "14510    106\n",
              "14511     77\n",
              "Length: 14512, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVg9ciQtyMVj",
        "outputId": "5e30417d-44bf-49d6-c399-66ed0a0b8191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "# dataframe\n",
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  nhogar  miembro  comuna                                 dominio  edad  \\\n",
              "0   1       1        1      13                            resto ciudad    18   \n",
              "1   2       1        1       1                            resto ciudad    18   \n",
              "2   3       1        1       6  inquilinatos hoteles inmuebles tomados    18   \n",
              "3   4       1        1      14                            resto ciudad    18   \n",
              "4   5       1        1       2                            resto ciudad    18   \n",
              "\n",
              "    sexo  parentes_2  p5_2  p6_a  ...  s8 sn14       sn16  s28  s29  fexp  \\\n",
              "0  mujer           1     6    95  ...   2    0  Excelente    2    0   973   \n",
              "1  varon           1     6    95  ...   2    0  Excelente    0    0   115   \n",
              "2  mujer           1     6    95  ...   2    0  Excelente    2    0   223   \n",
              "3  varon           1     6    95  ...   2    0  Excelente    0    0   421   \n",
              "4  varon           1     6    95  ...   2    0      Bueno    0    0   199   \n",
              "\n",
              "   sexotexto  inmigrante  logingreso  dsex  \n",
              "0      mujer  nativo arg    8.384804     1  \n",
              "1      varon  inmigrante    9.104980     0  \n",
              "2      mujer  inmigrante    8.482602     1  \n",
              "3      varon  nativo arg    9.615806     0  \n",
              "4      varon  nativo arg    9.615806     0  \n",
              "\n",
              "[5 rows x 96 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20c66036-cee4-403b-9ba8-0f4f8666bca7\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
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              "  <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",
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              "      <th>sexo</th>\n",
              "      <th>parentes_2</th>\n",
              "      <th>p5_2</th>\n",
              "      <th>p6_a</th>\n",
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              "      <th>s8</th>\n",
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              "      <th>sn16</th>\n",
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              "      <th>sexotexto</th>\n",
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              "      <th>logingreso</th>\n",
              "      <th>dsex</th>\n",
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              "      <th>0</th>\n",
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              "      <td>95</td>\n",
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              "      <td>2</td>\n",
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              "      <td>varon</td>\n",
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              "    <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>...</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",
              "      <td>inmigrante</td>\n",
              "      <td>8.482602</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "      <td>14</td>\n",
              "      <td>resto ciudad</td>\n",
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              "      <td>0</td>\n",
              "      <td>421</td>\n",
              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.615806</td>\n",
              "      <td>0</td>\n",
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              "    <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",
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              "      <td>95</td>\n",
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              "      <td>0</td>\n",
              "      <td>199</td>\n",
              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.615806</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 96 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20c66036-cee4-403b-9ba8-0f4f8666bca7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20c66036-cee4-403b-9ba8-0f4f8666bca7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-20c66036-cee4-403b-9ba8-0f4f8666bca7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Dg-fDZvspaa",
        "outputId": "690e3eca-470e-43dd-d882-23230cfa6208",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14512, 96)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRdSl3qaqb4M",
        "outputId": "4ea6a5ff-d2b5-495e-b0a5-2b13e103d0c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'nhogar', 'miembro', 'comuna', 'dominio', 'edad', 'sexo',\n",
              "       'parentes_2', 'p5_2', 'p6_a', 'p6_b', 'estado', 'categori', 't13',\n",
              "       't14', 't18', 't28', 't29', 't29a', 't30', 'sem_hs', 't33', 't34',\n",
              "       't35', 't37_cod_2', 't37_coda_2', 't38', 't39', 't40', 't41_cod_2',\n",
              "       't47', 't48', 't51_bis', 'i1', 'i4', 'i6_3', 'i10', 'i11', 'codioph',\n",
              "       'ioph_2', 'codioph_neto', 'ioph_neto_2', 'codios', 'ios_2', 'codioa',\n",
              "       'ioa_2', 'codlab', 'inglab_2', 'codi3_1x', 'i3_1x_2', 'codi3_2x',\n",
              "       'i3_2x_2', 'codi3_8x', 'i3_8x_2', 'codi3_est', 'i3_est_2', 'codi3_otr',\n",
              "       'i3_otr_2', 'codnolab', 'ingnolab_2', 'coding', 'ingtot_2', 'codi_tot',\n",
              "       'itfb_2', 'ipcfb_2', 'e2', 'e4', 'e6', 'e8', 'e12', 'e13', 'e14',\n",
              "       'edad_30', 'nivel', 'aesc', 'm1', 'm1_2', 'm2_anio', 'm3_anio',\n",
              "       'tipcob2_2', 'sn1_1', 'sn1_7', 'sn1_2', 'sn1_3', 'sn1_4', 'sn1_5', 's8',\n",
              "       'sn14', 'sn16', 's28', 's29', 'fexp', 'sexotexto', 'inmigrante',\n",
              "       'logingreso', 'dsex'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"ingtot_2\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hu9ltfH-fih",
        "outputId": "78b117c8-7dc7-488e-88c7-a154c8f5cc00"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         4380\n",
              "1         9000\n",
              "2         4830\n",
              "3        15000\n",
              "4        15000\n",
              "         ...  \n",
              "14507     6300\n",
              "14508    21000\n",
              "14509    10000\n",
              "14510     7500\n",
              "14511     9800\n",
              "Name: ingtot_2, Length: 14512, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.ingtot_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9-SqtyT-n7d",
        "outputId": "0be3ae8e-6778-46bb-9723-12464634d94f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         4380\n",
              "1         9000\n",
              "2         4830\n",
              "3        15000\n",
              "4        15000\n",
              "         ...  \n",
              "14507     6300\n",
              "14508    21000\n",
              "14509    10000\n",
              "14510     7500\n",
              "14511     9800\n",
              "Name: ingtot_2, Length: 14512, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"ingtot_2\"].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "A1uZHVZ-_Bw4",
        "outputId": "22063d57-a29f-4af3-974e-ac8475cc257b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41ca64b50>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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",
      "source": [
        "df[\"ingtot_2\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sxiltytn_gQM",
        "outputId": "c724a9c1-687f-4339-8bf9-9389f528a64c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     14512.000000\n",
              "mean      10386.681850\n",
              "std       13396.598595\n",
              "min           0.000000\n",
              "25%           0.000000\n",
              "50%        7000.000000\n",
              "75%       15000.000000\n",
              "max      276000.000000\n",
              "Name: ingtot_2, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"ingtot_2\"]==0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5oXzqVLi9mZ",
        "outputId": "76faa9c5-0128-4c1d-8587-946c4e8abf3a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "14507    False\n",
              "14508    False\n",
              "14509    False\n",
              "14510    False\n",
              "14511    False\n",
              "Name: ingtot_2, Length: 14512, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "2FBrRkm4_3jr"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ricardo2=np.log(df[\"ingtot_2\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB6HsaeaAI6q",
        "outputId": "3e01304e-47fb-4156-cfb9-8767bacf10df"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['logingreso']=np.log(df[\"ingtot_2\"])"
      ],
      "metadata": {
        "id": "5RFLZnumAkF3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['logingreso']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBkpUm1hAwnV",
        "outputId": "cb71afa8-9e08-4398-e980-9af2318fa400"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        8.384804\n",
              "1        9.104980\n",
              "2        8.482602\n",
              "3        9.615805\n",
              "4        9.615805\n",
              "           ...   \n",
              "14507    8.748305\n",
              "14508    9.952278\n",
              "14509    9.210340\n",
              "14510    8.922658\n",
              "14511    9.190138\n",
              "Name: logingreso, Length: 14512, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.loc[df['ingtot_2']!=0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "-V_RdphgjLAy",
        "outputId": "41f3db7f-d71f-4df6-c349-c49e7a7a2df6"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id  nhogar  miembro  comuna                                 dominio  \\\n",
              "0         1       1        1      13                            resto ciudad   \n",
              "1         2       1        1       1                            resto ciudad   \n",
              "2         3       1        1       6  inquilinatos hoteles inmuebles tomados   \n",
              "3         4       1        1      14                            resto ciudad   \n",
              "4         5       1        1       2                            resto ciudad   \n",
              "...     ...     ...      ...     ...                                     ...   \n",
              "14507  5943       1        2      10                            resto ciudad   \n",
              "14508  5944       1        1       1                            resto ciudad   \n",
              "14509  5944       1        2       1                            resto ciudad   \n",
              "14510  5945       1        1      13                            resto ciudad   \n",
              "14511  5945       1        2      13                            resto ciudad   \n",
              "\n",
              "       edad   sexo  parentes_2  p5_2  p6_a  ...  s28 s29  fexp  sexotexto  \\\n",
              "0        18  mujer           1     6    95  ...    2   0   973      mujer   \n",
              "1        18  varon           1     6    95  ...    0   0   115      varon   \n",
              "2        18  mujer           1     6    95  ...    2   0   223      mujer   \n",
              "3        18  varon           1     6    95  ...    0   0   421      varon   \n",
              "4        18  varon           1     6    95  ...    0   0   199      varon   \n",
              "...     ...    ...         ...   ...   ...  ...  ...  ..   ...        ...   \n",
              "14507    45  mujer           9     3     0  ...    1   5   242      mujer   \n",
              "14508    99  mujer           1     4     0  ...    1   6   112      mujer   \n",
              "14509    55  mujer           9     2     0  ...    1   4   112      mujer   \n",
              "14510   102  varon           1     4     0  ...    0   0   185      varon   \n",
              "14511    72  mujer           9     5     0  ...    1   4   185      mujer   \n",
              "\n",
              "       inmigrante  logingreso  dsex  dmujer  dinmigrante  dmujerinmigrante  \n",
              "0      nativo arg    8.384804     1     1.0            0               0.0  \n",
              "1      inmigrante    9.104980     0     0.0            1               0.0  \n",
              "2      inmigrante    8.482602     1     1.0            1               1.0  \n",
              "3      nativo arg    9.615805     0     0.0            0               0.0  \n",
              "4      nativo arg    9.615805     0     0.0            0               0.0  \n",
              "...           ...         ...   ...     ...          ...               ...  \n",
              "14507  inmigrante    8.748305     1     1.0            1               1.0  \n",
              "14508  nativo arg    9.952278     1     1.0            0               0.0  \n",
              "14509  inmigrante    9.210340     1     1.0            1               1.0  \n",
              "14510  nativo arg    8.922658     0     0.0            0               0.0  \n",
              "14511  inmigrante    9.190138     1     1.0            1               1.0  \n",
              "\n",
              "[10113 rows x 99 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe2dabcc-3166-4eb6-8cda-1ea70c58ffe2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</th>\n",
              "      <th>s28</th>\n",
              "      <th>s29</th>\n",
              "      <th>fexp</th>\n",
              "      <th>sexotexto</th>\n",
              "      <th>inmigrante</th>\n",
              "      <th>logingreso</th>\n",
              "      <th>dsex</th>\n",
              "      <th>dmujer</th>\n",
              "      <th>dinmigrante</th>\n",
              "      <th>dmujerinmigrante</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>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>973</td>\n",
              "      <td>mujer</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>8.384804</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>varon</td>\n",
              "      <td>inmigrante</td>\n",
              "      <td>9.104980</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>223</td>\n",
              "      <td>mujer</td>\n",
              "      <td>inmigrante</td>\n",
              "      <td>8.482602</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>421</td>\n",
              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.615805</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>199</td>\n",
              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.615805</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>242</td>\n",
              "      <td>mujer</td>\n",
              "      <td>inmigrante</td>\n",
              "      <td>8.748305</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>112</td>\n",
              "      <td>mujer</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>9.952278</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>112</td>\n",
              "      <td>mujer</td>\n",
              "      <td>inmigrante</td>\n",
              "      <td>9.210340</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>185</td>\n",
              "      <td>varon</td>\n",
              "      <td>nativo arg</td>\n",
              "      <td>8.922658</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>185</td>\n",
              "      <td>mujer</td>\n",
              "      <td>inmigrante</td>\n",
              "      <td>9.190138</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10113 rows × 99 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe2dabcc-3166-4eb6-8cda-1ea70c58ffe2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe2dabcc-3166-4eb6-8cda-1ea70c58ffe2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fe2dabcc-3166-4eb6-8cda-1ea70c58ffe2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['logingreso'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "KT2pn24yAymQ",
        "outputId": "6195246f-1e57-431c-a63b-fec4d1a0dd8a"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4010ef7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3KSrbYBrs-v",
        "outputId": "eab711ea-41ee-4950-f6ca-00f58b6352c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "df['logingreso'].hist()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41c4dd4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "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",
      "source": [
        "df[\"ingtot_2\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyVIFtkVA54r",
        "outputId": "2d15f55e-a26f-48f0-d246-2d6a2dc6048c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14904.729259369129"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"sexo\"]==\"mujer\"][\"ingtot_2\"].mean()"
      ],
      "metadata": {
        "id": "e_ckXjiBMNll",
        "outputId": "c1aa702c-d472-48fc-9c1a-8dcabec2880f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13037.54996243426"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"sexo\"]==\"varon\"][\"ingtot_2\"].mean()"
      ],
      "metadata": {
        "id": "BuoLNH3RNGGv",
        "outputId": "b33f0ae2-d602-4dc3-bbb7-0742d3e9d806",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16980.49926915849"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# histograma de logingreso pero solo ingresos>0\n",
        "\n",
        "df.loc[df[\"ingtot_2\"]>0][\"logingreso\"].hist()"
      ],
      "metadata": {
        "id": "WNp2on6RO_Mv",
        "outputId": "706639e8-4cd4-45dd-b9ff-8e36ef618230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe401075f10>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MbllPx4OPy-3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"sexo\"]==\"mujer\"][\"logingreso\"].mean()"
      ],
      "metadata": {
        "id": "G3nERYECN0Z5",
        "outputId": "24386748-5b01-4293-be99-4aab2cb5fcec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.136385530108724"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"sexo\"]==\"varon\"]['logingreso'].mean()"
      ],
      "metadata": {
        "id": "QuEZXuHBN5Df",
        "outputId": "3be1055a-952b-40b4-a62b-f9927cb537bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.444136001549921"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qrHGDQbt-nQ",
        "outputId": "2db0716d-762a-4a3e-b97f-cc06ee3af690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "df.logingreso.hist()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41cb07610>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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AHwD/NHQT8xn6xyHJ24D9VXXn0L0MaA3wBuDTVfV64Gl+9f+k/3/aeestjN4AfwM4KcnvD9vV8Gp0PXi314Qn+UtgDvjC0L3MZ+gfnzcDb0/yMHAtcHaSfxi2pYnbC+ytqtvb/PWM3gR68rvA96vqR1X1v8CXgd8euKeh/DDJqQDt5/6B+xlEkouBtwEX1Qr7MpShfxyq6rKqWl9V04w+uLulqro6wquqx4FHk7yylc6hv1tl/wA4K8mLk4TRGHT1YfY8NwJb2/RW4IYBexlEks2MTvm+vap+OnQ/h1pxd9nUqvTHwBfa/ZQeAt43cD8TVVW3J7ke+A6jP+fvYgV/DX+5JPkiMAOckmQvcDlwJXBdkkuAR4B3Ddfh+B1hDC4DXgjsHh0DcFtV/dFgTR7C2zBIUkc8vSNJHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkf+D0UeMA+7LeDAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzXSGe89waHl",
        "outputId": "0db5c34a-c973-40bc-d52e-726bf573f65f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "# pueden agrandar el grafico usando lo siguiente:\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams[\"figure.figsize\"] = (6,6)\n",
        "df.logingreso.hist(bins=20)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41c3eb2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xhj015Wy5To",
        "outputId": "aa7cb320-6f3e-41ee-bfcb-2bf6ec3df69d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "df.logingreso.hist(cumulative=True, density=1, bins=100)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe401020c10>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column='logingreso',by='sexo')"
      ],
      "metadata": {
        "id": "nFXMUKiES6-D",
        "outputId": "9a662040-b5e7-4d46-a51e-035f8324a3ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe400d989d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe400cc1e90>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1JUfBK01C_X"
      },
      "source": [
        "# \"Filtrando\" el Dataframe \n",
        "Seleccionamos las filas del dataframe en base a una condicion logica usando el método .loc \\\\\n",
        "Dentro del .loc tenemos que especificar una condición lógica\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eU8hNrV40014"
      },
      "source": [
        "Aqui van algunos ejemplos mas sobre como filtrar el dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e-8kinTxgNS",
        "outputId": "5c1accca-509c-4ece-8a22-dd70738f1bfc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Ejemplo: promedio de logingreso de personas mayores a 40 años\n",
        "\n",
        "df.loc[df.edad>=40].logingreso.mean()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.35795335849025"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33waEOd6yC2p",
        "outputId": "bb323043-8854-4110-898b-c10b0fc43d59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Ejemplo: promedio de logingreso de varones y mayores a 40 años\n",
        "\n",
        "df.loc[(df.sexo=='varon') & (df.edad>=40)].logingreso.mean()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.550831881911979"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3j6zSw4zW1O",
        "outputId": "2a9466d4-21b3-4503-e115-5e5be61bdfe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Ejemplo: promedio de logingreso de varones o mayores a 40 años\n",
        "df.loc[(df.sexo=='varon') | (df.edad>=40) ].logingreso.mean()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.343912623487702"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnSuc-vkcgmU"
      },
      "source": [
        "## Filtrando el dataframe\n",
        "\n",
        "\n",
        "Para escribir una condicion recuerden que pueden utilizar los siguientes lógicos:\n",
        "\n",
        "==    para chequear la  condicion \"equivale a\" o \"igual a\" va doble igual\n",
        "\n",
        "!= distinto a\n",
        " \n",
        "\\> mayor a \\\\\n",
        "\n",
        "\\> = mayor o igual a\n",
        "\n",
        "< menor a\n",
        "\n",
        "<= menor o igual a\n",
        "\n",
        "En el caso de querer usar dos condiciones o más:\n",
        "\n",
        "(esta condicion) & (esta condicion) Se cumple esta condicion Y esta otra\n",
        "\n",
        "(esta condicion) | (esta condicion) Se cumple esta condicion O esta otra\n",
        "\n",
        "Recuerden que las condiciones tiene que ir en parentesis!\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pImuoa1auWgA"
      },
      "source": [
        "# Estimando el CEF\n",
        "\n",
        "Vamos a explorar ahore el ingreso esperado de varones y mujeres. De acuerdo al modelo, nos interesa conocer:\n",
        "\n",
        "$$E[y|sexo=\\text{varon}]$$\n",
        "\n",
        "y\n",
        "\n",
        "$$E[y|sexo=\\text{mujer}]$$\n",
        "\n",
        "Estimamos cada una de estas esperanzas condicionales con el promedio del subgrupo de observaciones respectivo.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34G8nX_ow5ve",
        "outputId": "5e7020c6-3e4f-47e3-cb9d-d3dee3892eb2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[df.sexo=='varon'].logingreso.mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.444136001549921"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKqmxGt0uces",
        "outputId": "882fe1d4-d98b-4dc0-974d-e660e9b0ed9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[df.sexo=='mujer'].logingreso.mean()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.136385530108724"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EveVpZBc3Aq1"
      },
      "source": [
        "# Un metodo alternativo para contruir medias por grupos usando groupby"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKqzX__JrjxC",
        "outputId": "24ca03e2-7e50-40c0-f5c8-ede1870ca0df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "df.groupby(by='sexo').mean()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                id    nhogar   miembro    comuna       edad  parentes_2  \\\n",
              "sexo                                                                      \n",
              "mujer  3108.302404  1.007137  1.685763  7.196657  49.900826    1.994553   \n",
              "varon  2854.199833  1.010232  1.515765  7.331593  46.261015    1.721445   \n",
              "\n",
              "           p5_2      p6_a      p6_b  categori  ...        s8      sn14  \\\n",
              "sexo                                           ...                       \n",
              "mujer  3.250563  6.740609  4.709992  1.769910  ...  1.904207  0.182757   \n",
              "varon  3.002506  6.662560  4.410942  2.170182  ...  1.928169  0.125496   \n",
              "\n",
              "            s28       s29        fexp  logingreso  dsex  dmujer  dinmigrante  \\\n",
              "sexo                                                                           \n",
              "mujer  1.325507  1.647446  212.109692    9.136386   1.0     1.0     0.270098   \n",
              "varon  0.000000  0.000000  204.478806    9.444136   0.0     0.0     0.250365   \n",
              "\n",
              "       dmujerinmigrante  \n",
              "sexo                     \n",
              "mujer          0.270098  \n",
              "varon          0.000000  \n",
              "\n",
              "[2 rows x 90 columns]"
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              "      <th>id</th>\n",
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              "      <td>1.904207</td>\n",
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              "      <td>0.270098</td>\n",
              "      <td>0.270098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>varon</th>\n",
              "      <td>2854.199833</td>\n",
              "      <td>1.010232</td>\n",
              "      <td>1.515765</td>\n",
              "      <td>7.331593</td>\n",
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              "      <td>1.721445</td>\n",
              "      <td>3.002506</td>\n",
              "      <td>6.662560</td>\n",
              "      <td>4.410942</td>\n",
              "      <td>2.170182</td>\n",
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              "      <td>1.928169</td>\n",
              "      <td>0.125496</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>204.478806</td>\n",
              "      <td>9.444136</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfporgrupo=df.groupby(by=['sexo','aesc'],as_index=False)['logingreso'].mean()"
      ],
      "metadata": {
        "id": "G5lmz1J6UD_5"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Como hacer un grafico crossplot\n",
        "\n",
        "primergrafico=dfporgrupo.loc[(dfporgrupo['aesc']!=99)& (dfporgrupo['sexo']=='varon')].plot(x='aesc',y='logingreso', style='o')\n",
        "dfporgrupo.loc[(dfporgrupo['aesc']!=99) & (dfporgrupo['sexo']=='mujer')].plot(x='aesc',y='logingreso', style='o', ax=primergrafico)\n"
      ],
      "metadata": {
        "id": "iZz19OpTV6xy",
        "outputId": "669d15b8-5b9b-43ce-fdd2-a34849ad894d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe40093d850>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCC1p-dMq9Jl",
        "outputId": "636ef825-78b2-4916-88a0-c6682a683629",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.groupby(by='sexo')['logingreso'].mean()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sexo\n",
              "mujer    9.136386\n",
              "varon    9.444136\n",
              "Name: logingreso, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "doYyzTOr3Omu"
      },
      "source": [
        "# Ejemplo más avanzado: creando una tabla de promedios y luego grafico las funciones CEF segun sexo y escolaridad"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ebv_DeCasS-W"
      },
      "source": [
        "df_means=df.groupby(['sexo','aesc'],as_index=False)['logingreso'].mean()\n",
        "df_means=df_means.loc[df_means.aesc<20]\n",
        "#.plot(x='aesc',y='logingreso')"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iC3AfX7r1Ks",
        "outputId": "8d0fc856-ec72-4ab2-b946-35fcf90350e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "base=df_means.loc[df_means.sexo=='varon'].plot(x='aesc',y='logingreso', style='o', label='varon')\n",
        "df_means.loc[df_means.sexo=='mujer'].plot(x='aesc',y='logingreso', style='o', ax=base, label='mujer')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe41c156250>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpLKXlWw3xCg"
      },
      "source": [
        "Creando una variable dummy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wPIwFnCfa4W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf0dd5bd-4f05-418d-9d4a-b53a52006d4a"
      },
      "source": [
        "df.loc[df['sexo'] == 'varon', 'dmujer'] = 0\n",
        "df.loc[df['sexo'] == 'mujer', 'dmujer'] = 1"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1684: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[key] = infer_fill_value(value)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_column(loc, value, pi)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funciones"
      ],
      "metadata": {
        "id": "eJeUT_eSaAQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def suma():\n",
        "  a=6\n",
        "  b=5\n",
        "  print(a+b)\n",
        "  \n"
      ],
      "metadata": {
        "id": "D0khIi6AZ96m"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "suma()"
      ],
      "metadata": {
        "id": "-IcB5KkBamg_",
        "outputId": "293315e7-0c01-43a4-e883-c561e1c89b23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#funcion con parametros\n",
        "def nuevasuma(a,b):\n",
        "  print(a+b)"
      ],
      "metadata": {
        "id": "wLjMBkmsatqW"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nuevasuma(1,2)"
      ],
      "metadata": {
        "id": "pZ-QhxCKa2z7",
        "outputId": "420669f6-0280-4008-db24-2cd29b803796",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rr2sfj-UdQQ2"
      },
      "source": [
        "## OLS\n",
        "\n",
        "Statsmodels es la librería de operaciones estadísticas que usaremos para estimar OLS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9qvql6adR-y"
      },
      "source": [
        "import statsmodels.api as sm"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KwGkaLFdiqt"
      },
      "source": [
        "Estimar la regresion va a requerir varios pasos que voy a resumir en una funcion.\n",
        "\n",
        "Asegurarme de que los datos (las filas en cuestion) que voy a estar usando no tienen datos missing en ninguna variable. \\\\\n",
        "Separar a la variable a explicar (dependiente) de la(s) explicativas \\\\\n",
        "Si la regresión va a tener una constante, como en nuestro caso, agregar a las variables explicativas una columna de 1s (unos). \\\\\n",
        "Asignar a una variable el nombre del modelo y las especificaciones del mismo  \\\\\n",
        "Correr el modelo con el metodo fit() y guardar los resultados en otra variable\n",
        "Imprimir los resultados en pantalla (imprimir los contenidos de la variable del paso anterior)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$y=\\beta_0+\\beta_1 aesc+\\beta_2 edad + \\epsilon$"
      ],
      "metadata": {
        "id": "sZcvlXxbcmeB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#definir el modelo\n",
        "modelo=sm.OLS(df['logingreso'],df[['aesc','edad','constante']])\n",
        "# ajustar el modelo con fit()\n",
        "# con summary imprimo el resultado()\n",
        "modelo.fit().summary()"
      ],
      "metadata": {
        "id": "Auau20CzbraX",
        "outputId": "82d611b2-8a90-4c1f-8519-d0b96e04d724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:             logingreso   R-squared:                       0.076\n",
              "Model:                            OLS   Adj. R-squared:                  0.076\n",
              "Method:                 Least Squares   F-statistic:                     414.6\n",
              "Date:                Thu, 13 Oct 2022   Prob (F-statistic):          8.92e-174\n",
              "Time:                        13:49:30   Log-Likelihood:                -12439.\n",
              "No. Observations:               10113   AIC:                         2.488e+04\n",
              "Df Residuals:                   10110   BIC:                         2.491e+04\n",
              "Df Model:                           2                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "aesc           0.0376      0.001     27.681      0.000       0.035       0.040\n",
              "edad           0.0047      0.000     10.868      0.000       0.004       0.006\n",
              "constante      8.5712      0.030    287.013      0.000       8.513       8.630\n",
              "==============================================================================\n",
              "Omnibus:                     1866.292   Durbin-Watson:                   1.646\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5279.795\n",
              "Skew:                          -0.979   Prob(JB):                         0.00\n",
              "Kurtosis:                       5.949   Cond. No.                         193.\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>logingreso</td>    <th>  R-squared:         </th> <td>   0.076</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.076</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   414.6</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 13 Oct 2022</td> <th>  Prob (F-statistic):</th> <td>8.92e-174</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>13:49:30</td>     <th>  Log-Likelihood:    </th> <td> -12439.</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td> 10113</td>      <th>  AIC:               </th> <td>2.488e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td> 10110</td>      <th>  BIC:               </th> <td>2.491e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>aesc</th>      <td>    0.0376</td> <td>    0.001</td> <td>   27.681</td> <td> 0.000</td> <td>    0.035</td> <td>    0.040</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>edad</th>      <td>    0.0047</td> <td>    0.000</td> <td>   10.868</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>constante</th> <td>    8.5712</td> <td>    0.030</td> <td>  287.013</td> <td> 0.000</td> <td>    8.513</td> <td>    8.630</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>1866.292</td> <th>  Durbin-Watson:     </th> <td>   1.646</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5279.795</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>           <td>-0.979</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>       <td> 5.949</td>  <th>  Cond. No.          </th> <td>    193.</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['constante']=1\n",
        "df['constante']"
      ],
      "metadata": {
        "id": "uFB9QO92mnIT",
        "outputId": "c20532da-a893-49e2-be45-e7c5bf7daee9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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        },
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              "        ..\n",
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              "14511    1\n",
              "Name: constante, Length: 10113, dtype: int64"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DoC3j5ZdpJ_"
      },
      "source": [
        "def regress(lista, data):\n",
        "  \"\"\" \n",
        "  Funcion que realiza la regresion OLS\n",
        "  lista: Es una lista de nombres variables siendo la primera de ellas 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.OLS(Y,X)\n",
        "  resultados=modelo.fit()\n",
        "  print(resultados.summary())\n",
        "  return resultados"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regress(['logingreso','aesc','edad'],df)"
      ],
      "metadata": {
        "id": "DuJs62KOpd16",
        "outputId": "c0dcf358-4327-4df0-ec03-8f7aaa155733",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.076\n",
            "Model:                            OLS   Adj. R-squared:                  0.076\n",
            "Method:                 Least Squares   F-statistic:                     414.6\n",
            "Date:                Thu, 13 Oct 2022   Prob (F-statistic):          8.92e-174\n",
            "Time:                        14:02:42   Log-Likelihood:                -12439.\n",
            "No. Observations:               10113   AIC:                         2.488e+04\n",
            "Df Residuals:                   10110   BIC:                         2.491e+04\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          8.5712      0.030    287.013      0.000       8.513       8.630\n",
            "aesc           0.0376      0.001     27.681      0.000       0.035       0.040\n",
            "edad           0.0047      0.000     10.868      0.000       0.004       0.006\n",
            "==============================================================================\n",
            "Omnibus:                     1866.292   Durbin-Watson:                   1.646\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5279.795\n",
            "Skew:                          -0.979   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.949   Cond. No.                         193.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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",
      "source": [
        "# Generar Dummies\n"
      ],
      "metadata": {
        "id": "w5YzUcZFqJiv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dummy del sexo\n",
        "#Metodo 1: Indico 0 para todos y luego reemplazo para mujer\n",
        "df['dmujer']=0\n",
        "# sintaxis para reemplazar conjunto de observaciones\n",
        "df.loc[df['sexo']=='mujer','dmujer']=1"
      ],
      "metadata": {
        "id": "_avvuTPzqFvo",
        "outputId": "58c41192-dbbe-42ce-ada2-c42a300e1c95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_column(loc, value, pi)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['dmujer','sexo']]"
      ],
      "metadata": {
        "id": "eQjqwkOXq2Nu",
        "outputId": "88e43dcf-b18d-464e-e50e-f1877f521f45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       dmujer   sexo\n",
              "0           1  mujer\n",
              "1           0  varon\n",
              "2           1  mujer\n",
              "3           0  varon\n",
              "4           0  varon\n",
              "...       ...    ...\n",
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              "14508       1  mujer\n",
              "14509       1  mujer\n",
              "14510       0  varon\n",
              "14511       1  mujer\n",
              "\n",
              "[10113 rows x 2 columns]"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "my9f8-_keMlw",
        "outputId": "85c5c23a-816a-4c09-8b70-0d06ccb3ba26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regress(['logingreso','dmujer'],data=df)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.032\n",
            "Model:                            OLS   Adj. R-squared:                  0.032\n",
            "Method:                 Least Squares   F-statistic:                     332.5\n",
            "Date:                Thu, 13 Oct 2022   Prob (F-statistic):           4.01e-73\n",
            "Time:                        14:09:52   Log-Likelihood:                -12674.\n",
            "No. Observations:               10113   AIC:                         2.535e+04\n",
            "Df Residuals:                   10111   BIC:                         2.537e+04\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          9.4441      0.012    771.247      0.000       9.420       9.468\n",
            "dmujer        -0.3078      0.017    -18.235      0.000      -0.341      -0.275\n",
            "==============================================================================\n",
            "Omnibus:                     1137.319   Durbin-Watson:                   1.487\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2488.274\n",
            "Skew:                          -0.693   Prob(JB):                         0.00\n",
            "Kurtosis:                       4.996   Cond. No.                         2.68\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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": "code",
      "metadata": {
        "id": "9FjGWMny0FHN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a5bd968-247a-434a-8ed0-01224ca88afc"
      },
      "source": [
        "df['dinmigrante'] = 0\n",
        "df.loc[df['inmigrante'] == 'inmigrante', 'dinmigrante'] = 1"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_column(loc, value, pi)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwNzJjVP_tGL",
        "outputId": "cf678810-aa21-4107-fe12-009cf69eea8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[(df['sexo'] == 'varon')&(df['inmigrante'] != 'inmigrante')].logingreso.mean()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.50509170628835"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "EadC4C6r7Lnx",
        "outputId": "1686c22b-6d28-4fe1-bf78-2eb67f8f2be4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[(df['sexo'] == 'mujer')&(df['inmigrante'] != 'inmigrante')].logingreso.mean()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.209591040354109"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIMQoTDtAI64",
        "outputId": "e3ecfb64-b858-4cf3-87fa-703bc97697f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[(df['sexo'] == 'varon')&(df['inmigrante'] == 'inmigrante')].logingreso.mean()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.261624758838526"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVJ6T42vAdKF",
        "outputId": "909b577b-25cf-42ef-ebad-bc6c97226736",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.loc[(df['sexo'] == 'mujer')&(df['inmigrante'] == 'inmigrante')].logingreso.mean()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.9385575657043"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-z0lrzC7vAY",
        "outputId": "292e21c9-ff71-4161-a782-5bf11e4d4d17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regress(['logingreso','dmujer','dinmigrante'],data=df)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.049\n",
            "Model:                            OLS   Adj. R-squared:                  0.049\n",
            "Method:                 Least Squares   F-statistic:                     261.4\n",
            "Date:                Thu, 13 Oct 2022   Prob (F-statistic):          1.98e-111\n",
            "Time:                        11:49:08   Log-Likelihood:                -12583.\n",
            "No. Observations:               10113   AIC:                         2.517e+04\n",
            "Df Residuals:                   10110   BIC:                         2.519e+04\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===============================================================================\n",
            "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------\n",
            "const           9.5088      0.013    729.363      0.000       9.483       9.534\n",
            "dmujer         -0.3027      0.017    -18.090      0.000      -0.335      -0.270\n",
            "dinmigrante    -0.2583      0.019    -13.577      0.000      -0.296      -0.221\n",
            "==============================================================================\n",
            "Omnibus:                     1165.681   Durbin-Watson:                   1.519\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2615.035\n",
            "Skew:                          -0.700   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.061   Cond. No.                         2.94\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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": "code",
      "metadata": {
        "id": "gGsJRBhWCK07",
        "outputId": "92dd8341-dbf1-48e0-ccd6-d988b4f63ca9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#prediccion para la mujer inmigrante\n",
        "9.5088-0.3027-0.2583"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.9478"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZgN9vI_BYBi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61306ce4-e22e-4d89-908a-616d5687a818"
      },
      "source": [
        "df['dmujerinmigrante'] =df['dmujer']*df['dinmigrante'] "
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyHpuOfHBgbm",
        "outputId": "6d615501-57eb-45f6-a621-5d77d847a759",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "resultados=regress(['logingreso','dmujer','dinmigrante','dmujerinmigrante' ],data=df)\n",
        "# me permite computar directamente el diferencial de la mujer inmigrante, que de otra manera quedaria resumido en el promedio"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             logingreso   R-squared:                       0.049\n",
            "Model:                            OLS   Adj. R-squared:                  0.049\n",
            "Method:                 Least Squares   F-statistic:                     174.5\n",
            "Date:                Thu, 13 Oct 2022   Prob (F-statistic):          2.78e-110\n",
            "Time:                        14:27:44   Log-Likelihood:                -12583.\n",
            "No. Observations:               10113   AIC:                         2.517e+04\n",
            "Df Residuals:                   10109   BIC:                         2.520e+04\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "====================================================================================\n",
            "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------\n",
            "const                9.5051      0.014    678.116      0.000       9.478       9.533\n",
            "dmujer              -0.2955      0.019    -15.199      0.000      -0.334      -0.257\n",
            "dinmigrante         -0.2435      0.028     -8.691      0.000      -0.298      -0.189\n",
            "dmujerinmigrante    -0.0276      0.038     -0.722      0.470      -0.102       0.047\n",
            "==============================================================================\n",
            "Omnibus:                     1163.411   Durbin-Watson:                   1.519\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2609.900\n",
            "Skew:                          -0.699   Prob(JB):                         0.00\n",
            "Kurtosis:                       5.059   Cond. No.                         6.67\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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": "code",
      "source": [
        "df['predicc_ingreso']=9.5051-0.2955*df['dmujer']-0.2435*df['dinmigrante']-0.0276*df['dmujerinmigrante']"
      ],
      "metadata": {
        "id": "Y2uIm0_PuA6N",
        "outputId": "ee936cde-2b40-487a-b367-1030a8fc7396",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['logingreso','predicc_ingreso', 'sexo','inmigrante']]"
      ],
      "metadata": {
        "id": "jzIGmmYxuheq",
        "outputId": "755a86ff-a14d-49f9-a117-d85691eb59ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       logingreso  predicc_ingreso   sexo  inmigrante\n",
              "0        8.384804           9.2096  mujer  nativo arg\n",
              "1        9.104980           9.2616  varon  inmigrante\n",
              "2        8.482602           8.9385  mujer  inmigrante\n",
              "3        9.615805           9.5051  varon  nativo arg\n",
              "4        9.615805           9.5051  varon  nativo arg\n",
              "...           ...              ...    ...         ...\n",
              "14507    8.748305           8.9385  mujer  inmigrante\n",
              "14508    9.952278           9.2096  mujer  nativo arg\n",
              "14509    9.210340           8.9385  mujer  inmigrante\n",
              "14510    8.922658           9.5051  varon  nativo arg\n",
              "14511    9.190138           8.9385  mujer  inmigrante\n",
              "\n",
              "[10113 rows x 4 columns]"
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resultados.predict()"
      ],
      "metadata": {
        "id": "vB4ZViBztELM",
        "outputId": "1e247f16-d34d-4260-f7d8-22b32f0c81b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.20959104, 9.26162476, 8.93855757, ..., 8.93855757, 9.50509171,\n",
              "       8.93855757])"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhZcfrJkCZD2",
        "outputId": "70cfe718-e18c-4188-9810-60c1fcb35d23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# prediccion del ingreso para la mujer inmigrante\n",
        "9.5051-0.2955-0.2435-0.0276"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.938500000000001"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    }
  ]
}