Copy_of_Pandas_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fdopa/curso-ciencia-datos/blob/main/Copy_of_Pandas_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas\n",
        "!pip install gdown"
      ],
      "metadata": {
        "id": "YzFyzFdvmWfH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95c33e54-2909-45fa-fc63-0535f335feee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.12.2)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.27.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.65.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.4.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "SfCdLIqoQADh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Descargar datos limpios\n",
        "Inventario de vivienda\n",
        "Contiene la información que da cuenta de las viviendas y el entorno urbano en el que se están desarrollando.\n",
        "\n",
        "Año:\n",
        "2017 al 2023\n",
        "\n",
        "- [Link](https://sniiv.sedatu.gob.mx/Reporte/Datos_abiertos)\n",
        "- [Diccionario](https://sniiv.sedatu.gob.mx/Reporte/Datos_abiertos#:~:text=Diccionario%20de%20datos)\n"
      ],
      "metadata": {
        "id": "cHug6QapQXmI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/javierIA/infonavit.git"
      ],
      "metadata": {
        "id": "ojG3DPGgSQM4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a822fa7-1665-4497-e7a9-d62a42ba9660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'infonavit' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title UnirVarios DTF CON EL mismo formato\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "# Create an empty list to store the dataframes\n",
        "dfs = []\n",
        "\n",
        "# Iterate over each CSV file in the \"Datos\" directory\n",
        "for file_name in os.listdir(\"infonavit\"):\n",
        "    if file_name.endswith(\".csv\"):\n",
        "        file_path = os.path.join(\"infonavit\", file_name)\n",
        "        df = pd.read_csv(file_path, encoding=\"latin1\")#latin se usa para ñ , acentos\n",
        "        dfs.append(df)\n",
        "print(dfs)\n",
        "merged_df = pd.concat(dfs, ignore_index=True)\n",
        "\n",
        "# Convert merged dataframe to Parquet format\n",
        "output_file = \"merged_data.parquet\"\n",
        "merged_df.to_parquet(output_file, engine=\"pyarrow\")\n",
        "\n",
        "print(f\"Data converted to Parquet format and saved as {output_file}\")\n"
      ],
      "metadata": {
        "id": "XKhJULRTmS2O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c471ef59-606e-42d2-cecc-767f8a67b347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[         año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
            "0       2019    1      1.0  Aguascalientes      NaN  No distribuido   \n",
            "1       2019    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "2       2019    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "3       2019    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "4       2019    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "...      ...  ...      ...             ...      ...             ...   \n",
            "383368  2019   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "383369  2019   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "383370  2019   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "383371  2019   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "383372  2019   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               4          2      1.0     1   NaN         NaN             NaN   \n",
            "1               1          1      2.0     2   1.0         NaN             1.0   \n",
            "2               1          1      2.0     2   1.0         NaN             2.0   \n",
            "3               1          1      2.0     2   1.0         NaN             3.0   \n",
            "4               1          1      2.0     2   1.0         NaN             4.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "383368          4          1      2.0     1   NaN         NaN             5.0   \n",
            "383369          4          1      2.0     1   NaN         NaN             6.0   \n",
            "383370          4          1      2.0     1   NaN         NaN             6.0   \n",
            "383371          1          2      1.0     2   1.0         2.0             4.0   \n",
            "383372          2          2      1.0     2   1.0         2.0             3.0   \n",
            "\n",
            "        vivienda_valor  acciones    monto  \n",
            "0                  NaN       0.0  96860.0  \n",
            "1                  4.0       4.0      0.0  \n",
            "2                  3.0       1.0      0.0  \n",
            "3                  4.0       1.0      0.0  \n",
            "4                  4.0       1.0      0.0  \n",
            "...                ...       ...      ...  \n",
            "383368             4.0       1.0      0.0  \n",
            "383369             4.0       3.0      0.0  \n",
            "383370             5.0       3.0      0.0  \n",
            "383371             NaN       1.0      0.0  \n",
            "383372             1.0       1.0  57534.0  \n",
            "\n",
            "[383373 rows x 16 columns],          año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
            "0       2020    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "1       2020    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "2       2020    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "3       2020    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "4       2020    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "...      ...  ...      ...             ...      ...             ...   \n",
            "357613  2020   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "357614  2020   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "357615  2020   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "357616  2020   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "357617  2020   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               1          1      2.0     2   1.0         NaN             2.0   \n",
            "1               1          1      2.0     2   1.0         NaN             2.0   \n",
            "2               1          1      2.0     2   1.0         NaN             6.0   \n",
            "3               1          1      2.0     2   1.0         NaN             6.0   \n",
            "4               1          1      2.0     2   1.0         1.0             1.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "357613          1          2      1.0     2   1.0         2.0             1.0   \n",
            "357614          1          2      1.0     2   2.0         2.0             5.0   \n",
            "357615          2          2      1.0     2   1.0         1.0             3.0   \n",
            "357616          2          2      1.0     2   1.0         2.0             1.0   \n",
            "357617          2          2      1.0     2   2.0         2.0             4.0   \n",
            "\n",
            "        vivienda_valor  acciones      monto  \n",
            "0                  3.0       1.0       0.00  \n",
            "1                  5.0       1.0       0.00  \n",
            "2                  4.0       1.0       0.00  \n",
            "3                  5.0       1.0       0.00  \n",
            "4                  3.0       2.0  678391.84  \n",
            "...                ...       ...        ...  \n",
            "357613             NaN       1.0       0.00  \n",
            "357614             NaN       1.0       0.00  \n",
            "357615             1.0       1.0   43995.00  \n",
            "357616             1.0       1.0   17134.00  \n",
            "357617             1.0       1.0  110664.00  \n",
            "\n",
            "[357618 rows x 16 columns],          año  mes  cve_ent         entidad  cve_mun              minicipio  \\\n",
            "0       2017    1      NaN  No distribuido      NaN         No distribuido   \n",
            "1       2017    1      NaN  No distribuido      NaN         No distribuido   \n",
            "2       2017    1      NaN  No distribuido      NaN         No distribuido   \n",
            "3       2017    1      NaN  No distribuido      NaN         No distribuido   \n",
            "4       2017    1      1.0  Aguascalientes      1.0         Aguascalientes   \n",
            "...      ...  ...      ...             ...      ...                    ...   \n",
            "348808  2017   12     32.0       Zacatecas     58.0  Santa María de la Paz   \n",
            "348809  2017   12     32.0       Zacatecas     58.0  Santa María de la Paz   \n",
            "348810  2017   12     32.0       Zacatecas     58.0  Santa María de la Paz   \n",
            "348811  2017   12     32.0       Zacatecas     58.0  Santa María de la Paz   \n",
            "348812  2017   12     32.0       Zacatecas     58.0  Santa María de la Paz   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               4          2      1.0     1   NaN         NaN             NaN   \n",
            "1               4          4      NaN     1   NaN         NaN             NaN   \n",
            "2              10          1      2.0     2   1.0         2.0             2.0   \n",
            "3              10          1      2.0     2   2.0         2.0             1.0   \n",
            "4               1          1      2.0     2   1.0         1.0             5.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "348808          5          1     10.0     2   1.0         2.0             2.0   \n",
            "348809          5          1     10.0     2   1.0         2.0             3.0   \n",
            "348810          5          1     10.0     2   2.0         1.0             3.0   \n",
            "348811          5          1     10.0     2   2.0         2.0             3.0   \n",
            "348812          5          1     10.0     2   2.0         3.0             3.0   \n",
            "\n",
            "        vivienda_valor  acciones        monto  \n",
            "0                  NaN         0  49873528.00  \n",
            "1                  NaN         0  38207676.00  \n",
            "2                  3.0         1    672750.00  \n",
            "3                  3.0         1    536250.00  \n",
            "4                  3.0         2    548045.00  \n",
            "...                ...       ...          ...  \n",
            "348808             1.0         1     66551.98  \n",
            "348809             1.0         6    399311.86  \n",
            "348810             1.0         4    266207.91  \n",
            "348811             1.0         2    133103.95  \n",
            "348812             1.0         2    133103.95  \n",
            "\n",
            "[348813 rows x 16 columns],          año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
            "0       2018    1      NaN  No distribuido      NaN  No distribuido   \n",
            "1       2018    1      NaN  No distribuido      NaN  No distribuido   \n",
            "2       2018    1      NaN  No distribuido      NaN  No distribuido   \n",
            "3       2018    1      NaN  No distribuido      NaN  No distribuido   \n",
            "4       2018    1      NaN  No distribuido      NaN  No distribuido   \n",
            "...      ...  ...      ...             ...      ...             ...   \n",
            "416779  2018   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "416780  2018   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "416781  2018   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "416782  2018   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "416783  2018   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               4          1     10.0     1   NaN         NaN             NaN   \n",
            "1               4          2      1.0     1   NaN         NaN             NaN   \n",
            "2              10          1      2.0     2   1.0         2.0             1.0   \n",
            "3              10          1      2.0     2   1.0         2.0             2.0   \n",
            "4              10          1      2.0     2   1.0         2.0             2.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "416779          1          2      1.0     2   1.0         1.0             2.0   \n",
            "416780          1          2      1.0     2   1.0         1.0             4.0   \n",
            "416781          1          2      1.0     2   1.0         2.0             5.0   \n",
            "416782          2          2      1.0     2   1.0         1.0             2.0   \n",
            "416783          2          2      1.0     2   1.0         1.0             4.0   \n",
            "\n",
            "        vivienda_valor  acciones        monto  \n",
            "0                  NaN       0.0  13732400.00  \n",
            "1                  NaN       0.0  91871861.99  \n",
            "2                  3.0       1.0    536000.00  \n",
            "3                  3.0       6.0   4354000.00  \n",
            "4                  4.0       7.0   6006000.00  \n",
            "...                ...       ...          ...  \n",
            "416779             NaN       1.0         0.00  \n",
            "416780             NaN       1.0         0.00  \n",
            "416781             NaN       1.0         0.00  \n",
            "416782             1.0       1.0     12453.00  \n",
            "416783             1.0       1.0     26876.00  \n",
            "\n",
            "[416784 rows x 16 columns],          año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
            "0       2021    1      NaN  No distribuido      NaN  No distribuido   \n",
            "1       2021    1      1.0  Aguascalientes      NaN  No distribuido   \n",
            "2       2021    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "3       2021    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "4       2021    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "...      ...  ...      ...             ...      ...             ...   \n",
            "393128  2021   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "393129  2021   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "393130  2021   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "393131  2021   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "393132  2021   12     32.0       Zacatecas     57.0        Trancoso   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               4          2      1.0     1   NaN         NaN             NaN   \n",
            "1               4          2      1.0     1   NaN         NaN             NaN   \n",
            "2               1          1      2.0     2   1.0         NaN             2.0   \n",
            "3               1          1      2.0     2   1.0         NaN             2.0   \n",
            "4               1          1      2.0     2   1.0         NaN             6.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "393128          1          2      1.0     2   1.0         2.0             1.0   \n",
            "393129          1          2      1.0     2   1.0         2.0             4.0   \n",
            "393130          2          2      1.0     2   1.0         2.0             1.0   \n",
            "393131          2          2      1.0     2   1.0         2.0             3.0   \n",
            "393132          4          2      1.0     1   1.0         2.0             NaN   \n",
            "\n",
            "        vivienda_valor  acciones     monto  \n",
            "0                  NaN       0.0       0.0  \n",
            "1                  NaN       0.0  287860.0  \n",
            "2                  4.0       2.0       0.0  \n",
            "3                  5.0       3.0       0.0  \n",
            "4                  4.0       2.0       0.0  \n",
            "...                ...       ...       ...  \n",
            "393128             NaN       2.0       0.0  \n",
            "393129             NaN       1.0       0.0  \n",
            "393130             1.0       2.0   53126.0  \n",
            "393131             1.0       1.0   80000.0  \n",
            "393132             NaN       1.0       0.0  \n",
            "\n",
            "[393133 rows x 16 columns],          año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
            "0       2022    1      NaN  No distribuido      NaN  No distribuido   \n",
            "1       2022    1      1.0  Aguascalientes      NaN  No distribuido   \n",
            "2       2022    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "3       2022    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "4       2022    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
            "...      ...  ...      ...             ...      ...             ...   \n",
            "329843  2022   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "329844  2022   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "329845  2022   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "329846  2022   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "329847  2022   12     32.0       Zacatecas     56.0       Zacatecas   \n",
            "\n",
            "        organismo  modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0               4          2      1.0     1   NaN         NaN             NaN   \n",
            "1               4          2      1.0     1   NaN         NaN             NaN   \n",
            "2               1          1      2.0     2   1.0         NaN             2.0   \n",
            "3               1          1      2.0     2   1.0         1.0             4.0   \n",
            "4               1          1      2.0     2   1.0         1.0             5.0   \n",
            "...           ...        ...      ...   ...   ...         ...             ...   \n",
            "329843          3          3      3.0     1   1.0         2.0             5.0   \n",
            "329844          3          3      3.0     1   2.0         2.0             2.0   \n",
            "329845          3          4      4.0     1   1.0         2.0             6.0   \n",
            "329846          3          4      4.0     1   2.0         2.0             2.0   \n",
            "329847          4          2      1.0     1   2.0         2.0             NaN   \n",
            "\n",
            "        vivienda_valor  acciones       monto  \n",
            "0                  NaN       0.0        0.00  \n",
            "1                  NaN       0.0  1307216.96  \n",
            "2                  4.0       1.0        0.00  \n",
            "3                  3.0       1.0   245295.86  \n",
            "4                  3.0       1.0   379292.85  \n",
            "...                ...       ...         ...  \n",
            "329843             4.0       1.0  1300000.00  \n",
            "329844             3.0       2.0  1286846.81  \n",
            "329845             5.0       1.0  1200260.91  \n",
            "329846             4.0       1.0   444083.80  \n",
            "329847             NaN       1.0        0.00  \n",
            "\n",
            "[329848 rows x 16 columns],        aÃ±o  mes  cve_ent         entidad  cve_mun       minicipio  organismo  \\\n",
            "0      2023    1      NaN  No distribuido      NaN  No distribuido          4   \n",
            "1      2023    1      1.0  Aguascalientes      NaN  No distribuido          4   \n",
            "2      2023    1      1.0  Aguascalientes      1.0  Aguascalientes          1   \n",
            "3      2023    1      1.0  Aguascalientes      1.0  Aguascalientes          1   \n",
            "4      2023    1      1.0  Aguascalientes      1.0  Aguascalientes          1   \n",
            "...     ...  ...      ...             ...      ...             ...        ...   \n",
            "85722  2023    4     32.0       Zacatecas     56.0       Zacatecas          3   \n",
            "85723  2023    4     32.0       Zacatecas     57.0        Trancoso          1   \n",
            "85724  2023    4     32.0       Zacatecas     57.0        Trancoso          1   \n",
            "85725  2023    4     32.0       Zacatecas     57.0        Trancoso          1   \n",
            "85726  2023    4     32.0       Zacatecas     57.0        Trancoso          2   \n",
            "\n",
            "       modalidad  destino  tipo  sexo  edad_rango  ingresos_rango  \\\n",
            "0              2      1.0     1   NaN         NaN             NaN   \n",
            "1              2      1.0     1   NaN         NaN             NaN   \n",
            "2              1      2.0     2   1.0         NaN             1.0   \n",
            "3              1      2.0     2   1.0         NaN             1.0   \n",
            "4              1      2.0     2   1.0         NaN             1.0   \n",
            "...          ...      ...   ...   ...         ...             ...   \n",
            "85722          3      3.0     1   1.0         2.0             5.0   \n",
            "85723          2      1.0     2   1.0         2.0             1.0   \n",
            "85724          2      1.0     2   2.0         2.0             2.0   \n",
            "85725          2      1.0     2   2.0         2.0             3.0   \n",
            "85726          2      1.0     1   2.0         2.0             1.0   \n",
            "\n",
            "       vivienda_valor  acciones       monto  \n",
            "0                 NaN         0        0.00  \n",
            "1                 NaN         0  1158580.13  \n",
            "2                 2.0         1        0.00  \n",
            "3                 3.0         4        0.00  \n",
            "4                 4.0         1        0.00  \n",
            "...               ...       ...         ...  \n",
            "85722             4.0         2  1900000.00  \n",
            "85723             NaN         1        0.00  \n",
            "85724             NaN         1        0.00  \n",
            "85725             NaN         1        0.00  \n",
            "85726             1.0         2    86511.00  \n",
            "\n",
            "[85727 rows x 16 columns]]\n",
            "Data converted to Parquet format and saved as merged_data.parquet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db=pd.read_parquet(\"/content/merged_data.parquet\")\n",
        "\n",
        "db\n"
      ],
      "metadata": {
        "id": "b6DAcjXKrTEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "7da9adf4-2260-47dd-9171-4417c0983223"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
              "0        2019.0    1      1.0  Aguascalientes      NaN  No distribuido   \n",
              "1        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "2        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "3        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "4        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "...         ...  ...      ...             ...      ...             ...   \n",
              "2315291     NaN    4     32.0       Zacatecas     56.0            None   \n",
              "2315292     NaN    4     32.0       Zacatecas     57.0            None   \n",
              "2315293     NaN    4     32.0       Zacatecas     57.0            None   \n",
              "2315294     NaN    4     32.0       Zacatecas     57.0            None   \n",
              "2315295     NaN    4     32.0       Zacatecas     57.0            None   \n",
              "\n",
              "         organismo  modalidad  destino  tipo  sexo  edad_rango  \\\n",
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              "2                1          1      2.0     2   1.0         NaN   \n",
              "3                1          1      2.0     2   1.0         NaN   \n",
              "4                1          1      2.0     2   1.0         NaN   \n",
              "...            ...        ...      ...   ...   ...         ...   \n",
              "2315291          3          3      3.0     1   1.0         2.0   \n",
              "2315292          1          2      1.0     2   1.0         2.0   \n",
              "2315293          1          2      1.0     2   2.0         2.0   \n",
              "2315294          1          2      1.0     2   2.0         2.0   \n",
              "2315295          2          2      1.0     1   2.0         2.0   \n",
              "\n",
              "         ingresos_rango  vivienda_valor  acciones      monto  minicipio  \\\n",
              "0                   NaN             NaN       0.0    96860.0       None   \n",
              "1                   1.0             4.0       4.0        0.0       None   \n",
              "2                   2.0             3.0       1.0        0.0       None   \n",
              "3                   3.0             4.0       1.0        0.0       None   \n",
              "4                   4.0             4.0       1.0        0.0       None   \n",
              "...                 ...             ...       ...        ...        ...   \n",
              "2315291             5.0             4.0       2.0  1900000.0  Zacatecas   \n",
              "2315292             1.0             NaN       1.0        0.0   Trancoso   \n",
              "2315293             2.0             NaN       1.0        0.0   Trancoso   \n",
              "2315294             3.0             NaN       1.0        0.0   Trancoso   \n",
              "2315295             1.0             1.0       2.0    86511.0   Trancoso   \n",
              "\n",
              "           aÃ±o  \n",
              "0           NaN  \n",
              "1           NaN  \n",
              "2           NaN  \n",
              "3           NaN  \n",
              "4           NaN  \n",
              "...         ...  \n",
              "2315291  2023.0  \n",
              "2315292  2023.0  \n",
              "2315293  2023.0  \n",
              "2315294  2023.0  \n",
              "2315295  2023.0  \n",
              "\n",
              "[2315296 rows x 18 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>año</th>\n",
              "      <th>mes</th>\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0884e0d0-ddc1-42d0-b5bf-5d922c6ed4a3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d046132-ff62-4c2c-8c9a-3d2f0de5cfdc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d046132-ff62-4c2c-8c9a-3d2f0de5cfdc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Llenar un columna con datos vacios de otra"
      ],
      "metadata": {
        "id": "Yk0eMLSOurOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "db.describe()"
      ],
      "metadata": {
        "id": "cQ_MwH99vRLK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "5b849062-4de0-43a2-b83b-bc2c40de24aa"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                año           mes       cve_ent       cve_mun     organismo  \\\n",
              "count  2.229569e+06  2.315296e+06  2.314990e+06  2.310658e+06  2.315296e+06   \n",
              "mean   2.019457e+03  6.698356e+00  1.662320e+01  4.327088e+01  2.040924e+00   \n",
              "std    1.673627e+00  3.427975e+00  8.367111e+00  5.346035e+01  1.936413e+00   \n",
              "min    2.017000e+03  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
              "25%    2.018000e+03  4.000000e+00  1.000000e+01  1.000000e+01  1.000000e+00   \n",
              "50%    2.019000e+03  7.000000e+00  1.500000e+01  2.700000e+01  2.000000e+00   \n",
              "75%    2.021000e+03  1.000000e+01  2.300000e+01  5.400000e+01  2.000000e+00   \n",
              "max    2.022000e+03  1.200000e+01  3.200000e+01  5.700000e+02  2.600000e+01   \n",
              "\n",
              "          modalidad       destino          tipo          sexo    edad_rango  \\\n",
              "count  2.315296e+06  2.307319e+06  2.315296e+06  2.302037e+06  2.272656e+06   \n",
              "mean   2.019879e+00  2.307388e+00  1.372196e+00  1.425450e+00  1.800512e+00   \n",
              "std    8.984140e-01  1.591711e+00  4.833903e-01  4.944112e-01  5.160368e-01   \n",
              "min    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
              "25%    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
              "50%    2.000000e+00  2.000000e+00  1.000000e+00  1.000000e+00  2.000000e+00   \n",
              "75%    3.000000e+00  3.000000e+00  2.000000e+00  2.000000e+00  2.000000e+00   \n",
              "max    4.000000e+00  1.800000e+01  2.000000e+00  2.000000e+00  3.000000e+00   \n",
              "\n",
              "       ingresos_rango  vivienda_valor      acciones         monto     aÃ±o  \n",
              "count    2.224333e+06    1.877622e+06  2.315296e+06  2.315296e+06  85727.0  \n",
              "mean     3.279109e+00    2.979838e+00  2.810308e+00  1.123629e+06   2023.0  \n",
              "std      1.747322e+00    1.354455e+00  2.579043e+01  7.059333e+06      0.0  \n",
              "min      1.000000e+00    1.000000e+00  0.000000e+00 -4.246328e+05   2023.0  \n",
              "25%      2.000000e+00    2.000000e+00  1.000000e+00  5.140500e+04   2023.0  \n",
              "50%      3.000000e+00    3.000000e+00  1.000000e+00  4.673786e+05   2023.0  \n",
              "75%      5.000000e+00    4.000000e+00  2.000000e+00  1.080409e+06   2023.0  \n",
              "max      6.000000e+00    6.000000e+00  3.215300e+04  7.854406e+09   2023.0  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-2b11b5d3-3446-4a32-b608-e62296376e79\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>año</th>\n",
              "      <th>mes</th>\n",
              "      <th>cve_ent</th>\n",
              "      <th>cve_mun</th>\n",
              "      <th>organismo</th>\n",
              "      <th>modalidad</th>\n",
              "      <th>destino</th>\n",
              "      <th>tipo</th>\n",
              "      <th>sexo</th>\n",
              "      <th>edad_rango</th>\n",
              "      <th>ingresos_rango</th>\n",
              "      <th>vivienda_valor</th>\n",
              "      <th>acciones</th>\n",
              "      <th>monto</th>\n",
              "      <th>aÃ±o</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.229569e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>2.314990e+06</td>\n",
              "      <td>2.310658e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>2.307319e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>2.302037e+06</td>\n",
              "      <td>2.272656e+06</td>\n",
              "      <td>2.224333e+06</td>\n",
              "      <td>1.877622e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>2.315296e+06</td>\n",
              "      <td>85727.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.019457e+03</td>\n",
              "      <td>6.698356e+00</td>\n",
              "      <td>1.662320e+01</td>\n",
              "      <td>4.327088e+01</td>\n",
              "      <td>2.040924e+00</td>\n",
              "      <td>2.019879e+00</td>\n",
              "      <td>2.307388e+00</td>\n",
              "      <td>1.372196e+00</td>\n",
              "      <td>1.425450e+00</td>\n",
              "      <td>1.800512e+00</td>\n",
              "      <td>3.279109e+00</td>\n",
              "      <td>2.979838e+00</td>\n",
              "      <td>2.810308e+00</td>\n",
              "      <td>1.123629e+06</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.673627e+00</td>\n",
              "      <td>3.427975e+00</td>\n",
              "      <td>8.367111e+00</td>\n",
              "      <td>5.346035e+01</td>\n",
              "      <td>1.936413e+00</td>\n",
              "      <td>8.984140e-01</td>\n",
              "      <td>1.591711e+00</td>\n",
              "      <td>4.833903e-01</td>\n",
              "      <td>4.944112e-01</td>\n",
              "      <td>5.160368e-01</td>\n",
              "      <td>1.747322e+00</td>\n",
              "      <td>1.354455e+00</td>\n",
              "      <td>2.579043e+01</td>\n",
              "      <td>7.059333e+06</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.017000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-4.246328e+05</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.018000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.140500e+04</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.019000e+03</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>2.700000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.673786e+05</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.021000e+03</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>5.400000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.080409e+06</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.022000e+03</td>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>5.700000e+02</td>\n",
              "      <td>2.600000e+01</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.800000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>3.215300e+04</td>\n",
              "      <td>7.854406e+09</td>\n",
              "      <td>2023.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b11b5d3-3446-4a32-b608-e62296376e79')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8795158b-c266-4043-a98b-29331a1617a0\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8795158b-c266-4043-a98b-29331a1617a0')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8795158b-c266-4043-a98b-29331a1617a0 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b11b5d3-3446-4a32-b608-e62296376e79 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2b11b5d3-3446-4a32-b608-e62296376e79');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#valores_nulos = db.isnull().mean() * 100\n",
        "#valores_nulos\n",
        "\n",
        "valores_nulos = db.isnull().sum()\n",
        "porcentajes_nulos = valores_nulos / db.shape[0] * 100\n",
        "porcentajes_nulos_ordenados = porcentajes_nulos.sort_values(ascending=False)\n",
        "porcentajes_nulos_ordenados"
      ],
      "metadata": {
        "id": "b0L5kHXPv04V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4a0aa38-4c06-431c-ea09-ec607289466f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "aÃ±o              96.297363\n",
              "minicipio         81.231773\n",
              "vivienda_valor    18.903587\n",
              "municipio         18.768227\n",
              "ingresos_rango     3.928785\n",
              "año                3.702637\n",
              "edad_rango         1.841665\n",
              "sexo               0.572670\n",
              "destino            0.344535\n",
              "cve_mun            0.200320\n",
              "cve_ent            0.013216\n",
              "organismo          0.000000\n",
              "modalidad          0.000000\n",
              "mes                0.000000\n",
              "entidad            0.000000\n",
              "acciones           0.000000\n",
              "monto              0.000000\n",
              "tipo               0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db['municipio'] = db['municipio'].fillna(db['minicipio'])\n",
        "db['año'] = db['año'].fillna(db['aÃ±o'])\n",
        "db.drop(['aÃ±o','minicipio'], axis=1, inplace=True)\n",
        "db"
      ],
      "metadata": {
        "id": "G96DnMtAu4Xk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "d3e3eff0-6e49-4952-d096-512b480d75dc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            año  mes  cve_ent         entidad  cve_mun       municipio  \\\n",
              "0        2019.0    1      1.0  Aguascalientes      NaN  No distribuido   \n",
              "1        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "2        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "3        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "4        2019.0    1      1.0  Aguascalientes      1.0  Aguascalientes   \n",
              "...         ...  ...      ...             ...      ...             ...   \n",
              "2315291  2023.0    4     32.0       Zacatecas     56.0       Zacatecas   \n",
              "2315292  2023.0    4     32.0       Zacatecas     57.0        Trancoso   \n",
              "2315293  2023.0    4     32.0       Zacatecas     57.0        Trancoso   \n",
              "2315294  2023.0    4     32.0       Zacatecas     57.0        Trancoso   \n",
              "2315295  2023.0    4     32.0       Zacatecas     57.0        Trancoso   \n",
              "\n",
              "         organismo  modalidad  destino  tipo  sexo  edad_rango  \\\n",
              "0                4          2      1.0     1   NaN         NaN   \n",
              "1                1          1      2.0     2   1.0         NaN   \n",
              "2                1          1      2.0     2   1.0         NaN   \n",
              "3                1          1      2.0     2   1.0         NaN   \n",
              "4                1          1      2.0     2   1.0         NaN   \n",
              "...            ...        ...      ...   ...   ...         ...   \n",
              "2315291          3          3      3.0     1   1.0         2.0   \n",
              "2315292          1          2      1.0     2   1.0         2.0   \n",
              "2315293          1          2      1.0     2   2.0         2.0   \n",
              "2315294          1          2      1.0     2   2.0         2.0   \n",
              "2315295          2          2      1.0     1   2.0         2.0   \n",
              "\n",
              "         ingresos_rango  vivienda_valor  acciones      monto  \n",
              "0                   NaN             NaN       0.0    96860.0  \n",
              "1                   1.0             4.0       4.0        0.0  \n",
              "2                   2.0             3.0       1.0        0.0  \n",
              "3                   3.0             4.0       1.0        0.0  \n",
              "4                   4.0             4.0       1.0        0.0  \n",
              "...                 ...             ...       ...        ...  \n",
              "2315291             5.0             4.0       2.0  1900000.0  \n",
              "2315292             1.0             NaN       1.0        0.0  \n",
              "2315293             2.0             NaN       1.0        0.0  \n",
              "2315294             3.0             NaN       1.0        0.0  \n",
              "2315295             1.0             1.0       2.0    86511.0  \n",
              "\n",
              "[2315296 rows x 16 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>año</th>\n",
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              "      <th>...</th>\n",
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              "<p>2315296 rows × 16 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9dedbb3d-c4ec-49e3-aab8-26302caf1de6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9dedbb3d-c4ec-49e3-aab8-26302caf1de6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PxRnt4RbGGtj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Re ordenar las columnas de acuerdo orden original\n"
      ],
      "metadata": {
        "id": "7bu4RIgkFGRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "column_order = ['año', 'municipio', 'mes', 'cve_ent', 'entidad', 'cve_mun', 'organismo', 'modalidad', 'destino', 'tipo', 'sexo', 'edad_rango', 'ingresos_rango', 'vivienda_valor', 'acciones', 'monto']\n",
        "\n",
        "db = db.reindex(columns=column_order)"
      ],
      "metadata": {
        "id": "t4B-gz7uxPkx"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "db.info()"
      ],
      "metadata": {
        "id": "ieAEVAInvvAj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ed462db-06ad-44a6-f3ff-36005f342763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2315296 entries, 0 to 2315295\n",
            "Data columns (total 16 columns):\n",
            " #   Column          Dtype  \n",
            "---  ------          -----  \n",
            " 0   año             float64\n",
            " 1   municipio       object \n",
            " 2   mes             int64  \n",
            " 3   cve_ent         float64\n",
            " 4   entidad         object \n",
            " 5   cve_mun         float64\n",
            " 6   organismo       int64  \n",
            " 7   modalidad       int64  \n",
            " 8   destino         float64\n",
            " 9   tipo            int64  \n",
            " 10  sexo            float64\n",
            " 11  edad_rango      float64\n",
            " 12  ingresos_rango  float64\n",
            " 13  vivienda_valor  float64\n",
            " 14  acciones        float64\n",
            " 15  monto           float64\n",
            "dtypes: float64(10), int64(4), object(2)\n",
            "memory usage: 282.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Diccionario\n",
        "\n",
        "Para reemplazar los códigos de rango de edad con sus descripciones correspondientes en la columna 'edad_rango', puedes utilizar el método replace de pandas. Aquí tienes un ejemplo de cómo hacerlo"
      ],
      "metadata": {
        "id": "B2M3Mq5-IWHy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age_dict = {\n",
        "    1: '29 o menos',\n",
        "    2: '30 a 59',\n",
        "    3: '60 o más',\n",
        "    4: 'No especificado'\n",
        "}\n",
        "\n",
        "db['edad_rango'] = db['edad_rango'].replace(age_dict)\n",
        "db.edad_rango"
      ],
      "metadata": {
        "id": "DNYYkudaIQSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "471b57d2-212f-4379-fd5f-67c8220f71d6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              NaN\n",
              "1              NaN\n",
              "2              NaN\n",
              "3              NaN\n",
              "4              NaN\n",
              "            ...   \n",
              "2315291    30 a 59\n",
              "2315292    30 a 59\n",
              "2315293    30 a 59\n",
              "2315294    30 a 59\n",
              "2315295    30 a 59\n",
              "Name: edad_rango, Length: 2315296, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Supongamos que 'merged_df' es el dataframe combinado\n",
        "income_dict = {\n",
        "    1: '2.6 o menos',\n",
        "    2: '2.61 a 4.00',\n",
        "    3: '4.01 a 6.00',\n",
        "    4: '6.01 a 9.00',\n",
        "    5: '9.01 a 12.00',\n",
        "    6: 'Más de 12',\n",
        "    7: 'No especificado'\n",
        "}\n",
        "\n",
        "# Reemplazar los códigos de rango de ingresos con las descripciones correspondientes\n",
        "db['ingresos_rango'] = db['ingresos_rango'].replace(income_dict)\n",
        "\n",
        "# Imprimir el dataframe actualizado\n",
        "print(db)\n"
      ],
      "metadata": {
        "id": "o0ZXe8gSPRYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae5b49a9-985f-4c01-8978-0ebe289bfdc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            año       municipio  mes  cve_ent         entidad  cve_mun  \\\n",
            "0        2019.0  No distribuido    1      1.0  Aguascalientes      NaN   \n",
            "1        2019.0  Aguascalientes    1      1.0  Aguascalientes      1.0   \n",
            "2        2019.0  Aguascalientes    1      1.0  Aguascalientes      1.0   \n",
            "3        2019.0  Aguascalientes    1      1.0  Aguascalientes      1.0   \n",
            "4        2019.0  Aguascalientes    1      1.0  Aguascalientes      1.0   \n",
            "...         ...             ...  ...      ...             ...      ...   \n",
            "2315291  2023.0       Zacatecas    4     32.0       Zacatecas     56.0   \n",
            "2315292  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315293  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315294  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315295  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "\n",
            "         organismo  modalidad  destino  tipo  sexo edad_rango ingresos_rango  \\\n",
            "0                4          2      1.0     1   NaN        NaN            NaN   \n",
            "1                1          1      2.0     2   1.0        NaN    2.6 o menos   \n",
            "2                1          1      2.0     2   1.0        NaN    2.61 a 4.00   \n",
            "3                1          1      2.0     2   1.0        NaN    4.01 a 6.00   \n",
            "4                1          1      2.0     2   1.0        NaN    6.01 a 9.00   \n",
            "...            ...        ...      ...   ...   ...        ...            ...   \n",
            "2315291          3          3      3.0     1   1.0    30 a 59   9.01 a 12.00   \n",
            "2315292          1          2      1.0     2   1.0    30 a 59    2.6 o menos   \n",
            "2315293          1          2      1.0     2   2.0    30 a 59    2.61 a 4.00   \n",
            "2315294          1          2      1.0     2   2.0    30 a 59    4.01 a 6.00   \n",
            "2315295          2          2      1.0     1   2.0    30 a 59    2.6 o menos   \n",
            "\n",
            "         vivienda_valor  acciones      monto  \n",
            "0                   NaN       0.0    96860.0  \n",
            "1                   4.0       4.0        0.0  \n",
            "2                   3.0       1.0        0.0  \n",
            "3                   4.0       1.0        0.0  \n",
            "4                   4.0       1.0        0.0  \n",
            "...                 ...       ...        ...  \n",
            "2315291             4.0       2.0  1900000.0  \n",
            "2315292             NaN       1.0        0.0  \n",
            "2315293             NaN       1.0        0.0  \n",
            "2315294             NaN       1.0        0.0  \n",
            "2315295             1.0       2.0    86511.0  \n",
            "\n",
            "[2315296 rows x 16 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para reemplazar los códigos de valor de vivienda con sus descripciones correspondientes en la columna 'vivienda_valor', puedes utilizar el método replace de pandas. Aquí tienes un ejemplo de cómo hacerlo:\n",
        "\n"
      ],
      "metadata": {
        "id": "C_6gY4EjV5ZL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing_dict = {\n",
        "    1: 'Económica',\n",
        "    2: 'Popular',\n",
        "    3: 'Tradicional',\n",
        "    4: 'Media',\n",
        "    5: 'Residencial',\n",
        "    6: 'Residencial plus',\n",
        "    7: 'No especificado'\n",
        "}\n",
        "\n",
        "db['vivienda_valor'] = db['vivienda_valor'].replace(housing_dict)\n",
        "\n",
        "print(db)\n"
      ],
      "metadata": {
        "id": "yAeZyFehVcR-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5df51478-37a0-406c-9dfd-fde3d69acbdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            año       municipio  mes  cve_ent         entidad  cve_mun  \\\n",
            "0        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "1        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "2        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "3        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "4        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "...         ...             ...  ...      ...             ...      ...   \n",
            "2315291  2023.0       Zacatecas    4     32.0       Zacatecas     56.0   \n",
            "2315292  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315293  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315294  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315295  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "\n",
            "         organismo  modalidad  destino  tipo  sexo edad_rango ingresos_rango  \\\n",
            "0                4          1     10.0     1   NaN        NaN            NaN   \n",
            "1                4          2      1.0     1   NaN        NaN            NaN   \n",
            "2               10          1      2.0     2   1.0    30 a 59    2.6 o menos   \n",
            "3               10          1      2.0     2   1.0    30 a 59    2.61 a 4.00   \n",
            "4               10          1      2.0     2   1.0    30 a 59    2.61 a 4.00   \n",
            "...            ...        ...      ...   ...   ...        ...            ...   \n",
            "2315291          3          3      3.0     1   1.0    30 a 59   9.01 a 12.00   \n",
            "2315292          1          2      1.0     2   1.0    30 a 59    2.6 o menos   \n",
            "2315293          1          2      1.0     2   2.0    30 a 59    2.61 a 4.00   \n",
            "2315294          1          2      1.0     2   2.0    30 a 59    4.01 a 6.00   \n",
            "2315295          2          2      1.0     1   2.0    30 a 59    2.6 o menos   \n",
            "\n",
            "        vivienda_valor  acciones        monto  \n",
            "0                  NaN       0.0  13732400.00  \n",
            "1                  NaN       0.0  91871861.99  \n",
            "2          Tradicional       1.0    536000.00  \n",
            "3          Tradicional       6.0   4354000.00  \n",
            "4                Media       7.0   6006000.00  \n",
            "...                ...       ...          ...  \n",
            "2315291          Media       2.0   1900000.00  \n",
            "2315292            NaN       1.0         0.00  \n",
            "2315293            NaN       1.0         0.00  \n",
            "2315294            NaN       1.0         0.00  \n",
            "2315295      Económica       2.0     86511.00  \n",
            "\n",
            "[2315296 rows x 16 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "organism_dict = {\n",
        "    1: \"INFONAVIT\",\n",
        "    2: \"CNBV\",\n",
        "    3: \"FOVISSSTE\",\n",
        "    4: \"SHF\",\n",
        "    5: \"CONAVI\",\n",
        "    6: \"INVI\",\n",
        "    7: \"Banjercito\",\n",
        "    8: \"Hábitat México\",\n",
        "    9: \"CFE\",\n",
        "    10: \"ISSFAM\",\n",
        "    11: \"PEMEX\",\n",
        "    12: \"FONHAPO\",\n",
        "    13: \"PDZP SEDESOL\",\n",
        "    14: \"SOFOLES AMFE\",\n",
        "    15: \"ISSSTELEON\",\n",
        "    16: \"INDIVI\",\n",
        "    17: \"COVEG\",\n",
        "    18: \"COESVI\",\n",
        "    19: \"IMEVIS\",\n",
        "    20: \"IVEM\",\n",
        "    21: \"ITAVU\",\n",
        "    22: \"IVNL\",\n",
        "    23: \"INVIVIENDA\",\n",
        "    24: \"IVEY\",\n",
        "    25: \"INFOVIR\",\n",
        "    26: \"Instituto Nacional del Suelo Sustentable\"\n",
        "}\n",
        "\n",
        "db['organismo'].replace(organism_dict, inplace=True)\n",
        "\n",
        "print(db)\n"
      ],
      "metadata": {
        "id": "YEtMOFsHXbXX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efef1cf5-577e-49b9-af0d-bb774bc6fd65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            año       municipio  mes  cve_ent         entidad  cve_mun  \\\n",
            "0        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "1        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "2        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "3        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "4        2018.0  No distribuido    1      NaN  No distribuido      NaN   \n",
            "...         ...             ...  ...      ...             ...      ...   \n",
            "2315291  2023.0       Zacatecas    4     32.0       Zacatecas     56.0   \n",
            "2315292  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315293  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315294  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "2315295  2023.0        Trancoso    4     32.0       Zacatecas     57.0   \n",
            "\n",
            "         organismo  modalidad  destino  tipo  sexo edad_rango ingresos_rango  \\\n",
            "0              SHF          1     10.0     1   NaN        NaN            NaN   \n",
            "1              SHF          2      1.0     1   NaN        NaN            NaN   \n",
            "2           ISSFAM          1      2.0     2   1.0    30 a 59    2.6 o menos   \n",
            "3           ISSFAM          1      2.0     2   1.0    30 a 59    2.61 a 4.00   \n",
            "4           ISSFAM          1      2.0     2   1.0    30 a 59    2.61 a 4.00   \n",
            "...            ...        ...      ...   ...   ...        ...            ...   \n",
            "2315291  FOVISSSTE          3      3.0     1   1.0    30 a 59   9.01 a 12.00   \n",
            "2315292  INFONAVIT          2      1.0     2   1.0    30 a 59    2.6 o menos   \n",
            "2315293  INFONAVIT          2      1.0     2   2.0    30 a 59    2.61 a 4.00   \n",
            "2315294  INFONAVIT          2      1.0     2   2.0    30 a 59    4.01 a 6.00   \n",
            "2315295       CNBV          2      1.0     1   2.0    30 a 59    2.6 o menos   \n",
            "\n",
            "        vivienda_valor  acciones        monto  \n",
            "0                  NaN       0.0  13732400.00  \n",
            "1                  NaN       0.0  91871861.99  \n",
            "2          Tradicional       1.0    536000.00  \n",
            "3          Tradicional       6.0   4354000.00  \n",
            "4                Media       7.0   6006000.00  \n",
            "...                ...       ...          ...  \n",
            "2315291          Media       2.0   1900000.00  \n",
            "2315292            NaN       1.0         0.00  \n",
            "2315293            NaN       1.0         0.00  \n",
            "2315294            NaN       1.0         0.00  \n",
            "2315295      Económica       2.0     86511.00  \n",
            "\n",
            "[2315296 rows x 16 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graficar con m"
      ],
      "metadata": {
        "id": "ajIfdKlgXp1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "db.to_csv(\"data.csv\")\n",
        "db.head(10)\n",
        "db.shape"
      ],
      "metadata": {
        "id": "GjHJofbZX3ZC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8dfd8ec-4ae7-4772-93d0-23f4b8209bb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2315296, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "# Haz un histograma de la columna 'año'\n",
        "\n"
      ],
      "metadata": {
        "id": "jr0wAYjOhRTP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Haz un histograma de la columna 'año'\n",
        "db['año'].hist(bins=30, edgecolor='black')\n",
        "\n",
        "# Añade títulos y etiquetas\n",
        "plt.title('Histograma de Años')\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Frecuencia')\n"
      ],
      "metadata": {
        "id": "FxG0XXE3hAKK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "418f804d-dd81-4f3a-ae77-b3d6e0de02a9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frecuencia')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(db['organismo'].value_counts())\n",
        "db['organismo'].value_counts().plot(kind='pie')\n",
        "plt.title('Número de Acciones por Organismo')\n",
        "plt.xlabel('Organismo')\n",
        "plt.ylabel('Número de Acciones')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "BzfG_MunkOx_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "ceb96bb0-9196-4be9-bcf8-01692ef289b7"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1     1131684\n",
            "2      713844\n",
            "3      211375\n",
            "4      125001\n",
            "5       72172\n",
            "12      27759\n",
            "9        8517\n",
            "6        8347\n",
            "10       4807\n",
            "7        4282\n",
            "26       4021\n",
            "8        3269\n",
            "11        218\n",
            "Name: organismo, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "sI6F8SbExawr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_grouped = db.groupby('año')['monto'].mean().plot(kind='line',marker='o')\n",
        "\n",
        "# Dibuja el gráfico de líneas\n",
        "\n",
        "#print(data_grouped)\n",
        "#plt.plot(data_grouped.index, data_grouped.values, marker='o')\n",
        "\n",
        "# Añade títulos y etiquetas\n",
        "#plt.title('Cambio en el Monto a lo largo de los Años')\n",
        "#plt.xlabel('Año')\n",
        "#plt.ylabel('Monto Promedio')\n",
        "\n",
        "#plt.show()\n",
        "#-----------------------------------------------------------------\n",
        "#print(db['organismo'].value_counts())\n",
        "#db['organismo'].value_counts().plot(kind='line')\n",
        "\n",
        "#plt.title('Numero de acciones por organismo')\n",
        "#plt.xlabel('Organismo')\n",
        "#plt.ylabel('Numero de Acciones')\n",
        "#plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "jN38LjNehnIy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "7738a00a-e8bd-45c7-aa1d-d2d518105c8c"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1     1131684\n",
            "2      713844\n",
            "3      211375\n",
            "4      125001\n",
            "5       72172\n",
            "12      27759\n",
            "9        8517\n",
            "6        8347\n",
            "10       4807\n",
            "7        4282\n",
            "26       4021\n",
            "8        3269\n",
            "11        218\n",
            "Name: organismo, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db.shape"
      ],
      "metadata": {
        "id": "WojYrtiDnBS9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56d77f54-a124-444f-a8e1-21f361083a6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2315296, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Se duplico el gasto de costo de vivienda"
      ],
      "metadata": {
        "id": "tRxzlaMVlN13"
      }
    }
  ]
}