extraction_transform.ipynb

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    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "KgWK5luQ0W8Z"
      },
      "source": [
        "# Tratamiento de datos 2017"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "HKZBW58TMF-m"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "#NHCCPCTRL2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "algnJHPNYrkP"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "DIRECTORIO: identificación de la vivienda -- \n",
        "DIRECTORIO_HOG: identificación del hogar\n",
        "DIRECTORIO_PER: identificación de la persona  \n",
        "NHCCPCTRL2: 16. ¿Cuántas personas componen este hogar? \n",
        "NHCCP19: Incluyendo sala y comedor, ¿de cuántos cuartos o piezas dispone este hogar? (excluya cocina, baños, garajes y los cuartos destinados a negocio)\n",
        "NHCCP20: 18. ¿En cuántos de esos cuartos duermen las personas del hogar?\n",
        "NHCCP35A: ¿Cuantos cuartos?\n",
        "CODLOCALIDAD: localidad\n",
        "NHCCP21A: 19. Durante el día, cuáles de los siguientes espacios cuentan con iluminación natural: 1. Sala - comedor\n",
        "NHCCP21B: 19. Durante el día, cuáles de los siguientes espacios cuentan con iluminación natural: 2. Cuartos para dormir\n",
        "NPCFP14I: 15. le han diagnosticado alguna de estas enfermedades o problemas de salud: 9. Asma?\n",
        "1: Si\n",
        "2: No\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "features_2017 = ['DIRECTORIO', 'DIRECTORIO_HOG', 'DIRECTORIO_PER', 'NHCCPCTRL2', 'NHCCP19', \n",
        "                 'NHCCP20', 'NHCCP35A', 'CODLOCALIDAD', 'NHCCP21A', 'NHCCP21B', 'NPCFP14I']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "YoVr9m8gMF-s",
        "outputId": "7aab8502-b639-4f1c-ffbd-299c93068fa4"
      },
      "outputs": [],
      "source": [
        "no_asma_2017_df = pd.read_csv('./data/data_sin_asma/Datos_proyecto_II_BI_2017_sin_asma.csv', encoding=\"utf-8\", sep = ',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1I0Sa279YTK",
        "outputId": "ba1dca83-35f8-4388-e924-d4a29fd065a9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Esteban\\AppData\\Local\\Temp\\ipykernel_1516\\1698132705.py:1: DtypeWarning: Columns (145,295,297,380,381,383,469,495,496,499,500,501,508,509,510,511) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  asma_2017_df = pd.read_csv('./data/data_asma/Datos_proyecto_II_BI_2017.csv', encoding=\"utf-8\", sep = ',')\n"
          ]
        }
      ],
      "source": [
        "asma_2017_df = pd.read_csv('./data/data_asma/Datos_proyecto_II_BI_2017.csv', encoding=\"utf-8\", sep = ',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "no_asma_2017_df.shape, asma_2017_df.shape "
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIRECTORIO</th>\n",
              "      <th>DIRECTORIO_HOG</th>\n",
              "      <th>DIRECTORIO_PER</th>\n",
              "      <th>NHCCPCTRL2</th>\n",
              "      <th>NHCCP19</th>\n",
              "      <th>NHCCP20</th>\n",
              "      <th>NHCCP35A</th>\n",
              "      <th>CODLOCALIDAD</th>\n",
              "      <th>NHCCP21A</th>\n",
              "      <th>NHCCP21B</th>\n",
              "      <th>NPCFP14I</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101033</td>\n",
              "      <td>1010331</td>\n",
              "      <td>10103311</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101042</td>\n",
              "      <td>1010421</td>\n",
              "      <td>10104213</td>\n",
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              "      <th>3</th>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>101059</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105914</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>5</th>\n",
              "      <td>101059</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>101063</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
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              "      <td>4</td>\n",
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              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>9</th>\n",
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              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
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            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0      101033         1010331        10103311           1        4        1  \\\n",
              "1      101041         1010411        10104112           3        1        1   \n",
              "2      101042         1010421        10104213           5        4        3   \n",
              "3      101047         1010471        10104713           3        3        3   \n",
              "4      101059         1010591        10105914           5        4        4   \n",
              "5      101059         1010591        10105915           5        4        4   \n",
              "6      101063         1010631        10106311           3        5        3   \n",
              "7      101089         1010891        10108911           4        4        3   \n",
              "8      101089         1010891        10108914           4        4        3   \n",
              "9      101142         1011422        10114221           3        2        1   \n",
              "\n",
              "   NHCCP35A  CODLOCALIDAD  NHCCP21A  NHCCP21B  NPCFP14I  \n",
              "0       2.0          11.0         1         1         1  \n",
              "1       1.0           1.0         2         2         1  \n",
              "2       1.0           1.0         1         1         1  \n",
              "3       1.0           1.0         3         1         1  \n",
              "4       2.0          11.0         1         1         1  \n",
              "5       2.0          11.0         1         1         1  \n",
              "6       1.0          11.0         1         1         1  \n",
              "7       2.0          11.0         1         1         1  \n",
              "8       2.0          11.0         1         1         1  \n",
              "9       1.0          13.0         1         1         1  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "asma_2017_df[features_2017].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqZfzZN3p_W6",
        "outputId": "ee4e8dde-78a2-4ba7-a8cf-cdde579a6ae9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas DIRECTORIO_HOG es: 414\n",
            "El número de filas duplicadas es: 414\n"
          ]
        }
      ],
      "source": [
        "duplicados = asma_2017_df[features_2017].duplicated(subset=['DIRECTORIO_HOG'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "\n",
        "duplicados = asma_2017_df[features_2017].duplicated(subset=['DIRECTORIO'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "\n",
        "print(\"El número de filas duplicadas DIRECTORIO_HOG es:\", numero_duplicados)\n",
        "\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnTCRWQI-Kr-",
        "outputId": "847fdaec-2377-4bef-8725-14b3afe4d351"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas es: 0\n"
          ]
        }
      ],
      "source": [
        "duplicados = no_asma_2017_df[features_2017].duplicated(subset=['DIRECTORIO_PER'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuJOJhfF-kuv",
        "outputId": "fecf85d1-8f5b-404d-83ea-75e7f5ec8e33"
      },
      "outputs": [],
      "source": [
        "#UNIR DATASET\n",
        "# Método 1: Usar append()\n",
        "provisional = pd.concat([asma_2017_df, no_asma_2017_df], ignore_index=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "      <th>DIRECTORIO_PER</th>\n",
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              "      <th>NHCCP19</th>\n",
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              "      <td>10104713</td>\n",
              "      <td>3</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>10105914</td>\n",
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              "      <td>11.0</td>\n",
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              "      <td>101059</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>6</th>\n",
              "      <td>101063</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>101089</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108911</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>101089</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101142</td>\n",
              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0      101033         1010331        10103311           1        4        1  \\\n",
              "1      101041         1010411        10104112           3        1        1   \n",
              "2      101042         1010421        10104213           5        4        3   \n",
              "3      101047         1010471        10104713           3        3        3   \n",
              "4      101059         1010591        10105914           5        4        4   \n",
              "5      101059         1010591        10105915           5        4        4   \n",
              "6      101063         1010631        10106311           3        5        3   \n",
              "7      101089         1010891        10108911           4        4        3   \n",
              "8      101089         1010891        10108914           4        4        3   \n",
              "9      101142         1011422        10114221           3        2        1   \n",
              "\n",
              "   NHCCP35A  CODLOCALIDAD  NHCCP21A  NHCCP21B  NPCFP14I  \n",
              "0       2.0          11.0         1         1         1  \n",
              "1       1.0           1.0         2         2         1  \n",
              "2       1.0           1.0         1         1         1  \n",
              "3       1.0           1.0         3         1         1  \n",
              "4       2.0          11.0         1         1         1  \n",
              "5       2.0          11.0         1         1         1  \n",
              "6       1.0          11.0         1         1         1  \n",
              "7       2.0          11.0         1         1         1  \n",
              "8       2.0          11.0         1         1         1  \n",
              "9       1.0          13.0         1         1         1  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "provisional[features_2017].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oq9jPZKarCTG"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Esteban\\AppData\\Local\\Temp\\ipykernel_1516\\1933145409.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  provisional['year'] = 2017\n"
          ]
        }
      ],
      "source": [
        "provisional['year'] = 2017"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xtMh1YtnrhtW"
      },
      "outputs": [],
      "source": [
        "features_2017.append('year')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "EiM-MYeqrdus",
        "outputId": "22e58077-7f2f-4cbf-c6e3-5e589b12d35c"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIRECTORIO</th>\n",
              "      <th>DIRECTORIO_HOG</th>\n",
              "      <th>DIRECTORIO_PER</th>\n",
              "      <th>NHCCPCTRL2</th>\n",
              "      <th>NHCCP19</th>\n",
              "      <th>NHCCP20</th>\n",
              "      <th>NHCCP35A</th>\n",
              "      <th>CODLOCALIDAD</th>\n",
              "      <th>NHCCP21A</th>\n",
              "      <th>NHCCP21B</th>\n",
              "      <th>NPCFP14I</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101033</td>\n",
              "      <td>1010331</td>\n",
              "      <td>10103311</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101041</td>\n",
              "      <td>1010411</td>\n",
              "      <td>10104112</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101042</td>\n",
              "      <td>1010421</td>\n",
              "      <td>10104213</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101047</td>\n",
              "      <td>1010471</td>\n",
              "      <td>10104713</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101059</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105914</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>101059</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>101063</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>101089</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108911</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>101089</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101142</td>\n",
              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0      101033         1010331        10103311           1        4        1  \\\n",
              "1      101041         1010411        10104112           3        1        1   \n",
              "2      101042         1010421        10104213           5        4        3   \n",
              "3      101047         1010471        10104713           3        3        3   \n",
              "4      101059         1010591        10105914           5        4        4   \n",
              "5      101059         1010591        10105915           5        4        4   \n",
              "6      101063         1010631        10106311           3        5        3   \n",
              "7      101089         1010891        10108911           4        4        3   \n",
              "8      101089         1010891        10108914           4        4        3   \n",
              "9      101142         1011422        10114221           3        2        1   \n",
              "\n",
              "   NHCCP35A  CODLOCALIDAD  NHCCP21A  NHCCP21B  NPCFP14I  year  \n",
              "0       2.0          11.0         1         1         1  2017  \n",
              "1       1.0           1.0         2         2         1  2017  \n",
              "2       1.0           1.0         1         1         1  2017  \n",
              "3       1.0           1.0         3         1         1  2017  \n",
              "4       2.0          11.0         1         1         1  2017  \n",
              "5       2.0          11.0         1         1         1  2017  \n",
              "6       1.0          11.0         1         1         1  2017  \n",
              "7       2.0          11.0         1         1         1  2017  \n",
              "8       2.0          11.0         1         1         1  2017  \n",
              "9       1.0          13.0         1         1         1  2017  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "provisional[features_2017].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nt5kFEfy_5ji",
        "outputId": "68c3387f-7632-488b-bf44-825a53c8404e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['DIRECTORIO', 'DIRECTORIO_HOG', 'DIRECTORIO_PER', 'NHCCPCTRL2',\n",
              "       'NHCCP19', 'NHCCP20', 'NHCCP35A', 'CODLOCALIDAD', 'NHCCP21A',\n",
              "       'NHCCP21B', 'NPCFP14I', 'year'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "provisional[features_2017].columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bd8SSji40ni7"
      },
      "outputs": [],
      "source": [
        "df_2017 = provisional[features_2017].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fyqCZ0zc0yeH"
      },
      "outputs": [],
      "source": [
        "# Renombrar las columnas\n",
        "nuevos_nombres = {'DIRECTORIO': 'id_vivienda', 'DIRECTORIO_HOG': 'id_hogar', 'DIRECTORIO_PER': 'id_persona', \n",
        "         'NHCCPCTRL2': 'num_personas_hogar', 'NHCCP19': 'num_cuartos_hogar', 'NHCCP20': 'num_cuartos_ocupados', \n",
        "         'NHCCP35A': 'num_cuartos', 'CODLOCALIDAD': 'localidad', 'NHCCP21A':'iluminacion_sala',  'NHCCP21B':'iluminacion_cuartos',\n",
        "         'NPCFP14I': 'asma',          \n",
        "                  }\n",
        "df_2017 = df_2017.rename(columns=nuevos_nombres)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7t8dQ8CozN8l",
        "outputId": "6e687bc4-19ec-4ffd-b333-e9ab45891312"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "localidad               0.306743\n",
              "num_cuartos             0.032986\n",
              "id_vivienda             0.000000\n",
              "id_hogar                0.000000\n",
              "id_persona              0.000000\n",
              "num_personas_hogar      0.000000\n",
              "num_cuartos_hogar       0.000000\n",
              "num_cuartos_ocupados    0.000000\n",
              "iluminacion_sala        0.000000\n",
              "iluminacion_cuartos     0.000000\n",
              "asma                    0.000000\n",
              "year                    0.000000\n",
              "dtype: float64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df_2017.isnull().sum() / df_2017.shape[0]).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "P2MF2dqw6ung",
        "outputId": "0c6161bf-e7f9-4e76-eff6-96c4241f45bb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos_ocupados</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>319952.000000</td>\n",
              "      <td>3.199520e+05</td>\n",
              "      <td>3.199520e+05</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>309398.000000</td>\n",
              "      <td>221809.000000</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>319952.000000</td>\n",
              "      <td>319952.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>195527.604478</td>\n",
              "      <td>1.955277e+06</td>\n",
              "      <td>1.968230e+07</td>\n",
              "      <td>3.667050</td>\n",
              "      <td>3.652954</td>\n",
              "      <td>2.420813</td>\n",
              "      <td>1.421299</td>\n",
              "      <td>9.760312</td>\n",
              "      <td>1.139815</td>\n",
              "      <td>1.058221</td>\n",
              "      <td>1.985226</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>58686.783373</td>\n",
              "      <td>5.868678e+05</td>\n",
              "      <td>7.563171e+06</td>\n",
              "      <td>1.592154</td>\n",
              "      <td>1.272738</td>\n",
              "      <td>0.966174</td>\n",
              "      <td>0.663760</td>\n",
              "      <td>5.108173</td>\n",
              "      <td>0.460414</td>\n",
              "      <td>0.234162</td>\n",
              "      <td>0.120648</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>101000.000000</td>\n",
              "      <td>1.010001e+06</td>\n",
              "      <td>1.010001e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>145678.000000</td>\n",
              "      <td>1.456781e+06</td>\n",
              "      <td>1.457021e+07</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>181810.000000</td>\n",
              "      <td>1.818101e+06</td>\n",
              "      <td>1.818611e+07</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>248360.250000</td>\n",
              "      <td>2.483604e+06</td>\n",
              "      <td>2.485031e+07</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>317885.000000</td>\n",
              "      <td>3.178851e+06</td>\n",
              "      <td>3.175431e+08</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id_vivienda      id_hogar    id_persona  num_personas_hogar   \n",
              "count  319952.000000  3.199520e+05  3.199520e+05       319952.000000  \\\n",
              "mean   195527.604478  1.955277e+06  1.968230e+07            3.667050   \n",
              "std     58686.783373  5.868678e+05  7.563171e+06            1.592154   \n",
              "min    101000.000000  1.010001e+06  1.010001e+07            1.000000   \n",
              "25%    145678.000000  1.456781e+06  1.457021e+07            3.000000   \n",
              "50%    181810.000000  1.818101e+06  1.818611e+07            4.000000   \n",
              "75%    248360.250000  2.483604e+06  2.485031e+07            4.000000   \n",
              "max    317885.000000  3.178851e+06  3.175431e+08           17.000000   \n",
              "\n",
              "       num_cuartos_hogar  num_cuartos_ocupados    num_cuartos      localidad   \n",
              "count      319952.000000         319952.000000  309398.000000  221809.000000  \\\n",
              "mean            3.652954              2.420813       1.421299       9.760312   \n",
              "std             1.272738              0.966174       0.663760       5.108173   \n",
              "min             1.000000              1.000000       1.000000       1.000000   \n",
              "25%             3.000000              2.000000       1.000000       6.000000   \n",
              "50%             4.000000              2.000000       1.000000       9.000000   \n",
              "75%             4.000000              3.000000       2.000000      13.000000   \n",
              "max            15.000000             11.000000      10.000000      21.000000   \n",
              "\n",
              "       iluminacion_sala  iluminacion_cuartos           asma      year  \n",
              "count     319952.000000        319952.000000  319952.000000  319952.0  \n",
              "mean           1.139815             1.058221       1.985226    2017.0  \n",
              "std            0.460414             0.234162       0.120648       0.0  \n",
              "min            1.000000             1.000000       1.000000    2017.0  \n",
              "25%            1.000000             1.000000       2.000000    2017.0  \n",
              "50%            1.000000             1.000000       2.000000    2017.0  \n",
              "75%            1.000000             1.000000       2.000000    2017.0  \n",
              "max            3.000000             2.000000       2.000000    2017.0  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_2017.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7OcdotVf5qf0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "id_vivienda\n",
            "184980    22\n",
            "145788    22\n",
            "112379    21\n",
            "135993    19\n",
            "172991    19\n",
            "          ..\n",
            "164260     1\n",
            "260939     1\n",
            "164268     1\n",
            "164276     1\n",
            "101033     1\n",
            "Name: count, Length: 107218, dtype: int64\n",
            "\n",
            "\n",
            "id_hogar\n",
            "1409051    17\n",
            "1091071    14\n",
            "1590861    14\n",
            "1857011    14\n",
            "1692321    14\n",
            "           ..\n",
            "1638881     1\n",
            "2604411     1\n",
            "1638931     1\n",
            "1638951     1\n",
            "1010331     1\n",
            "Name: count, Length: 109111, dtype: int64\n",
            "\n",
            "\n",
            "id_persona\n",
            "10103311    1\n",
            "23136314    1\n",
            "23136512    1\n",
            "23136511    1\n",
            "23136414    1\n",
            "           ..\n",
            "15816523    1\n",
            "15816522    1\n",
            "15816521    1\n",
            "15816515    1\n",
            "31788512    1\n",
            "Name: count, Length: 319952, dtype: int64\n",
            "\n",
            "\n",
            "num_personas_hogar\n",
            "4     86172\n",
            "3     80508\n",
            "2     55300\n",
            "5     45010\n",
            "1     18979\n",
            "6     18726\n",
            "7      8253\n",
            "8      3696\n",
            "9      1773\n",
            "10      780\n",
            "11      374\n",
            "12      204\n",
            "13      104\n",
            "14       56\n",
            "17       17\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos_hogar\n",
            "4     107177\n",
            "3      97632\n",
            "5      44408\n",
            "2      39581\n",
            "6      13372\n",
            "1      10619\n",
            "7       4330\n",
            "8       1740\n",
            "9        525\n",
            "10       339\n",
            "11        87\n",
            "12        64\n",
            "13        60\n",
            "15        11\n",
            "14         7\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos_ocupados\n",
            "2     122628\n",
            "3     110726\n",
            "1      52976\n",
            "4      26848\n",
            "5       4886\n",
            "6       1265\n",
            "7        411\n",
            "8        128\n",
            "9         43\n",
            "11        24\n",
            "10        17\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos\n",
            "1.0     202585\n",
            "2.0      88409\n",
            "3.0      14523\n",
            "4.0       3002\n",
            "5.0        613\n",
            "6.0        192\n",
            "7.0         50\n",
            "8.0         19\n",
            "10.0         3\n",
            "9.0          2\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "localidad\n",
            "8.0     28006\n",
            "11.0    22603\n",
            "9.0     18746\n",
            "10.0    16623\n",
            "19.0    16218\n",
            "5.0     14901\n",
            "4.0     12596\n",
            "7.0     12559\n",
            "1.0     12004\n",
            "16.0    10243\n",
            "13.0     9810\n",
            "18.0     9668\n",
            "3.0      8587\n",
            "12.0     5549\n",
            "15.0     5474\n",
            "6.0      5281\n",
            "14.0     4591\n",
            "2.0      4089\n",
            "17.0     1984\n",
            "20.0     1232\n",
            "21.0     1045\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "iluminacion_sala\n",
            "1    289890\n",
            "2     15390\n",
            "3     14672\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "iluminacion_cuartos\n",
            "1    301324\n",
            "2     18628\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "asma\n",
            "2    315225\n",
            "1      4727\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "year\n",
            "2017    319952\n",
            "Name: count, dtype: int64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for value in df_2017:\n",
        "  print() \n",
        "  print(df_2017[value].value_counts())\n",
        "  print() \n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WiCKBUhwmJPA"
      },
      "outputs": [],
      "source": [
        "df_2017['iluminacion_sala'] = df_2017['iluminacion_sala'].replace(3, 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKQ66bjmmqD1",
        "outputId": "50032985-84b5-4dc7-81f6-ec7072d53b87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "iluminacion_sala\n",
            "1    289890\n",
            "2     30062\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(df_2017['iluminacion_sala'].value_counts())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ANr-B16y0dkd"
      },
      "source": [
        "# Tratamiento datos 2021"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YaenshjO0hgf"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "DIRECTORIO: identificación de la vivienda\n",
        "DIRECTORIO_HOG: identificación del hogar\n",
        "DIRECTORIO_PER: identificación de la persona \n",
        "NHCCPCTRL2: 16. ¿Cuántas personas componen este hogar?\n",
        "NHCCP19: Incluyendo sala y comedor, ¿de cuántos cuartos o piezas dispone este hogar? (excluya cocina, baños, garajes y los cuartos destinados a negocio)\n",
        "NHCCP20: 18. ¿En cuántos de esos cuartos duermen las personas del hogar?\n",
        "NHCCP35A: ¿Cuantos cuartos?\n",
        "COD_LOCALIDAD: codigo de localidad\n",
        "\n",
        "NHCCP21A: 19. Durante el día, cuáles de los siguientes espacios cuentan con iluminación natural: 1. Sala - comedor\n",
        "NHCCP21B: 19. Durante el día, cuáles de los siguientes espacios cuentan con iluminación natural: 2. Cuartos para dormir\n",
        "\n",
        "NPCFP14F: 15. ¿A ...le han diagnosticado alguna de estas enfermedades o problemas de salud: 6. Enfermedades respiratorias crónicas como: Asma, EPOC, Enfisema?\n",
        "1: Si\n",
        "2: No\n",
        "\"\"\"\n",
        "\n",
        "features_2021 = ['DIRECTORIO', 'DIRECTORIO_HOG','DIRECTORIO_PER', \n",
        "                 'NHCCPCTRL2', 'NHCCP19', 'NHCCP20', 'NHCCP35A', \n",
        "                 'COD_LOCALIDAD', 'NPCFP14F']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Sd5cO970njt",
        "outputId": "ba4b9b0c-78ec-4e11-bf76-1fdb4d6fb476"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Esteban\\AppData\\Local\\Temp\\ipykernel_1516\\3873647657.py:1: DtypeWarning: Columns (8,10,334,408,409,410,412,538,540,541,543,545,549,552,553,554,555,556,557) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  no_asma_2021_df = pd.read_csv('./data/data_sin_asma/Datos_proyecto_II_BI_2021_sin_asma.csv', sep = ',', encoding='latin1')\n"
          ]
        }
      ],
      "source": [
        "no_asma_2021_df = pd.read_csv('./data/data_sin_asma/Datos_proyecto_II_BI_2021_sin_asma.csv', sep = ',', encoding='latin1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1eykNZCAXoo",
        "outputId": "93ea0afc-784c-44a2-ec7e-c13cba66355f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Esteban\\AppData\\Local\\Temp\\ipykernel_1516\\2157790421.py:1: DtypeWarning: Columns (334,410,412,540,541,543,545,549,552,553,554,555,556,557) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  asma_2021_df = pd.read_csv('./data/data_asma/Datos_proyecto_II_BI_2021.csv', sep = ',', encoding='latin1')\n"
          ]
        }
      ],
      "source": [
        "asma_2021_df = pd.read_csv('./data/data_asma/Datos_proyecto_II_BI_2021.csv', sep = ',', encoding='latin1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wd2deGGo0w8m",
        "outputId": "873104c2-dd39-4110-b6df-032fac253ac2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((283651, 596), (8630, 601))"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "no_asma_2021_df.shape, asma_2021_df.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XLqvJB8Q0w5t",
        "outputId": "df93eb1e-df6d-498f-fc30-e2f715e2da64"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>DIRECTORIO</th>\n",
              "      <th>DIRECTORIO_HOG</th>\n",
              "      <th>DIRECTORIO_PER</th>\n",
              "      <th>NHCCPCTRL2</th>\n",
              "      <th>NHCCP19</th>\n",
              "      <th>NHCCP20</th>\n",
              "      <th>NHCCP35A</th>\n",
              "      <th>COD_LOCALIDAD</th>\n",
              "      <th>NPCFP14F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000003.0</td>\n",
              "      <td>10000031</td>\n",
              "      <td>100000311</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000003.0</td>\n",
              "      <td>10000031</td>\n",
              "      <td>100000312</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003.0</td>\n",
              "      <td>10000031</td>\n",
              "      <td>100000313</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004.0</td>\n",
              "      <td>10000041</td>\n",
              "      <td>100000411</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000007.0</td>\n",
              "      <td>10000071</td>\n",
              "      <td>100000711</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000007.0</td>\n",
              "      <td>10000071</td>\n",
              "      <td>100000712</td>\n",
              "      <td>5</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000007.0</td>\n",
              "      <td>10000071</td>\n",
              "      <td>100000713</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000007.0</td>\n",
              "      <td>10000071</td>\n",
              "      <td>100000714</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000007.0</td>\n",
              "      <td>10000071</td>\n",
              "      <td>100000715</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000008.0</td>\n",
              "      <td>10000081</td>\n",
              "      <td>100000811</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0   1000003.0        10000031       100000311           3        3        2  \\\n",
              "1   1000003.0        10000031       100000312           3        3        2   \n",
              "2   1000003.0        10000031       100000313           3        3        2   \n",
              "3   1000004.0        10000041       100000411           2        2        2   \n",
              "4   1000007.0        10000071       100000711           5        3        3   \n",
              "5   1000007.0        10000071       100000712           5        3        3   \n",
              "6   1000007.0        10000071       100000713           5        3        3   \n",
              "7   1000007.0        10000071       100000714           5        3        3   \n",
              "8   1000007.0        10000071       100000715           5        3        3   \n",
              "9   1000008.0        10000081       100000811           2        3        2   \n",
              "\n",
              "   NHCCP35A  COD_LOCALIDAD  NPCFP14F  \n",
              "0       1.0            7.0         2  \n",
              "1       1.0            7.0         2  \n",
              "2       1.0            7.0         2  \n",
              "3       1.0            7.0         2  \n",
              "4       1.0            7.0         2  \n",
              "5       1.0            7.0         2  \n",
              "6       1.0            7.0         2  \n",
              "7       1.0            7.0         2  \n",
              "8       1.0            7.0         2  \n",
              "9       1.0            7.0         2  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "no_asma_2021_df[features_2021].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "9nnqx3yiA35g",
        "outputId": "7e03de1f-ed45-4635-ab8e-b7fbea151f8f"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIRECTORIO</th>\n",
              "      <th>DIRECTORIO_HOG</th>\n",
              "      <th>DIRECTORIO_PER</th>\n",
              "      <th>NHCCPCTRL2</th>\n",
              "      <th>NHCCP19</th>\n",
              "      <th>NHCCP20</th>\n",
              "      <th>NHCCP35A</th>\n",
              "      <th>COD_LOCALIDAD</th>\n",
              "      <th>NPCFP14F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>10000011</td>\n",
              "      <td>100000111</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000004</td>\n",
              "      <td>10000041</td>\n",
              "      <td>100000412</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000033</td>\n",
              "      <td>10000331</td>\n",
              "      <td>100003313</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000040</td>\n",
              "      <td>10000401</td>\n",
              "      <td>100004011</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000041</td>\n",
              "      <td>10000411</td>\n",
              "      <td>100004111</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000055</td>\n",
              "      <td>10000551</td>\n",
              "      <td>100005516</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000062</td>\n",
              "      <td>10000621</td>\n",
              "      <td>100006211</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000090</td>\n",
              "      <td>10000901</td>\n",
              "      <td>100009011</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000093</td>\n",
              "      <td>10000931</td>\n",
              "      <td>100009312</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000093</td>\n",
              "      <td>10000931</td>\n",
              "      <td>100009313</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0     1000001        10000011       100000111           1        4        1  \\\n",
              "1     1000004        10000041       100000412           2        2        2   \n",
              "2     1000033        10000331       100003313           4        4        3   \n",
              "3     1000040        10000401       100004011           2        3        2   \n",
              "4     1000041        10000411       100004111           3        3        2   \n",
              "5     1000055        10000551       100005516           6        5        4   \n",
              "6     1000062        10000621       100006211           2        5        2   \n",
              "7     1000090        10000901       100009011           3        4        2   \n",
              "8     1000093        10000931       100009312           3        4        2   \n",
              "9     1000093        10000931       100009313           3        4        2   \n",
              "\n",
              "   NHCCP35A  COD_LOCALIDAD  NPCFP14F  \n",
              "0       2.0            7.0         1  \n",
              "1       1.0            7.0         1  \n",
              "2       1.0            5.0         1  \n",
              "3       2.0            9.0         1  \n",
              "4       1.0            9.0         1  \n",
              "5       1.0            4.0         1  \n",
              "6       1.0            4.0         1  \n",
              "7       1.0           11.0         1  \n",
              "8       1.0           11.0         1  \n",
              "9       1.0           11.0         1  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "asma_2021_df[features_2021].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1P7opXJI02t1",
        "outputId": "fbbcf8b0-14d3-4043-8aa9-c149b205a4b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas es: 0\n"
          ]
        }
      ],
      "source": [
        "duplicados = no_asma_2021_df[features_2021].duplicated(subset=['DIRECTORIO_PER'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x1BU-a4A7UD",
        "outputId": "d5366da6-0882-42b1-b174-ed2b8b92ed87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas es: 0\n"
          ]
        }
      ],
      "source": [
        "duplicados = asma_2021_df[features_2021].duplicated(subset=['DIRECTORIO_PER'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4UU3LdTBQaD",
        "outputId": "bfd25bfb-fd8b-42e9-f207-ba641fa9b30f"
      },
      "outputs": [],
      "source": [
        "#UNIR DATASET\n",
        "# Método 1: Usar append()\n",
        "provisional1 = pd.concat([asma_2021_df, no_asma_2021_df], ignore_index=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaGvmkjQBSVF",
        "outputId": "3950f93c-d4f8-4f29-f6f2-20af8b41c4d2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Esteban\\AppData\\Local\\Temp\\ipykernel_1516\\1221507135.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  provisional1['year'] = 2021\n"
          ]
        }
      ],
      "source": [
        "provisional1['year'] = 2021"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Bt7FeoptK-H"
      },
      "outputs": [],
      "source": [
        "features_2021.append('year')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "1O9UPf4ptK7g",
        "outputId": "39146fd5-f2fc-4ab8-a856-578454641ba1"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DIRECTORIO</th>\n",
              "      <th>DIRECTORIO_HOG</th>\n",
              "      <th>DIRECTORIO_PER</th>\n",
              "      <th>NHCCPCTRL2</th>\n",
              "      <th>NHCCP19</th>\n",
              "      <th>NHCCP20</th>\n",
              "      <th>NHCCP35A</th>\n",
              "      <th>COD_LOCALIDAD</th>\n",
              "      <th>NPCFP14F</th>\n",
              "      <th>year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1000001.0</td>\n",
              "      <td>10000011</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>2021</td>\n",
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              "      <th>1</th>\n",
              "      <td>1000004.0</td>\n",
              "      <td>10000041</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000033.0</td>\n",
              "      <td>10000331</td>\n",
              "      <td>100003313</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000040.0</td>\n",
              "      <td>10000401</td>\n",
              "      <td>100004011</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000041.0</td>\n",
              "      <td>10000411</td>\n",
              "      <td>100004111</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000055.0</td>\n",
              "      <td>10000551</td>\n",
              "      <td>100005516</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000062.0</td>\n",
              "      <td>10000621</td>\n",
              "      <td>100006211</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000090.0</td>\n",
              "      <td>10000901</td>\n",
              "      <td>100009011</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000093.0</td>\n",
              "      <td>10000931</td>\n",
              "      <td>100009312</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000093.0</td>\n",
              "      <td>10000931</td>\n",
              "      <td>100009313</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DIRECTORIO  DIRECTORIO_HOG  DIRECTORIO_PER  NHCCPCTRL2  NHCCP19  NHCCP20   \n",
              "0   1000001.0        10000011       100000111           1        4        1  \\\n",
              "1   1000004.0        10000041       100000412           2        2        2   \n",
              "2   1000033.0        10000331       100003313           4        4        3   \n",
              "3   1000040.0        10000401       100004011           2        3        2   \n",
              "4   1000041.0        10000411       100004111           3        3        2   \n",
              "5   1000055.0        10000551       100005516           6        5        4   \n",
              "6   1000062.0        10000621       100006211           2        5        2   \n",
              "7   1000090.0        10000901       100009011           3        4        2   \n",
              "8   1000093.0        10000931       100009312           3        4        2   \n",
              "9   1000093.0        10000931       100009313           3        4        2   \n",
              "\n",
              "   NHCCP35A  COD_LOCALIDAD  NPCFP14F  year  \n",
              "0       2.0            7.0         1  2021  \n",
              "1       1.0            7.0         1  2021  \n",
              "2       1.0            5.0         1  2021  \n",
              "3       2.0            9.0         1  2021  \n",
              "4       1.0            9.0         1  2021  \n",
              "5       1.0            4.0         1  2021  \n",
              "6       1.0            4.0         1  2021  \n",
              "7       1.0           11.0         1  2021  \n",
              "8       1.0           11.0         1  2021  \n",
              "9       1.0           11.0         1  2021  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "provisional1[features_2021].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JGhm0e74z_9"
      },
      "outputs": [],
      "source": [
        "df_2021 = provisional1[features_2021].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CWufBtMJ46ad"
      },
      "outputs": [],
      "source": [
        "# Renombrar las columnas\n",
        "nuevos_nombres = {'DIRECTORIO': 'id_vivienda', 'DIRECTORIO_HOG': 'id_hogar', 'DIRECTORIO_PER': 'id_persona', \n",
        "         'NHCCPCTRL2': 'num_personas_hogar', 'NHCCP19': 'num_cuartos_hogar', 'NHCCP20': 'num_cuartos_ocupados', \n",
        "         'NHCCP35A': 'num_cuartos', 'COD_LOCALIDAD': 'localidad', 'NPCFP14F': 'asma',          \n",
        "                  }\n",
        "df_2021 = df_2021.rename(columns=nuevos_nombres)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoiqaISVz5cV",
        "outputId": "d403129a-3ffa-4bc5-fab1-a1cc64b2897c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "localidad               0.195627\n",
              "num_cuartos             0.031152\n",
              "id_vivienda             0.000000\n",
              "id_hogar                0.000000\n",
              "id_persona              0.000000\n",
              "num_personas_hogar      0.000000\n",
              "num_cuartos_hogar       0.000000\n",
              "num_cuartos_ocupados    0.000000\n",
              "asma                    0.000000\n",
              "year                    0.000000\n",
              "dtype: float64"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(df_2021.isnull().sum() / df_2021.shape[0]).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAgo5FZr8QKS",
        "outputId": "a441f2e0-f7e7-4cbc-d4e8-5874cf4e4771"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "id_vivienda\n",
            "990174.0     21\n",
            "1166213.0    20\n",
            "342432.0     18\n",
            "987009.0     15\n",
            "326730.0     15\n",
            "             ..\n",
            "1152662.0     1\n",
            "1152660.0     1\n",
            "1152658.0     1\n",
            "1152657.0     1\n",
            "999999.0      1\n",
            "Name: count, Length: 106467, dtype: int64\n",
            "\n",
            "\n",
            "id_hogar\n",
            "3267301     15\n",
            "11652041    14\n",
            "10240261    14\n",
            "10643521    13\n",
            "11385171    13\n",
            "            ..\n",
            "11529101     1\n",
            "11529071     1\n",
            "11529061     1\n",
            "11529051     1\n",
            "9999991      1\n",
            "Name: count, Length: 107119, dtype: int64\n",
            "\n",
            "\n",
            "id_persona\n",
            "100000111    1\n",
            "192803214    1\n",
            "192803912    1\n",
            "192803911    1\n",
            "192803412    1\n",
            "            ..\n",
            "110101813    1\n",
            "110101814    1\n",
            "110101911    1\n",
            "110101912    1\n",
            "99999911     1\n",
            "Name: count, Length: 292281, dtype: int64\n",
            "\n",
            "\n",
            "num_personas_hogar\n",
            "3     81513\n",
            "4     75480\n",
            "2     59292\n",
            "5     33500\n",
            "1     21538\n",
            "6     12474\n",
            "7      4907\n",
            "8      2128\n",
            "9       729\n",
            "10      370\n",
            "11      220\n",
            "12       48\n",
            "13       39\n",
            "14       28\n",
            "15       15\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos_hogar\n",
            "4     98550\n",
            "3     94181\n",
            "2     39662\n",
            "5     33623\n",
            "1     12997\n",
            "6      8687\n",
            "7      2448\n",
            "8      1120\n",
            "9       492\n",
            "10      281\n",
            "11       97\n",
            "99       39\n",
            "12       29\n",
            "13       22\n",
            "14       22\n",
            "15       19\n",
            "16        9\n",
            "19        3\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos_ocupados\n",
            "2     120701\n",
            "3      87111\n",
            "1      66441\n",
            "4      15149\n",
            "5       2208\n",
            "6        451\n",
            "7        141\n",
            "99        39\n",
            "8         26\n",
            "9         14\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "num_cuartos\n",
            "1.0     181725\n",
            "2.0      83335\n",
            "3.0      13919\n",
            "4.0       3175\n",
            "5.0        798\n",
            "6.0        166\n",
            "7.0         47\n",
            "8.0          6\n",
            "10.0         4\n",
            "9.0          1\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "localidad\n",
            "8.0     26641\n",
            "11.0    24536\n",
            "10.0    17780\n",
            "19.0    17723\n",
            "1.0     17179\n",
            "9.0     16781\n",
            "5.0     13602\n",
            "4.0     12616\n",
            "7.0     12565\n",
            "18.0    11796\n",
            "13.0    11386\n",
            "3.0      9949\n",
            "16.0     8992\n",
            "2.0      8814\n",
            "12.0     6775\n",
            "14.0     4736\n",
            "6.0      4608\n",
            "15.0     4391\n",
            "17.0     2247\n",
            "20.0     1986\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "asma\n",
            "2    283651\n",
            "1      8630\n",
            "Name: count, dtype: int64\n",
            "\n",
            "\n",
            "year\n",
            "2021    292281\n",
            "Name: count, dtype: int64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for value in df_2021:\n",
        "  print() \n",
        "  print(df_2021[value].value_counts())\n",
        "  print() "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "vK59upJ6CV4T"
      },
      "source": [
        "# Dataset unificado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLOgLI90D6fm",
        "outputId": "822af2d0-b018-460f-97b7-4568327f13bd"
      },
      "outputs": [],
      "source": [
        "#UNIR DATASET\n",
        "# Método 1: Usar append()\n",
        "\n",
        "unificado = pd.concat([df_2017, df_2021], ignore_index=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOp59FkBEEhF",
        "outputId": "e0ea9b79-ec22-411b-f07a-85e97ace85f8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(612233, 12)"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unificado.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Qc8pZluUEdix",
        "outputId": "55a25ba2-696f-48f3-a750-e625acbb3020"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos_ocupados</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>358115</th>\n",
              "      <td>1028222.0</td>\n",
              "      <td>10282221</td>\n",
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              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245540</th>\n",
              "      <td>250555.0</td>\n",
              "      <td>2505551</td>\n",
              "      <td>25055511</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>484667</th>\n",
              "      <td>1159876.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304623</th>\n",
              "      <td>294255.0</td>\n",
              "      <td>2942551</td>\n",
              "      <td>29425514</td>\n",
              "      <td>4</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169187</th>\n",
              "      <td>184420.0</td>\n",
              "      <td>1844201</td>\n",
              "      <td>18442013</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540568</th>\n",
              "      <td>324842.0</td>\n",
              "      <td>3248421</td>\n",
              "      <td>32484211</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37009</th>\n",
              "      <td>125201.0</td>\n",
              "      <td>1252011</td>\n",
              "      <td>12520112</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351992</th>\n",
              "      <td>1023975.0</td>\n",
              "      <td>10239751</td>\n",
              "      <td>102397511</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154589</th>\n",
              "      <td>179051.0</td>\n",
              "      <td>1790511</td>\n",
              "      <td>17905111</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343464</th>\n",
              "      <td>1015215.0</td>\n",
              "      <td>10152151</td>\n",
              "      <td>101521514</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
              "358115    1028222.0  10282221   102822212                   2  \\\n",
              "245540     250555.0   2505551    25055511                   4   \n",
              "484667    1159876.0  11598761   115987612                   2   \n",
              "304623     294255.0   2942551    29425514                   4   \n",
              "169187     184420.0   1844201    18442013                   3   \n",
              "540568     324842.0   3248421    32484211                   5   \n",
              "37009      125201.0   1252011    12520112                   2   \n",
              "351992    1023975.0  10239751   102397511                   4   \n",
              "154589     179051.0   1790511    17905111                   4   \n",
              "343464    1015215.0  10152151   101521514                   4   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos_ocupados  num_cuartos  localidad   \n",
              "358115                  6                     1          2.0        8.0  \\\n",
              "245540                  5                     3          2.0       15.0   \n",
              "484667                  2                     1          1.0        8.0   \n",
              "304623                  3                     2          1.0        4.0   \n",
              "169187                  4                     3          3.0        8.0   \n",
              "540568                  3                     2          1.0        8.0   \n",
              "37009                   1                     1          1.0       15.0   \n",
              "351992                  3                     2          1.0        8.0   \n",
              "154589                  3                     3          1.0        8.0   \n",
              "343464                  4                     3          2.0        8.0   \n",
              "\n",
              "        iluminacion_sala  iluminacion_cuartos  asma  year  \n",
              "358115               2.0                  2.0     2  2021  \n",
              "245540               1.0                  1.0     2  2017  \n",
              "484667               2.0                  2.0     2  2021  \n",
              "304623               1.0                  1.0     2  2017  \n",
              "169187               1.0                  1.0     2  2017  \n",
              "540568               2.0                  2.0     2  2021  \n",
              "37009                2.0                  1.0     2  2017  \n",
              "351992               2.0                  2.0     2  2021  \n",
              "154589               1.0                  1.0     2  2017  \n",
              "343464               2.0                  2.0     2  2021  "
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unificado.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RprAcUK_ECLI",
        "outputId": "15c68935-8876-4ca1-c5e8-e88551537b43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "iluminacion_sala        0.477402\n",
              "iluminacion_cuartos     0.477402\n",
              "localidad               0.253696\n",
              "num_cuartos             0.032110\n",
              "id_vivienda             0.000000\n",
              "id_hogar                0.000000\n",
              "id_persona              0.000000\n",
              "num_personas_hogar      0.000000\n",
              "num_cuartos_hogar       0.000000\n",
              "num_cuartos_ocupados    0.000000\n",
              "asma                    0.000000\n",
              "year                    0.000000\n",
              "dtype: float64"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(unificado.isnull().sum() / unificado.shape[0]).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vl8J6oQqFNKy"
      },
      "outputs": [],
      "source": [
        "unificado['num_cuartos'].fillna(unificado['num_cuartos'].mode()[0],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A5NAChS2FQjw"
      },
      "outputs": [],
      "source": [
        "unificado['localidad'].fillna(unificado['localidad'].mode()[0],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "unificado['iluminacion_sala'] = unificado['iluminacion_sala'].replace(0, 2)\n",
        "unificado['iluminacion_cuartos'] = unificado['iluminacion_cuartos'].replace(0, 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "iluminacion_cuartos\n",
              "2.0    310851\n",
              "1.0    301324\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unificado['iluminacion_cuartos'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "iluminacion_sala\n",
              "2.0    322285\n",
              "1.0    289890\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unificado['iluminacion_sala'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Y1iPf5aHLdu"
      },
      "outputs": [],
      "source": [
        "unificado = unificado.drop_duplicates(subset='id_persona')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id_vivienda             0.0\n",
              "id_hogar                0.0\n",
              "id_persona              0.0\n",
              "num_personas_hogar      0.0\n",
              "num_cuartos_hogar       0.0\n",
              "num_cuartos_ocupados    0.0\n",
              "num_cuartos             0.0\n",
              "localidad               0.0\n",
              "iluminacion_sala        0.0\n",
              "iluminacion_cuartos     0.0\n",
              "asma                    0.0\n",
              "year                    0.0\n",
              "dtype: float64"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(unificado.isnull().sum() / unificado.shape[0]).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tkIQIDBDFmMY"
      },
      "outputs": [],
      "source": [
        "unificado.to_csv('./data/unificado_asma.csv', index=False)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "j19WPa1HF0vI"
      },
      "source": [
        "# Gráficas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "auli7GVSF3Zg"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.read_csv('./unificado_asma.csv', encoding=\"utf-8\", sep = ',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "UussOIXQGGA4",
        "outputId": "8d7692d6-294b-48b0-821a-34aecb0fb6b9"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos_ocupados</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>579219</th>\n",
              "      <td>344033.0</td>\n",
              "      <td>3440331</td>\n",
              "      <td>34403314</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27468</th>\n",
              "      <td>112290.0</td>\n",
              "      <td>1122901</td>\n",
              "      <td>11229012</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370633</th>\n",
              "      <td>1035118.0</td>\n",
              "      <td>10351181</td>\n",
              "      <td>103511811</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602820</th>\n",
              "      <td>994518.0</td>\n",
              "      <td>9945181</td>\n",
              "      <td>99451812</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214860</th>\n",
              "      <td>232093.0</td>\n",
              "      <td>2320931</td>\n",
              "      <td>23209312</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>551318</th>\n",
              "      <td>330520.0</td>\n",
              "      <td>3305201</td>\n",
              "      <td>33052013</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593459</th>\n",
              "      <td>982289.0</td>\n",
              "      <td>9822891</td>\n",
              "      <td>98228913</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54321</th>\n",
              "      <td>134885.0</td>\n",
              "      <td>1348851</td>\n",
              "      <td>13488511</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420523</th>\n",
              "      <td>1102470.0</td>\n",
              "      <td>11024701</td>\n",
              "      <td>110247012</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38745</th>\n",
              "      <td>126602.0</td>\n",
              "      <td>1266021</td>\n",
              "      <td>12660212</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ],
            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar  \\\n",
              "579219     344033.0   3440331    34403314                   4   \n",
              "27468      112290.0   1122901    11229012                   4   \n",
              "370633    1035118.0  10351181   103511811                   1   \n",
              "602820     994518.0   9945181    99451812                   3   \n",
              "214860     232093.0   2320931    23209312                   6   \n",
              "551318     330520.0   3305201    33052013                   3   \n",
              "593459     982289.0   9822891    98228913                   3   \n",
              "54321      134885.0   1348851    13488511                   1   \n",
              "420523    1102470.0  11024701   110247012                   2   \n",
              "38745      126602.0   1266021    12660212                   2   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos_ocupados  num_cuartos  localidad  asma  \\\n",
              "579219                  3                     2          2.0        7.0     2   \n",
              "27468                   4                     2          1.0        4.0     2   \n",
              "370633                  1                     1          1.0        8.0     2   \n",
              "602820                  4                     3          2.0       11.0     2   \n",
              "214860                  3                     2          1.0        5.0     2   \n",
              "551318                  2                     2          1.0        3.0     2   \n",
              "593459                  3                     2          1.0       19.0     2   \n",
              "54321                   2                     1          1.0        8.0     2   \n",
              "420523                  4                     1          2.0        1.0     2   \n",
              "38745                   3                     2          1.0        8.0     2   \n",
              "\n",
              "        year  \n",
              "579219  2021  \n",
              "27468   2017  \n",
              "370633  2021  \n",
              "602820  2021  \n",
              "214860  2017  \n",
              "551318  2021  \n",
              "593459  2021  \n",
              "54321   2017  \n",
              "420523  2021  \n",
              "38745   2017  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVGjcdCfGhns",
        "outputId": "bbde84bf-276e-4d96-e646-0773b10b48a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas es: 0\n"
          ]
        }
      ],
      "source": [
        "duplicados = df_asma.duplicated(subset=['id_persona'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hAmHxTO5J8-L"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Supongamos que tienes un DataFrame llamado 'df' con las columnas mencionadas en la fórmula\n",
        "\n",
        "# Calcular el resultado de la fórmula\n",
        "df_asma['ocupacion'] = df_asma['num_personas_hogar'] / df_asma['num_cuartos_ocupados']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "d5oc92GvNEqs",
        "outputId": "15ae7022-16f6-49c0-d65e-83bf2d9bf11e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='asma', ylabel='ocupacion'>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Crear la gráfica de barras con seaborn\n",
        "sns.scatterplot(x='asma', y='ocupacion', data=df_asma)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Mm3euhJQMYS1",
        "outputId": "19c7c4d1-7a46-4e92-fda4-339d160cb09a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(x='localidad', hue='asma', data=df_asma)\n",
        "plt.title('Personas con y sin asma por localidad')\n",
        "plt.xlabel('Localidad')\n",
        "plt.ylabel('Cantidad de personas')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "WaOI2ue1P5wN",
        "outputId": "a7f8897d-ddce-4561-8c97-c4efcef6db0c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x='num_personas_hogar', y='num_cuartos_ocupados', hue='asma', data=df_asma, palette=['blue', 'red'])\n",
        "plt.title('Relación entre número de personas en el hogar y número de cuartos ocupados')\n",
        "plt.xlabel('Número de personas en el hogar')\n",
        "plt.ylabel('Número de cuartos ocupados')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HxX8kFkHQAbj",
        "outputId": "335542d9-92a3-4e0a-8146-9e3557d9cf59"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "sns.scatterplot(x='asma', y='num_personas_hogar', data=df_asma)\n",
        "plt.title('Número de personas en el hogar')\n",
        "plt.xlabel('Tener asma')\n",
        "plt.ylabel('Número de personas en el hogar')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fNKYaulvRz_O",
        "outputId": "eb54406f-ed5d-4e18-f54b-a335447280a0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_counts = df_asma.groupby(['year', 'asma']).size().reset_index(name='count')\n",
        "sns.barplot(x='year', y='count', hue='asma', data=df_counts)\n",
        "plt.title('Presencia o ausencia de asma por año')\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Cantidad de personas')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "mn0WQi8oUYjE",
        "outputId": "89a44f0a-1b21-49da-f0c8-e20f0ebb0034"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "sns.scatterplot(x='asma', y='num_cuartos_hogar', data=df_asma)\n",
        "plt.title('Número de personas en el hogar')\n",
        "plt.xlabel('Tener asma')\n",
        "plt.ylabel('Número de cuartos en el hogar')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-4BRuiMga2JM"
      },
      "source": [
        "# ETL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Jy-KlWtbM-c"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y930rrFia5Ck"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.read_csv('./data/unificado_asma.csv', encoding=\"utf-8\", sep = ',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGtyfHmOVU9r",
        "outputId": "3af43bad-94d0-4fc4-d814-fc33212559be"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(612175, 12)"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iBc_ecchbEuo",
        "outputId": "23ceccba-3eea-424e-c0b7-4dfeb8145e15"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos_ocupados</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>192482</th>\n",
              "      <td>221389.0</td>\n",
              "      <td>2213891</td>\n",
              "      <td>22138913</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113622</th>\n",
              "      <td>160693.0</td>\n",
              "      <td>1606931</td>\n",
              "      <td>16069312</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342774</th>\n",
              "      <td>1013974.0</td>\n",
              "      <td>10139741</td>\n",
              "      <td>101397414</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390213</th>\n",
              "      <td>1046136.0</td>\n",
              "      <td>10461361</td>\n",
              "      <td>104613612</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575526</th>\n",
              "      <td>342111.0</td>\n",
              "      <td>3421111</td>\n",
              "      <td>34211113</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322076</th>\n",
              "      <td>1054562.0</td>\n",
              "      <td>10545621</td>\n",
              "      <td>105456213</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46082</th>\n",
              "      <td>129827.0</td>\n",
              "      <td>1298271</td>\n",
              "      <td>12982711</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>388603</th>\n",
              "      <td>1044911.0</td>\n",
              "      <td>10449111</td>\n",
              "      <td>104491112</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420463</th>\n",
              "      <td>1102445.0</td>\n",
              "      <td>11024451</td>\n",
              "      <td>110244511</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477344</th>\n",
              "      <td>1156509.0</td>\n",
              "      <td>11565091</td>\n",
              "      <td>115650911</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
              "192482     221389.0   2213891    22138913                   3  \\\n",
              "113622     160693.0   1606931    16069312                   2   \n",
              "342774    1013974.0  10139741   101397414                   4   \n",
              "390213    1046136.0  10461361   104613612                   3   \n",
              "575526     342111.0   3421111    34211113                   4   \n",
              "322076    1054562.0  10545621   105456213                   4   \n",
              "46082      129827.0   1298271    12982711                   1   \n",
              "388603    1044911.0  10449111   104491112                   4   \n",
              "420463    1102445.0  11024451   110244511                   3   \n",
              "477344    1156509.0  11565091   115650911                   2   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos_ocupados  num_cuartos  localidad   \n",
              "192482                  2                     1          1.0       19.0  \\\n",
              "113622                  4                     3          1.0        8.0   \n",
              "342774                  3                     2          1.0        8.0   \n",
              "390213                  4                     3          1.0        5.0   \n",
              "575526                  3                     2          1.0        7.0   \n",
              "322076                  5                     3          2.0        1.0   \n",
              "46082                   2                     1          1.0       19.0   \n",
              "388603                  4                     3          1.0        3.0   \n",
              "420463                  5                     3          2.0        7.0   \n",
              "477344                  4                     2          3.0       13.0   \n",
              "\n",
              "        iluminacion_sala  iluminacion_cuartos  asma  year  \n",
              "192482               1.0                  1.0     2  2017  \n",
              "113622               1.0                  1.0     2  2017  \n",
              "342774               2.0                  2.0     2  2021  \n",
              "390213               2.0                  2.0     2  2021  \n",
              "575526               2.0                  2.0     2  2021  \n",
              "322076               2.0                  2.0     1  2021  \n",
              "46082                2.0                  1.0     2  2017  \n",
              "388603               2.0                  2.0     2  2021  \n",
              "420463               2.0                  2.0     2  2021  \n",
              "477344               2.0                  2.0     2  2021  "
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XACsdY6ZbwiY",
        "outputId": "08f12aaf-af85-4df1-e0ad-835ea0bacfa2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
            "27       101543.0   1015431    10154311                   7  \\\n",
            "28       101543.0   1015431    10154315                   7   \n",
            "5870     101543.0   1015431    10154312                   7   \n",
            "5871     101543.0   1015431    10154313                   7   \n",
            "5872     101543.0   1015431    10154314                   7   \n",
            "5873     101543.0   1015431    10154316                   7   \n",
            "5874     101543.0   1015431    10154317                   7   \n",
            "\n",
            "      num_cuartos_hogar  num_cuartos_ocupados  num_cuartos  localidad   \n",
            "27                    2                     2          1.0       14.0  \\\n",
            "28                    2                     2          1.0       14.0   \n",
            "5870                  2                     2          1.0       14.0   \n",
            "5871                  2                     2          1.0       14.0   \n",
            "5872                  2                     2          1.0       14.0   \n",
            "5873                  2                     2          1.0       14.0   \n",
            "5874                  2                     2          1.0       14.0   \n",
            "\n",
            "      iluminacion_sala  iluminacion_cuartos  asma  year  \n",
            "27                 1.0                  1.0     1  2017  \n",
            "28                 1.0                  1.0     1  2017  \n",
            "5870               1.0                  1.0     2  2017  \n",
            "5871               1.0                  1.0     2  2017  \n",
            "5872               1.0                  1.0     2  2017  \n",
            "5873               1.0                  1.0     2  2017  \n",
            "5874               1.0                  1.0     2  2017  \n"
          ]
        }
      ],
      "source": [
        "resultado = df_asma.loc[df_asma['id_vivienda'] == 101543.0]\n",
        "\n",
        "print(resultado)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZR_qXqmqbEr-",
        "outputId": "f1494d9a-f0ed-45df-c053-470e6e85cecc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El número de filas duplicadas es: 398516\n"
          ]
        }
      ],
      "source": [
        "duplicados = df_asma.duplicated(subset=['id_vivienda'])\n",
        "numero_duplicados = duplicados.sum()\n",
        "print(\"El número de filas duplicadas es:\", numero_duplicados)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Calcula el porcentaje\n",
        "def calcular_porcentaje(row):\n",
        "    if row['iluminacion_sala'] == 1 and row['iluminacion_cuartos'] == 1:\n",
        "        return 100\n",
        "    elif row['iluminacion_sala'] == 2 and row['iluminacion_cuartos'] == 2:\n",
        "        return 0\n",
        "    else:\n",
        "        return 50\n",
        "\n",
        "#129827.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_asma['porcentaje_iluminacion'] = df_asma.apply(lambda x: calcular_porcentaje(x), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a_HQEC2abEpU"
      },
      "outputs": [],
      "source": [
        "#indice de hacinamiento\n",
        "df_asma['ocupacion'] = df_asma['num_personas_hogar'] / df_asma['num_cuartos_hogar']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u61cv1MiXgMf",
        "outputId": "49a1461d-8c50-4cfe-a5fb-5220533ab675"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id_vivienda\n",
            "101000.0     0\n",
            "101001.0     0\n",
            "101002.0     0\n",
            "101003.0     0\n",
            "101004.0     0\n",
            "            ..\n",
            "3006808.0    0\n",
            "3006809.0    0\n",
            "3006810.0    0\n",
            "3006811.0    0\n",
            "3006812.0    0\n",
            "Name: asma, Length: 213659, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#Porcentaje de hacinamiento\n",
        "\n",
        "count_by_vivienda = df_asma.groupby('id_vivienda')['asma'].apply(lambda x: (x == 1).sum())\n",
        "\n",
        "# Imprimir el resultado\n",
        "print(count_by_vivienda)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GeLg2mEZrCV",
        "outputId": "ae6bb474-0016-4ee6-895e-f3a882a81dfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
            "0          101033.0   1010331    10103311                   1  \\\n",
            "1          101041.0   1010411    10104112                   3   \n",
            "2          101042.0   1010421    10104213                   5   \n",
            "3          101047.0   1010471    10104713                   3   \n",
            "4          101059.0   1010591    10105914                   5   \n",
            "...             ...       ...         ...                 ...   \n",
            "612170     999996.0   9999961    99999612                   2   \n",
            "612171     999997.0   9999971    99999711                   1   \n",
            "612172     999998.0   9999981    99999811                   2   \n",
            "612173     999998.0   9999981    99999812                   2   \n",
            "612174     999999.0   9999991    99999911                   1   \n",
            "\n",
            "        num_cuartos_hogar  num_cuartos_ocupados  num_cuartos  localidad   \n",
            "0                       4                     1          2.0       11.0  \\\n",
            "1                       1                     1          1.0        1.0   \n",
            "2                       4                     3          1.0        1.0   \n",
            "3                       3                     3          1.0        1.0   \n",
            "4                       4                     4          2.0       11.0   \n",
            "...                   ...                   ...          ...        ...   \n",
            "612170                  2                     2          1.0        7.0   \n",
            "612171                  2                     1          1.0        7.0   \n",
            "612172                  2                     2          1.0        7.0   \n",
            "612173                  2                     2          1.0        7.0   \n",
            "612174                  2                     1          1.0        7.0   \n",
            "\n",
            "        iluminacion_sala  iluminacion_cuartos  asma  year  ocupacion   \n",
            "0                    1.0                  1.0     1  2017       0.25  \\\n",
            "1                    2.0                  2.0     1  2017       3.00   \n",
            "2                    1.0                  1.0     1  2017       1.25   \n",
            "3                    2.0                  1.0     1  2017       1.00   \n",
            "4                    1.0                  1.0     1  2017       1.25   \n",
            "...                  ...                  ...   ...   ...        ...   \n",
            "612170               2.0                  2.0     2  2021       1.00   \n",
            "612171               2.0                  2.0     2  2021       0.50   \n",
            "612172               2.0                  2.0     2  2021       1.00   \n",
            "612173               2.0                  2.0     2  2021       1.00   \n",
            "612174               2.0                  2.0     2  2021       0.50   \n",
            "\n",
            "        cantidad_asma  \n",
            "0                   1  \n",
            "1                   1  \n",
            "2                   1  \n",
            "3                   1  \n",
            "4                   2  \n",
            "...               ...  \n",
            "612170              0  \n",
            "612171              0  \n",
            "612172              0  \n",
            "612173              0  \n",
            "612174              0  \n",
            "\n",
            "[612175 rows x 14 columns]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "df_asma['cantidad_asma'] = df_asma['id_vivienda'].map(count_by_vivienda)\n",
        "print(df_asma)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "SHHerWQGWORy",
        "outputId": "710aab98-0f9f-414e-95df-d1183bcdd634"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos_ocupados</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "      <th>ocupacion</th>\n",
              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101033.0</td>\n",
              "      <td>1010331</td>\n",
              "      <td>10103311</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101041.0</td>\n",
              "      <td>1010411</td>\n",
              "      <td>10104112</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101042.0</td>\n",
              "      <td>1010421</td>\n",
              "      <td>10104213</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101047.0</td>\n",
              "      <td>1010471</td>\n",
              "      <td>10104713</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101059.0</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105914</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>101059.0</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>101063.0</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108911</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101142.0</td>\n",
              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101158.0</td>\n",
              "      <td>1011582</td>\n",
              "      <td>10115822</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>101189.0</td>\n",
              "      <td>1011891</td>\n",
              "      <td>10118916</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>101200.0</td>\n",
              "      <td>1012001</td>\n",
              "      <td>10120013</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>101208.0</td>\n",
              "      <td>1012081</td>\n",
              "      <td>10120812</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>101238.0</td>\n",
              "      <td>1012381</td>\n",
              "      <td>10123812</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>101270.0</td>\n",
              "      <td>1012701</td>\n",
              "      <td>10127011</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>101281.0</td>\n",
              "      <td>1012811</td>\n",
              "      <td>10128111</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>101291.0</td>\n",
              "      <td>1012911</td>\n",
              "      <td>10129118</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>101296.0</td>\n",
              "      <td>1012961</td>\n",
              "      <td>10129614</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>101397.0</td>\n",
              "      <td>1013971</td>\n",
              "      <td>10139713</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id_vivienda  id_hogar  id_persona  num_personas_hogar  num_cuartos_hogar   \n",
              "0      101033.0   1010331    10103311                   1                  4  \\\n",
              "1      101041.0   1010411    10104112                   3                  1   \n",
              "2      101042.0   1010421    10104213                   5                  4   \n",
              "3      101047.0   1010471    10104713                   3                  3   \n",
              "4      101059.0   1010591    10105914                   5                  4   \n",
              "5      101059.0   1010591    10105915                   5                  4   \n",
              "6      101063.0   1010631    10106311                   3                  5   \n",
              "7      101089.0   1010891    10108911                   4                  4   \n",
              "8      101089.0   1010891    10108914                   4                  4   \n",
              "9      101142.0   1011422    10114221                   3                  2   \n",
              "10     101158.0   1011582    10115822                   2                  1   \n",
              "11     101189.0   1011891    10118916                   6                  4   \n",
              "12     101200.0   1012001    10120013                   3                  3   \n",
              "13     101208.0   1012081    10120812                   3                  3   \n",
              "14     101238.0   1012381    10123812                   2                  3   \n",
              "15     101270.0   1012701    10127011                   2                  4   \n",
              "16     101281.0   1012811    10128111                   4                  2   \n",
              "17     101291.0   1012911    10129118                   8                  3   \n",
              "18     101296.0   1012961    10129614                  10                  2   \n",
              "19     101397.0   1013971    10139713                   6                  3   \n",
              "\n",
              "    num_cuartos_ocupados  num_cuartos  localidad  iluminacion_sala   \n",
              "0                      1          2.0       11.0               1.0  \\\n",
              "1                      1          1.0        1.0               2.0   \n",
              "2                      3          1.0        1.0               1.0   \n",
              "3                      3          1.0        1.0               2.0   \n",
              "4                      4          2.0       11.0               1.0   \n",
              "5                      4          2.0       11.0               1.0   \n",
              "6                      3          1.0       11.0               1.0   \n",
              "7                      3          2.0       11.0               1.0   \n",
              "8                      3          2.0       11.0               1.0   \n",
              "9                      1          1.0       13.0               1.0   \n",
              "10                     1          1.0       10.0               2.0   \n",
              "11                     3          2.0        4.0               1.0   \n",
              "12                     2          1.0        5.0               1.0   \n",
              "13                     3          2.0        5.0               1.0   \n",
              "14                     2          1.0        5.0               1.0   \n",
              "15                     2          1.0        6.0               1.0   \n",
              "16                     2          1.0        6.0               2.0   \n",
              "17                     2          1.0       19.0               1.0   \n",
              "18                     2          1.0       19.0               1.0   \n",
              "19                     2          1.0       11.0               2.0   \n",
              "\n",
              "    iluminacion_cuartos  asma  year  ocupacion  cantidad_asma   \n",
              "0                   1.0     1  2017   0.250000              1  \\\n",
              "1                   2.0     1  2017   3.000000              1   \n",
              "2                   1.0     1  2017   1.250000              1   \n",
              "3                   1.0     1  2017   1.000000              1   \n",
              "4                   1.0     1  2017   1.250000              2   \n",
              "5                   1.0     1  2017   1.250000              2   \n",
              "6                   1.0     1  2017   0.600000              1   \n",
              "7                   1.0     1  2017   1.000000              2   \n",
              "8                   1.0     1  2017   1.000000              2   \n",
              "9                   1.0     1  2017   1.500000              1   \n",
              "10                  2.0     1  2017   2.000000              1   \n",
              "11                  1.0     1  2017   1.500000              1   \n",
              "12                  2.0     1  2017   1.000000              1   \n",
              "13                  1.0     1  2017   1.000000              1   \n",
              "14                  1.0     1  2017   0.666667              1   \n",
              "15                  1.0     1  2017   0.500000              1   \n",
              "16                  1.0     1  2017   2.000000              1   \n",
              "17                  1.0     1  2017   2.666667              1   \n",
              "18                  1.0     1  2017   5.000000              1   \n",
              "19                  2.0     1  2017   2.000000              1   \n",
              "\n",
              "    porcentaje_iluminacion  \n",
              "0                      100  \n",
              "1                        0  \n",
              "2                      100  \n",
              "3                       50  \n",
              "4                      100  \n",
              "5                      100  \n",
              "6                      100  \n",
              "7                      100  \n",
              "8                      100  \n",
              "9                      100  \n",
              "10                       0  \n",
              "11                     100  \n",
              "12                      50  \n",
              "13                     100  \n",
              "14                     100  \n",
              "15                     100  \n",
              "16                      50  \n",
              "17                     100  \n",
              "18                     100  \n",
              "19                       0  "
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EEQtywxE4DJA"
      },
      "outputs": [],
      "source": [
        "df_asma['asma'] = df_asma['asma'].replace({1: 'si', 2: 'no'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "replace_localidades = {1:'Usaquén', 2:'Chapinero', 3:'Santafé', 4:'San Cristó', 5:'Usme', 6:'Tunjuelito', 7:'Bosa',\n",
        "                     8:'Kennedy', 9:'Fontibón', 10:'Engativá', 11:'Suba', 12:'Barrios Unidos', 13:'Teusaquillo', 14:'Los Mártires',\n",
        "                     15:'Antonio Nariño', 16:'Puente Aranda',17:'La Candelaria', 18:'Rafael Uribe Uribe', 19:'Ciudad Bolívar',\n",
        "                     20:'Sumapaz', 21:'Otra localidad rural'}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c92mXUqB4nwV"
      },
      "outputs": [],
      "source": [
        "df_asma['localidad'] = df_asma['localidad'].replace(replace_localidades)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uJkpnoGeC1r_"
      },
      "outputs": [],
      "source": [
        "#Eliminamos la columna num_cuartos_ocupados\n",
        "df_asma = df_asma.drop('num_cuartos_ocupados', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "CMywj3T1C1mU",
        "outputId": "3dcea6fb-438b-4128-c84f-d05634770172"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "      <th>ocupacion</th>\n",
              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101033.0</td>\n",
              "      <td>1010331</td>\n",
              "      <td>10103311</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101041.0</td>\n",
              "      <td>1010411</td>\n",
              "      <td>10104112</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101042.0</td>\n",
              "      <td>1010421</td>\n",
              "      <td>10104213</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101047.0</td>\n",
              "      <td>1010471</td>\n",
              "      <td>10104713</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101059.0</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105914</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>101059.0</td>\n",
              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>101063.0</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108911</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101142.0</td>\n",
              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Teusaquillo</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id_vivienda  id_hogar  id_persona  num_personas_hogar  num_cuartos_hogar   \n",
              "0     101033.0   1010331    10103311                   1                  4  \\\n",
              "1     101041.0   1010411    10104112                   3                  1   \n",
              "2     101042.0   1010421    10104213                   5                  4   \n",
              "3     101047.0   1010471    10104713                   3                  3   \n",
              "4     101059.0   1010591    10105914                   5                  4   \n",
              "5     101059.0   1010591    10105915                   5                  4   \n",
              "6     101063.0   1010631    10106311                   3                  5   \n",
              "7     101089.0   1010891    10108911                   4                  4   \n",
              "8     101089.0   1010891    10108914                   4                  4   \n",
              "9     101142.0   1011422    10114221                   3                  2   \n",
              "\n",
              "   num_cuartos    localidad  iluminacion_sala  iluminacion_cuartos asma  year   \n",
              "0          2.0         Suba               1.0                  1.0   si  2017  \\\n",
              "1          1.0      Usaquén               2.0                  2.0   si  2017   \n",
              "2          1.0      Usaquén               1.0                  1.0   si  2017   \n",
              "3          1.0      Usaquén               2.0                  1.0   si  2017   \n",
              "4          2.0         Suba               1.0                  1.0   si  2017   \n",
              "5          2.0         Suba               1.0                  1.0   si  2017   \n",
              "6          1.0         Suba               1.0                  1.0   si  2017   \n",
              "7          2.0         Suba               1.0                  1.0   si  2017   \n",
              "8          2.0         Suba               1.0                  1.0   si  2017   \n",
              "9          1.0  Teusaquillo               1.0                  1.0   si  2017   \n",
              "\n",
              "   ocupacion  cantidad_asma  porcentaje_iluminacion  \n",
              "0       0.25              1                     100  \n",
              "1       3.00              1                       0  \n",
              "2       1.25              1                     100  \n",
              "3       1.00              1                      50  \n",
              "4       1.25              2                     100  \n",
              "5       1.25              2                     100  \n",
              "6       0.60              1                     100  \n",
              "7       1.00              2                     100  \n",
              "8       1.00              2                     100  \n",
              "9       1.50              1                     100  "
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "YAhzoGWMzNEu",
        "outputId": "cfa64f94-b1d6-4a34-ac71-0cc5ed8d03aa"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>id_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   year  id_year\n",
              "0  2017        1\n",
              "1  2021        2"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Dimensión año\n",
        "df_year = pd.DataFrame({'year': [2017, 2021]})\n",
        "#agremamos una columna id_year\n",
        "df_year['id_year'] = range(1, len(df_year) + 1)\n",
        "df_year.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UyMUMaVJOeX8"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.merge(df_asma, df_year[['year', 'id_year']], on='year', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "OEZMF4ogPMQO",
        "outputId": "8bd75227-4d36-48bf-d6dd-1ee8d4208d9b"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
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              "      <th>id_year</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>289649</th>\n",
              "      <td>280695.0</td>\n",
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              "      <td>28069511</td>\n",
              "      <td>1</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
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              "      <td>100</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>290782</th>\n",
              "      <td>281230.0</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>24314</th>\n",
              "      <td>111147.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>Suba</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2021</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>107340</th>\n",
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              "      <td>2017</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>160660</th>\n",
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              "      <td>2</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
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              "      <th>548698</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>127935</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
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            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
              "289649     280695.0   2806951    28069511                   1  \\\n",
              "290782     281230.0   2812301    28123012                   2   \n",
              "24314      111147.0   1111471    11114711                   3   \n",
              "351951    1023962.0  10239621   102396212                   2   \n",
              "511492    1911341.0  19113411   191134111                   3   \n",
              "107340     158422.0   1584221    15842211                   1   \n",
              "160660     181192.0   1811921    18119212                   2   \n",
              "548698     329195.0   3291951    32919514                   5   \n",
              "127935     166490.0   1664901    16649012                   3   \n",
              "336490    1004024.0  10040241   100402411                   4   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos           localidad  iluminacion_sala   \n",
              "289649                  4          3.0             Usaquén               1.0  \\\n",
              "290782                  2          1.0  Rafael Uribe Uribe               1.0   \n",
              "24314                   2          1.0                Usme               1.0   \n",
              "351951                  3          1.0                Suba               2.0   \n",
              "511492                  4          2.0         Teusaquillo               2.0   \n",
              "107340                  3          1.0             Kennedy               1.0   \n",
              "160660                  2          1.0             Kennedy               2.0   \n",
              "548698                  2          1.0          San Cristó               2.0   \n",
              "127935                  5          1.0             Kennedy               1.0   \n",
              "336490                  2          1.0                Usme               2.0   \n",
              "\n",
              "        iluminacion_cuartos asma  year  ocupacion  cantidad_asma   \n",
              "289649                  1.0   no  2017   0.250000              0  \\\n",
              "290782                  1.0   no  2017   1.000000              0   \n",
              "24314                   1.0   no  2017   1.500000              0   \n",
              "351951                  2.0   no  2021   0.666667              0   \n",
              "511492                  2.0   no  2021   0.750000              0   \n",
              "107340                  1.0   no  2017   0.333333              0   \n",
              "160660                  1.0   no  2017   1.000000              0   \n",
              "548698                  2.0   no  2021   2.500000              1   \n",
              "127935                  1.0   no  2017   0.600000              0   \n",
              "336490                  2.0   no  2021   2.000000              0   \n",
              "\n",
              "        porcentaje_iluminacion  id_year  \n",
              "289649                     100        1  \n",
              "290782                     100        1  \n",
              "24314                      100        1  \n",
              "351951                       0        2  \n",
              "511492                       0        2  \n",
              "107340                     100        1  \n",
              "160660                      50        1  \n",
              "548698                       0        2  \n",
              "127935                     100        1  \n",
              "336490                       0        2  "
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "VwwNyR_lzNAp",
        "outputId": "94e5eecd-b445-439f-dc58-67fbe1c68ee4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>asma</th>\n",
              "      <th>id_asma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>si</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
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            ],
            "text/plain": [
              "  asma  id_asma\n",
              "0   si        1\n",
              "1   no        2"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Dimensión asma\n",
        "\n",
        "df_asma_ = pd.DataFrame({'asma': ['si', 'no']})\n",
        "df_asma_['id_asma'] = range(1, len(df_asma_) + 1)\n",
        "df_asma_.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zrFJFI7LRrZx"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.merge(df_asma, df_asma_[['asma', 'id_asma']], on='asma', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 862
        },
        "id": "swhYfGKKRuau",
        "outputId": "98b8f374-2713-4260-d485-7d8782071614"
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      "outputs": [
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              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271871</th>\n",
              "      <td>266623.0</td>\n",
              "      <td>2666231</td>\n",
              "      <td>26662311</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379772</th>\n",
              "      <td>1040189.0</td>\n",
              "      <td>10401891</td>\n",
              "      <td>104018911</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528191</th>\n",
              "      <td>3002731.0</td>\n",
              "      <td>30027311</td>\n",
              "      <td>300273111</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200336</th>\n",
              "      <td>225199.0</td>\n",
              "      <td>2251991</td>\n",
              "      <td>22519914</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Teusaquillo</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194114</th>\n",
              "      <td>222077.0</td>\n",
              "      <td>2220771</td>\n",
              "      <td>22207711</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301384</th>\n",
              "      <td>290821.0</td>\n",
              "      <td>2908211</td>\n",
              "      <td>29082112</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433215</th>\n",
              "      <td>1109242.0</td>\n",
              "      <td>11092421</td>\n",
              "      <td>110924211</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232022</th>\n",
              "      <td>240201.0</td>\n",
              "      <td>2402011</td>\n",
              "      <td>24020111</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Santafé</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41505</th>\n",
              "      <td>127875.0</td>\n",
              "      <td>1278751</td>\n",
              "      <td>12787511</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250965</th>\n",
              "      <td>253418.0</td>\n",
              "      <td>2534181</td>\n",
              "      <td>25341813</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398787</th>\n",
              "      <td>1054162.0</td>\n",
              "      <td>10541621</td>\n",
              "      <td>105416214</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401583</th>\n",
              "      <td>1057903.0</td>\n",
              "      <td>10579031</td>\n",
              "      <td>105790313</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Santafé</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78046</th>\n",
              "      <td>143125.0</td>\n",
              "      <td>1431251</td>\n",
              "      <td>14312511</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Fontibón</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378228</th>\n",
              "      <td>1039204.0</td>\n",
              "      <td>10392041</td>\n",
              "      <td>103920412</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>457008</th>\n",
              "      <td>1145400.0</td>\n",
              "      <td>11454001</td>\n",
              "      <td>114540011</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150904</th>\n",
              "      <td>175194.0</td>\n",
              "      <td>1751941</td>\n",
              "      <td>17519413</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374644</th>\n",
              "      <td>1037660.0</td>\n",
              "      <td>10376601</td>\n",
              "      <td>103766012</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218751</th>\n",
              "      <td>233589.0</td>\n",
              "      <td>2335891</td>\n",
              "      <td>23358915</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
              "133883     169020.0   1690201    16902011                   4  \\\n",
              "588158     565362.0   5653621    56536213                   4   \n",
              "271871     266623.0   2666231    26662311                   2   \n",
              "379772    1040189.0  10401891   104018911                   3   \n",
              "528191    3002731.0  30027311   300273111                   3   \n",
              "200336     225199.0   2251991    22519914                   4   \n",
              "194114     222077.0   2220771    22207711                   3   \n",
              "301384     290821.0   2908211    29082112                   4   \n",
              "433215    1109242.0  11092421   110924211                   2   \n",
              "232022     240201.0   2402011    24020111                   2   \n",
              "41505      127875.0   1278751    12787511                   3   \n",
              "250965     253418.0   2534181    25341813                   4   \n",
              "398787    1054162.0  10541621   105416214                   4   \n",
              "401583    1057903.0  10579031   105790313                   4   \n",
              "78046      143125.0   1431251    14312511                   4   \n",
              "378228    1039204.0  10392041   103920412                   4   \n",
              "457008    1145400.0  11454001   114540011                   5   \n",
              "150904     175194.0   1751941    17519413                   4   \n",
              "374644    1037660.0  10376601   103766012                   4   \n",
              "218751     233589.0   2335891    23358915                   5   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos           localidad  iluminacion_sala   \n",
              "133883                  3          1.0      Ciudad Bolívar               1.0  \\\n",
              "588158                  4          1.0             Kennedy               2.0   \n",
              "271871                  3          1.0            Fontibón               1.0   \n",
              "379772                  4          2.0            Fontibón               2.0   \n",
              "528191                  5          2.0                Suba               2.0   \n",
              "200336                  6          3.0         Teusaquillo               1.0   \n",
              "194114                  4          2.0            Fontibón               1.0   \n",
              "301384                  6          2.0       Puente Aranda               1.0   \n",
              "433215                  4          1.0      Ciudad Bolívar               2.0   \n",
              "232022                  3          1.0             Santafé               1.0   \n",
              "41505                   3          1.0            Fontibón               1.0   \n",
              "250965                  2          1.0             Kennedy               1.0   \n",
              "398787                  4          2.0       Puente Aranda               2.0   \n",
              "401583                  5          1.0             Santafé               2.0   \n",
              "78046                   4          2.0            Fontibón               1.0   \n",
              "378228                  2          1.0             Kennedy               2.0   \n",
              "457008                  5          4.0                Suba               2.0   \n",
              "150904                  5          2.0             Kennedy               1.0   \n",
              "374644                  5          2.0             Kennedy               2.0   \n",
              "218751                  2          1.0  Rafael Uribe Uribe               2.0   \n",
              "\n",
              "        iluminacion_cuartos asma  year  ocupacion  cantidad_asma   \n",
              "133883                  1.0   no  2017   1.333333              0  \\\n",
              "588158                  2.0   no  2021   1.000000              0   \n",
              "271871                  1.0   no  2017   0.666667              0   \n",
              "379772                  2.0   no  2021   0.750000              0   \n",
              "528191                  2.0   no  2021   0.600000              0   \n",
              "200336                  1.0   no  2017   0.666667              0   \n",
              "194114                  1.0   no  2017   0.750000              0   \n",
              "301384                  1.0   no  2017   0.666667              0   \n",
              "433215                  2.0   no  2021   0.500000              0   \n",
              "232022                  1.0   no  2017   0.666667              0   \n",
              "41505                   1.0   no  2017   1.000000              0   \n",
              "250965                  1.0   no  2017   2.000000              0   \n",
              "398787                  2.0   no  2021   1.000000              0   \n",
              "401583                  2.0   no  2021   0.800000              0   \n",
              "78046                   1.0   no  2017   1.000000              0   \n",
              "378228                  2.0   no  2021   2.000000              0   \n",
              "457008                  2.0   no  2021   1.000000              0   \n",
              "150904                  1.0   no  2017   0.800000              0   \n",
              "374644                  2.0   no  2021   0.800000              0   \n",
              "218751                  2.0   no  2017   2.500000              0   \n",
              "\n",
              "        porcentaje_iluminacion  id_year  id_asma  \n",
              "133883                     100        1        2  \n",
              "588158                       0        2        2  \n",
              "271871                     100        1        2  \n",
              "379772                       0        2        2  \n",
              "528191                       0        2        2  \n",
              "200336                     100        1        2  \n",
              "194114                     100        1        2  \n",
              "301384                     100        1        2  \n",
              "433215                       0        2        2  \n",
              "232022                     100        1        2  \n",
              "41505                      100        1        2  \n",
              "250965                     100        1        2  \n",
              "398787                       0        2        2  \n",
              "401583                       0        2        2  \n",
              "78046                      100        1        2  \n",
              "378228                       0        2        2  \n",
              "457008                       0        2        2  \n",
              "150904                     100        1        2  \n",
              "374644                       0        2        2  \n",
              "218751                       0        1        2  "
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.sample(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "moO6pD7F71CT",
        "outputId": "e101e54f-e091-4b51-ccf9-bcd5771a4afd"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>localidad</th>\n",
              "      <th>id_localidad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Usaquén</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chapinero</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Santafé</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>San Cristó</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Usme</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tunjuelito</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bosa</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kennedy</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fontibón</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Engativá</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Suba</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     localidad  id_localidad\n",
              "0      Usaquén             1\n",
              "1    Chapinero             2\n",
              "2      Santafé             3\n",
              "3   San Cristó             4\n",
              "4         Usme             5\n",
              "5   Tunjuelito             6\n",
              "6         Bosa             7\n",
              "7      Kennedy             8\n",
              "8     Fontibón             9\n",
              "9     Engativá            10\n",
              "10        Suba            11"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "localidades = ['Usaquén', 'Chapinero', 'Santafé', 'San Cristó', 'Usme', 'Tunjuelito', 'Bosa', 'Kennedy', 'Fontibón', 'Engativá', 'Suba', 'Barrios Unidos', 'Teusaquillo', 'Los Mártires', 'Antonio Nariño', 'Puente Aranda', 'La Candelaria', 'Rafael Uribe Uribe', 'Ciudad Bolívar', 'Sumapaz', 'Otra localidad rural']\n",
        "\n",
        "df_localidad = pd.DataFrame({'localidad': localidades})\n",
        "\n",
        "df_localidad['id_localidad'] = range(1, len(df_localidad) + 1)\n",
        "df_localidad.head(11)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cU20V_odVBBm"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.merge(df_asma, df_localidad[['localidad', 'id_localidad']], on='localidad', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "RXVcb34MVCf3",
        "outputId": "372948b6-7c11-45cf-e07c-52c04c0f6fba"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
              "      <th>year</th>\n",
              "      <th>ocupacion</th>\n",
              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "      <th>id_year</th>\n",
              "      <th>id_asma</th>\n",
              "      <th>id_localidad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21313</th>\n",
              "      <td>109452.0</td>\n",
              "      <td>1094521</td>\n",
              "      <td>10945212</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
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              "      <td>100</td>\n",
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              "    <tr>\n",
              "      <th>346466</th>\n",
              "      <td>1016906.0</td>\n",
              "      <td>10169061</td>\n",
              "      <td>101690615</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184551</th>\n",
              "      <td>195095.0</td>\n",
              "      <td>1950951</td>\n",
              "      <td>19509515</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>496499</th>\n",
              "      <td>1165963.0</td>\n",
              "      <td>11659631</td>\n",
              "      <td>116596311</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rafael Uribe Uribe</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567473</th>\n",
              "      <td>338301.0</td>\n",
              "      <td>3383011</td>\n",
              "      <td>33830111</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233721</th>\n",
              "      <td>240998.0</td>\n",
              "      <td>2409981</td>\n",
              "      <td>24099811</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114928</th>\n",
              "      <td>161123.0</td>\n",
              "      <td>1611231</td>\n",
              "      <td>16112313</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.800000</td>\n",
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              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>218402</th>\n",
              "      <td>233477.0</td>\n",
              "      <td>2334771</td>\n",
              "      <td>23347713</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usme</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499625</th>\n",
              "      <td>1457132.0</td>\n",
              "      <td>14571321</td>\n",
              "      <td>145713213</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>386161</th>\n",
              "      <td>1043813.0</td>\n",
              "      <td>10438131</td>\n",
              "      <td>104381314</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373792</th>\n",
              "      <td>1037218.0</td>\n",
              "      <td>10372181</td>\n",
              "      <td>103721811</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>277891</th>\n",
              "      <td>274125.0</td>\n",
              "      <td>2741251</td>\n",
              "      <td>27412511</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>129758</th>\n",
              "      <td>167102.0</td>\n",
              "      <td>1671021</td>\n",
              "      <td>16710212</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>188469</th>\n",
              "      <td>196466.0</td>\n",
              "      <td>1964661</td>\n",
              "      <td>19646611</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Puente Aranda</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500316</th>\n",
              "      <td>1458334.0</td>\n",
              "      <td>14583341</td>\n",
              "      <td>145833412</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534466</th>\n",
              "      <td>3005694.0</td>\n",
              "      <td>30056941</td>\n",
              "      <td>300569412</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Barrios Unidos</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315392</th>\n",
              "      <td>305976.0</td>\n",
              "      <td>3059761</td>\n",
              "      <td>30597611</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Bosa</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252174</th>\n",
              "      <td>253999.0</td>\n",
              "      <td>2539991</td>\n",
              "      <td>25399914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95566</th>\n",
              "      <td>153955.0</td>\n",
              "      <td>1539551</td>\n",
              "      <td>15395511</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Kennedy</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405952</th>\n",
              "      <td>1060581.0</td>\n",
              "      <td>10605811</td>\n",
              "      <td>106058111</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id_vivienda  id_hogar  id_persona  num_personas_hogar   \n",
              "21313      109452.0   1094521    10945212                   2  \\\n",
              "346466    1016906.0  10169061   101690615                   5   \n",
              "184551     195095.0   1950951    19509515                   5   \n",
              "496499    1165963.0  11659631   116596311                   5   \n",
              "567473     338301.0   3383011    33830111                   3   \n",
              "233721     240998.0   2409981    24099811                   4   \n",
              "114928     161123.0   1611231    16112313                   4   \n",
              "218402     233477.0   2334771    23347713                   4   \n",
              "499625    1457132.0  14571321   145713213                   6   \n",
              "386161    1043813.0  10438131   104381314                   4   \n",
              "373792    1037218.0  10372181   103721811                   2   \n",
              "277891     274125.0   2741251    27412511                   1   \n",
              "129758     167102.0   1671021    16710212                   3   \n",
              "188469     196466.0   1964661    19646611                   2   \n",
              "500316    1458334.0  14583341   145833412                   3   \n",
              "534466    3005694.0  30056941   300569412                   2   \n",
              "315392     305976.0   3059761    30597611                   4   \n",
              "252174     253999.0   2539991    25399914                   4   \n",
              "95566      153955.0   1539551    15395511                   9   \n",
              "405952    1060581.0  10605811   106058111                   2   \n",
              "\n",
              "        num_cuartos_hogar  num_cuartos           localidad  iluminacion_sala   \n",
              "21313                   4          3.0             Kennedy               1.0  \\\n",
              "346466                  4          1.0  Rafael Uribe Uribe               2.0   \n",
              "184551                  3          3.0             Kennedy               1.0   \n",
              "496499                  4          1.0  Rafael Uribe Uribe               2.0   \n",
              "567473                  3          1.0      Ciudad Bolívar               2.0   \n",
              "233721                  2          1.0             Kennedy               1.0   \n",
              "114928                  5          1.0             Kennedy               1.0   \n",
              "218402                  3          1.0                Usme               1.0   \n",
              "499625                  6          2.0      Ciudad Bolívar               2.0   \n",
              "386161                  4          2.0             Kennedy               2.0   \n",
              "373792                  3          1.0             Kennedy               2.0   \n",
              "277891                  3          2.0                Suba               1.0   \n",
              "129758                  4          1.0             Kennedy               1.0   \n",
              "188469                  3          1.0       Puente Aranda               1.0   \n",
              "500316                  4          4.0                Suba               2.0   \n",
              "534466                  3          1.0      Barrios Unidos               2.0   \n",
              "315392                  2          1.0                Bosa               1.0   \n",
              "252174                  4          1.0            Engativá               1.0   \n",
              "95566                   7          2.0             Kennedy               1.0   \n",
              "405952                  4          1.0             Usaquén               2.0   \n",
              "\n",
              "        iluminacion_cuartos asma  year  ocupacion  cantidad_asma   \n",
              "21313                   1.0   no  2017   0.500000              0  \\\n",
              "346466                  2.0   no  2021   1.250000              0   \n",
              "184551                  1.0   no  2017   1.666667              0   \n",
              "496499                  2.0   no  2021   1.250000              0   \n",
              "567473                  2.0   no  2021   1.000000              2   \n",
              "233721                  1.0   no  2017   2.000000              0   \n",
              "114928                  1.0   no  2017   0.800000              0   \n",
              "218402                  1.0   no  2017   1.333333              0   \n",
              "499625                  2.0   no  2021   1.000000              0   \n",
              "386161                  2.0   no  2021   1.000000              0   \n",
              "373792                  2.0   no  2021   0.666667              0   \n",
              "277891                  1.0   no  2017   0.333333              0   \n",
              "129758                  1.0   no  2017   0.750000              0   \n",
              "188469                  1.0   no  2017   0.666667              0   \n",
              "500316                  2.0   no  2021   0.750000              0   \n",
              "534466                  2.0   no  2021   0.666667              0   \n",
              "315392                  1.0   no  2017   2.000000              0   \n",
              "252174                  1.0   no  2017   1.000000              0   \n",
              "95566                   1.0   no  2017   1.285714              0   \n",
              "405952                  2.0   no  2021   0.500000              0   \n",
              "\n",
              "        porcentaje_iluminacion  id_year  id_asma  id_localidad  \n",
              "21313                      100        1        2             8  \n",
              "346466                       0        2        2            18  \n",
              "184551                     100        1        2             8  \n",
              "496499                       0        2        2            18  \n",
              "567473                       0        2        2            19  \n",
              "233721                     100        1        2             8  \n",
              "114928                     100        1        2             8  \n",
              "218402                     100        1        2             5  \n",
              "499625                       0        2        2            19  \n",
              "386161                       0        2        2             8  \n",
              "373792                       0        2        2             8  \n",
              "277891                     100        1        2            11  \n",
              "129758                     100        1        2             8  \n",
              "188469                     100        1        2            16  \n",
              "500316                       0        2        2            11  \n",
              "534466                       0        2        2            12  \n",
              "315392                     100        1        2             7  \n",
              "252174                     100        1        2            10  \n",
              "95566                      100        1        2             8  \n",
              "405952                       0        2        2             1  "
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.sample(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ARNKLNtMzM7h",
        "outputId": "6b6415a3-a784-42ec-eefa-f0f09406f04d"
      },
      "outputs": [
        {
          "data": {
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              "      <th>id_vivienda</th>\n",
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            "text/plain": [
              "   id_vivienda  id_casa\n",
              "0     101033.0        1\n",
              "1     101041.0        2\n",
              "2     101042.0        3\n",
              "3     101047.0        4\n",
              "4     101059.0        5\n",
              "5     101063.0        6\n",
              "6     101089.0        7\n",
              "7     101142.0        8\n",
              "8     101158.0        9\n",
              "9     101189.0       10"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Obtener una lista de los IDs de las viviendas sin repeticiones\n",
        "ids_viviendas = df_asma['id_vivienda'].unique()\n",
        "\n",
        "# Crear un nuevo dataset solo con la columna de los IDs de las viviendas\n",
        "df_viviendas = pd.DataFrame({'id_vivienda': ids_viviendas})\n",
        "df_viviendas['id_casa'] = range(1, len(df_viviendas) + 1)\n",
        "df_viviendas.head(10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "njU8M2uKVmwC"
      },
      "outputs": [],
      "source": [
        "df_asma = pd.merge(df_asma, df_viviendas[['id_vivienda', 'id_casa']], on='id_vivienda', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "id": "1t6-ZCiUVoF8",
        "outputId": "ecd760bd-39ef-493e-cbfd-7168aafa3269"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>id_vivienda</th>\n",
              "      <th>id_hogar</th>\n",
              "      <th>id_persona</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>num_cuartos</th>\n",
              "      <th>localidad</th>\n",
              "      <th>iluminacion_sala</th>\n",
              "      <th>iluminacion_cuartos</th>\n",
              "      <th>asma</th>\n",
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              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "      <th>id_year</th>\n",
              "      <th>id_asma</th>\n",
              "      <th>id_localidad</th>\n",
              "      <th>id_casa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101033.0</td>\n",
              "      <td>1010331</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101041.0</td>\n",
              "      <td>1010411</td>\n",
              "      <td>10104112</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>101042.0</td>\n",
              "      <td>1010421</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
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              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101047.0</td>\n",
              "      <td>1010471</td>\n",
              "      <td>10104713</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usaquén</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101059.0</td>\n",
              "      <td>1010591</td>\n",
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              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
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              "      <th>5</th>\n",
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              "      <td>1010591</td>\n",
              "      <td>10105915</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>101063.0</td>\n",
              "      <td>1010631</td>\n",
              "      <td>10106311</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108911</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>101089.0</td>\n",
              "      <td>1010891</td>\n",
              "      <td>10108914</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>101142.0</td>\n",
              "      <td>1011422</td>\n",
              "      <td>10114221</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Teusaquillo</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101158.0</td>\n",
              "      <td>1011582</td>\n",
              "      <td>10115822</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Engativá</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>101189.0</td>\n",
              "      <td>1011891</td>\n",
              "      <td>10118916</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>San Cristó</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>101200.0</td>\n",
              "      <td>1012001</td>\n",
              "      <td>10120013</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usme</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>101208.0</td>\n",
              "      <td>1012081</td>\n",
              "      <td>10120812</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Usme</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>101238.0</td>\n",
              "      <td>1012381</td>\n",
              "      <td>10123812</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Usme</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>101270.0</td>\n",
              "      <td>1012701</td>\n",
              "      <td>10127011</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Tunjuelito</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>101281.0</td>\n",
              "      <td>1012811</td>\n",
              "      <td>10128111</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Tunjuelito</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>101291.0</td>\n",
              "      <td>1012911</td>\n",
              "      <td>10129118</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>101296.0</td>\n",
              "      <td>1012961</td>\n",
              "      <td>10129614</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Ciudad Bolívar</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>101397.0</td>\n",
              "      <td>1013971</td>\n",
              "      <td>10139713</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Suba</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>si</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id_vivienda  id_hogar  id_persona  num_personas_hogar  num_cuartos_hogar   \n",
              "0      101033.0   1010331    10103311                   1                  4  \\\n",
              "1      101041.0   1010411    10104112                   3                  1   \n",
              "2      101042.0   1010421    10104213                   5                  4   \n",
              "3      101047.0   1010471    10104713                   3                  3   \n",
              "4      101059.0   1010591    10105914                   5                  4   \n",
              "5      101059.0   1010591    10105915                   5                  4   \n",
              "6      101063.0   1010631    10106311                   3                  5   \n",
              "7      101089.0   1010891    10108911                   4                  4   \n",
              "8      101089.0   1010891    10108914                   4                  4   \n",
              "9      101142.0   1011422    10114221                   3                  2   \n",
              "10     101158.0   1011582    10115822                   2                  1   \n",
              "11     101189.0   1011891    10118916                   6                  4   \n",
              "12     101200.0   1012001    10120013                   3                  3   \n",
              "13     101208.0   1012081    10120812                   3                  3   \n",
              "14     101238.0   1012381    10123812                   2                  3   \n",
              "15     101270.0   1012701    10127011                   2                  4   \n",
              "16     101281.0   1012811    10128111                   4                  2   \n",
              "17     101291.0   1012911    10129118                   8                  3   \n",
              "18     101296.0   1012961    10129614                  10                  2   \n",
              "19     101397.0   1013971    10139713                   6                  3   \n",
              "\n",
              "    num_cuartos       localidad  iluminacion_sala  iluminacion_cuartos asma   \n",
              "0           2.0            Suba               1.0                  1.0   si  \\\n",
              "1           1.0         Usaquén               2.0                  2.0   si   \n",
              "2           1.0         Usaquén               1.0                  1.0   si   \n",
              "3           1.0         Usaquén               2.0                  1.0   si   \n",
              "4           2.0            Suba               1.0                  1.0   si   \n",
              "5           2.0            Suba               1.0                  1.0   si   \n",
              "6           1.0            Suba               1.0                  1.0   si   \n",
              "7           2.0            Suba               1.0                  1.0   si   \n",
              "8           2.0            Suba               1.0                  1.0   si   \n",
              "9           1.0     Teusaquillo               1.0                  1.0   si   \n",
              "10          1.0        Engativá               2.0                  2.0   si   \n",
              "11          2.0      San Cristó               1.0                  1.0   si   \n",
              "12          1.0            Usme               1.0                  2.0   si   \n",
              "13          2.0            Usme               1.0                  1.0   si   \n",
              "14          1.0            Usme               1.0                  1.0   si   \n",
              "15          1.0      Tunjuelito               1.0                  1.0   si   \n",
              "16          1.0      Tunjuelito               2.0                  1.0   si   \n",
              "17          1.0  Ciudad Bolívar               1.0                  1.0   si   \n",
              "18          1.0  Ciudad Bolívar               1.0                  1.0   si   \n",
              "19          1.0            Suba               2.0                  2.0   si   \n",
              "\n",
              "    year  ocupacion  cantidad_asma  porcentaje_iluminacion  id_year  id_asma   \n",
              "0   2017   0.250000              1                     100        1        1  \\\n",
              "1   2017   3.000000              1                       0        1        1   \n",
              "2   2017   1.250000              1                     100        1        1   \n",
              "3   2017   1.000000              1                      50        1        1   \n",
              "4   2017   1.250000              2                     100        1        1   \n",
              "5   2017   1.250000              2                     100        1        1   \n",
              "6   2017   0.600000              1                     100        1        1   \n",
              "7   2017   1.000000              2                     100        1        1   \n",
              "8   2017   1.000000              2                     100        1        1   \n",
              "9   2017   1.500000              1                     100        1        1   \n",
              "10  2017   2.000000              1                       0        1        1   \n",
              "11  2017   1.500000              1                     100        1        1   \n",
              "12  2017   1.000000              1                      50        1        1   \n",
              "13  2017   1.000000              1                     100        1        1   \n",
              "14  2017   0.666667              1                     100        1        1   \n",
              "15  2017   0.500000              1                     100        1        1   \n",
              "16  2017   2.000000              1                      50        1        1   \n",
              "17  2017   2.666667              1                     100        1        1   \n",
              "18  2017   5.000000              1                     100        1        1   \n",
              "19  2017   2.000000              1                       0        1        1   \n",
              "\n",
              "    id_localidad  id_casa  \n",
              "0             11        1  \n",
              "1              1        2  \n",
              "2              1        3  \n",
              "3              1        4  \n",
              "4             11        5  \n",
              "5             11        5  \n",
              "6             11        6  \n",
              "7             11        7  \n",
              "8             11        7  \n",
              "9             13        8  \n",
              "10            10        9  \n",
              "11             4       10  \n",
              "12             5       11  \n",
              "13             5       12  \n",
              "14             5       13  \n",
              "15             6       14  \n",
              "16             6       15  \n",
              "17            19       16  \n",
              "18            19       17  \n",
              "19            11       18  "
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2cTNji9X3Nl",
        "outputId": "03680a10-aac1-49d5-fa3d-21dc672df0a8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['id_vivienda', 'id_hogar', 'id_persona', 'num_personas_hogar',\n",
              "       'num_cuartos_hogar', 'num_cuartos', 'localidad', 'iluminacion_sala',\n",
              "       'iluminacion_cuartos', 'asma', 'year', 'ocupacion', 'cantidad_asma',\n",
              "       'porcentaje_iluminacion', 'id_year', 'id_asma', 'id_localidad',\n",
              "       'id_casa'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2VSevWqSX0Yg"
      },
      "outputs": [],
      "source": [
        "lista_columns_delete = ['id_vivienda', 'id_hogar', 'id_persona',\n",
        "                        'num_cuartos', 'localidad', 'iluminacion_sala',\n",
        "                        'iluminacion_cuartos', 'asma','year']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "N83ycbBHXaFk",
        "outputId": "fe29e6b2-c8c3-4d6e-dac8-4783b47426fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id_vivienda\n",
            "id_hogar\n",
            "id_persona\n",
            "num_cuartos\n",
            "localidad\n",
            "iluminacion_sala\n",
            "iluminacion_cuartos\n",
            "asma\n",
            "year\n"
          ]
        }
      ],
      "source": [
        "#Eliminamos la columna num_cuartos_ocupados\n",
        "for value in lista_columns_delete:\n",
        "  df_asma = df_asma.drop(value, axis=1)\n",
        "  print(value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "xzNxNpv_Y1B5",
        "outputId": "a7255d70-f4d2-4621-dc89-70feea748a59"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>ocupacion</th>\n",
              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "      <th>id_year</th>\n",
              "      <th>id_asma</th>\n",
              "      <th>id_localidad</th>\n",
              "      <th>id_casa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    num_personas_hogar  num_cuartos_hogar  ocupacion  cantidad_asma   \n",
              "0                    1                  4   0.250000              1  \\\n",
              "1                    3                  1   3.000000              1   \n",
              "2                    5                  4   1.250000              1   \n",
              "3                    3                  3   1.000000              1   \n",
              "4                    5                  4   1.250000              2   \n",
              "5                    5                  4   1.250000              2   \n",
              "6                    3                  5   0.600000              1   \n",
              "7                    4                  4   1.000000              2   \n",
              "8                    4                  4   1.000000              2   \n",
              "9                    3                  2   1.500000              1   \n",
              "10                   2                  1   2.000000              1   \n",
              "11                   6                  4   1.500000              1   \n",
              "12                   3                  3   1.000000              1   \n",
              "13                   3                  3   1.000000              1   \n",
              "14                   2                  3   0.666667              1   \n",
              "15                   2                  4   0.500000              1   \n",
              "16                   4                  2   2.000000              1   \n",
              "17                   8                  3   2.666667              1   \n",
              "18                  10                  2   5.000000              1   \n",
              "19                   6                  3   2.000000              1   \n",
              "\n",
              "    porcentaje_iluminacion  id_year  id_asma  id_localidad  id_casa  \n",
              "0                      100        1        1            11        1  \n",
              "1                        0        1        1             1        2  \n",
              "2                      100        1        1             1        3  \n",
              "3                       50        1        1             1        4  \n",
              "4                      100        1        1            11        5  \n",
              "5                      100        1        1            11        5  \n",
              "6                      100        1        1            11        6  \n",
              "7                      100        1        1            11        7  \n",
              "8                      100        1        1            11        7  \n",
              "9                      100        1        1            13        8  \n",
              "10                       0        1        1            10        9  \n",
              "11                     100        1        1             4       10  \n",
              "12                      50        1        1             5       11  \n",
              "13                     100        1        1             5       12  \n",
              "14                     100        1        1             5       13  \n",
              "15                     100        1        1             6       14  \n",
              "16                      50        1        1             6       15  \n",
              "17                     100        1        1            19       16  \n",
              "18                     100        1        1            19       17  \n",
              "19                       0        1        1            11       18  "
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVOfBhJFekkI",
        "outputId": "75fa38e4-e625-4d15-c9db-093fc0e25420"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.010101010101010102,\n",
              " 11.0,\n",
              " 1.0911968028901085,\n",
              " 0    1.0\n",
              " Name: ocupacion, dtype: float64)"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma['ocupacion'].min(), df_asma['ocupacion'].max(), df_asma['ocupacion'].mean(),  df_asma['ocupacion'].mode(),"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Maaqyeoxm8-0"
      },
      "outputs": [],
      "source": [
        "df_asma.to_csv('./data/datos_tratados/hacinamiento_fact.csv', index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fm8biScYzMpv"
      },
      "outputs": [],
      "source": [
        "df_year.to_csv('./data/datos_tratados/year_dim.csv', index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0XsLfHSfzMlf"
      },
      "outputs": [],
      "source": [
        "df_asma_.to_csv('./data/datos_tratados/asma_dim.csv', index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9pj9UaGWzMgd"
      },
      "outputs": [],
      "source": [
        "df_localidad.to_csv('./data/datos_tratados/localidad_dim.csv', index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3-QCq6rpVyCi"
      },
      "outputs": [],
      "source": [
        "df_viviendas.to_csv('./data/datos_tratados/vivienda_dim.csv', index=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Correción tabla de hechos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_asma = pd.read_csv('./data/datos_tratados/hacinamiento_fact.csv', encoding=\"utf-8\", sep = ',')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(612175, 10)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>num_personas_hogar</th>\n",
              "      <th>num_cuartos_hogar</th>\n",
              "      <th>ocupacion</th>\n",
              "      <th>cantidad_asma</th>\n",
              "      <th>porcentaje_iluminacion</th>\n",
              "      <th>id_year</th>\n",
              "      <th>id_asma</th>\n",
              "      <th>id_localidad</th>\n",
              "      <th>id_casa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "      <td>1.25</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1.25</td>\n",
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              "      <td>100</td>\n",
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              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  num_personas_hogar  num_cuartos_hogar  ocupacion   \n",
              "0           0                   1                  4       0.25  \\\n",
              "1           1                   3                  1       3.00   \n",
              "2           2                   5                  4       1.25   \n",
              "3           3                   3                  3       1.00   \n",
              "4           4                   5                  4       1.25   \n",
              "5           5                   5                  4       1.25   \n",
              "6           6                   3                  5       0.60   \n",
              "7           7                   4                  4       1.00   \n",
              "8           8                   4                  4       1.00   \n",
              "9           9                   3                  2       1.50   \n",
              "\n",
              "   cantidad_asma  porcentaje_iluminacion  id_year  id_asma  id_localidad   \n",
              "0              1                     100        1        1            11  \\\n",
              "1              1                       0        1        1             1   \n",
              "2              1                     100        1        1             1   \n",
              "3              1                      50        1        1             1   \n",
              "4              2                     100        1        1            11   \n",
              "5              2                     100        1        1            11   \n",
              "6              1                     100        1        1            11   \n",
              "7              2                     100        1        1            11   \n",
              "8              2                     100        1        1            11   \n",
              "9              1                     100        1        1            13   \n",
              "\n",
              "   id_casa  \n",
              "0        1  \n",
              "1        2  \n",
              "2        3  \n",
              "3        4  \n",
              "4        5  \n",
              "5        5  \n",
              "6        6  \n",
              "7        7  \n",
              "8        7  \n",
              "9        8  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(10) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_asma = df_asma.drop_duplicates(subset=['id_casa'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(213659, 10)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>id_localidad</th>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
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              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1</td>\n",
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              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
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              "      <td>10</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Unnamed: 0  num_personas_hogar  num_cuartos_hogar  ocupacion   \n",
              "0            0                   1                  4       0.25  \\\n",
              "1            1                   3                  1       3.00   \n",
              "2            2                   5                  4       1.25   \n",
              "3            3                   3                  3       1.00   \n",
              "4            4                   5                  4       1.25   \n",
              "6            6                   3                  5       0.60   \n",
              "7            7                   4                  4       1.00   \n",
              "9            9                   3                  2       1.50   \n",
              "10          10                   2                  1       2.00   \n",
              "11          11                   6                  4       1.50   \n",
              "\n",
              "    cantidad_asma  porcentaje_iluminacion  id_year  id_asma  id_localidad   \n",
              "0               1                     100        1        1            11  \\\n",
              "1               1                       0        1        1             1   \n",
              "2               1                     100        1        1             1   \n",
              "3               1                      50        1        1             1   \n",
              "4               2                     100        1        1            11   \n",
              "6               1                     100        1        1            11   \n",
              "7               2                     100        1        1            11   \n",
              "9               1                     100        1        1            13   \n",
              "10              1                       0        1        1            10   \n",
              "11              1                     100        1        1             4   \n",
              "\n",
              "    id_casa  \n",
              "0         1  \n",
              "1         2  \n",
              "2         3  \n",
              "3         4  \n",
              "4         5  \n",
              "6         6  \n",
              "7         7  \n",
              "9         8  \n",
              "10        9  \n",
              "11       10  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_asma.head(10) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_asma.to_csv('./data/datos_tratados/hacinamiento_fact_actualizada.csv', index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "vK59upJ6CV4T",
        "j19WPa1HF0vI",
        "-4BRuiMga2JM"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "env",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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