risk_factor_data_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "risk factor data analysis",
      "provenance": [],
      "collapsed_sections": [
        "ETrp2DtRZka3",
        "oWy7AsVgd7i6",
        "-U8KQcFTI7rk"
      ],
      "mount_file_id": "1tB2sULozwj31wpzZOMhXpi1-ljmpk70-",
      "authorship_tag": "ABX9TyOnVO2CjDweXSDB0RgOBH8Y",
      "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/Python-Neiva/risk-factors-covid19/blob/main/risk_factor_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1mNHrree0tI"
      },
      "source": [
        "#Request"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK5Xw1xMe5L8"
      },
      "source": [
        "import requests\n",
        "\n",
        "csv_url = \"https://www.datos.gov.co/api/views/gt2j-8ykr/rows.csv?&accessType=DOWNLOAD&api_foundry=true\"\n",
        "#csv_url = \"./content/sample_data/sample_downloaded.csv\"\n",
        "root_path = \"/content/drive/MyDrive/Salisbury/data/covid_downloaded.csv\"\n",
        "def downloadDataCsv(csv_url,root_path):\n",
        "  req = requests.get(csv_url)\n",
        "  url_content = req.content\n",
        "  try:\n",
        "      csv_file = open(root_path, 'wb')\n",
        "\n",
        "      csv_file.write(url_content)\n",
        "\n",
        "      csv_file.close()\n",
        "      print(\"I finished to write csv\")\n",
        "  except:\n",
        "      print(\"something was wrong\")\n",
        "\n",
        "#downloadDataCsv(csv_url,root_path)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEHaKmL6r1Me"
      },
      "source": [
        "#Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9oAju1KydJH"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from datetime import datetime\n",
        "import math\n",
        "\n",
        "# import os\n",
        "# !pip install sodapy -q\n",
        "# !pip install socrata-py -q\n",
        "\n",
        "# from sodapy import Socrata\n",
        "# from socrata.authorization import Authorization\n"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGV_v1H8NAmR"
      },
      "source": [
        "def requestColombianData(limit = 1000):\n",
        "  client = Socrata(\n",
        "                  \"www.datos.gov.co\",\n",
        "                  \"idLwSviPi5dGMTd7GfIfW3CLU\",\n",
        "                  #username=\"sebastian_romerola@fet.edu.co\",\n",
        "                  #password=\"Sebastian#2018\",\n",
        "                  timeout=300\n",
        "                  )\n",
        "  results = client.get(\"gt2j-8ykr\",\n",
        "                      #  ciudad_municipio_nom=\"Neiva\",\n",
        "                      departamento_nom=\"HUILA\",\n",
        "                      limit=limit\n",
        "                      )\n",
        "  \n",
        "  return pd.DataFrame.from_records(results)\n",
        "\n",
        "\n",
        "def getCsvDataSet(path='/content/drive/MyDrive/Salisbury/data/covid_downloaded.csv'):\n",
        "  return pd.read_csv(r''+path, low_memory=False)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbtZ8Bu1PjSF"
      },
      "source": [
        "#dataset from csv\n",
        "#df = getCsvDataSet()\n",
        "\n",
        "path_huila_csv = \"/content/drive/MyDrive/Salisbury/data/covid_downloaded_huila1.csv\" # Huila data to do not overpast the power\n",
        "df = getCsvDataSet(path_huila_csv)\n",
        "\n",
        "#dataset from API\n",
        "# df = None\n",
        "# df = requestColombianData(90000)"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "PqzU-WCWCk6O",
        "outputId": "48e68387-57a3-4215-b38a-a1a05a948f2f"
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>29</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
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              "      <td>1</td>\n",
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              "      <td>25/3/2020 0:00:00</td>\n",
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              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
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              "      <td>NEIVA</td>\n",
              "      <td>79</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
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              "      <td>14/3/2020 0:00:00</td>\n",
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              "      <td>15/3/2020 0:00:00</td>\n",
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              "      <td>24/3/2020 0:00:00</td>\n",
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              "      <th>6</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>44</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
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              "      <td>1</td>\n",
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              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
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              "      <th>7</th>\n",
              "      <td>17/3/2020 0:00:00</td>\n",
              "      <td>90</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
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              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17/3/2020 0:00:00</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>19/3/2020 0:00:00</td>\n",
              "      <td>124</td>\n",
              "      <td>16/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
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              "      <td>NEIVA</td>\n",
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              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>8/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19/3/2020 0:00:00</td>\n",
              "      <td>4/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>21/3/2020 0:00:00</td>\n",
              "      <td>196</td>\n",
              "      <td>19/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>724.0</td>\n",
              "      <td>ESPAÑA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21/3/2020 0:00:00</td>\n",
              "      <td>2/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>23/3/2020 0:00:00</td>\n",
              "      <td>286</td>\n",
              "      <td>21/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>83</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>18/3/2020 0:00:00</td>\n",
              "      <td>6/4/2020 0:00:00</td>\n",
              "      <td>23/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>341</td>\n",
              "      <td>21/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
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              "      <td>NEIVA</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>20/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>25/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>353</td>\n",
              "      <td>22/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41524</td>\n",
              "      <td>PALERMO</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>218.0</td>\n",
              "      <td>ECUADOR</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>21/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>13/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>359</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>18/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>25/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>29/3/2020 0:00:00</td>\n",
              "      <td>659</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>18/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29/3/2020 0:00:00</td>\n",
              "      <td>1/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>29/3/2020 0:00:00</td>\n",
              "      <td>660</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>20/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29/3/2020 0:00:00</td>\n",
              "      <td>3/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>709</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>12/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>710</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>22/3/2020 0:00:00</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>711</td>\n",
              "      <td>20/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>724.0</td>\n",
              "      <td>ESPAÑA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>6/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>712</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41524</td>\n",
              "      <td>PALERMO</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>18/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>14/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      web_date_report  case_id  ... ethnicity  ethnic_group_name\n",
              "0   12/3/2020 0:00:00       12  ...       6.0                NaN\n",
              "1   12/3/2020 0:00:00       13  ...       6.0                NaN\n",
              "2   12/3/2020 0:00:00       29  ...       6.0                NaN\n",
              "3   14/3/2020 0:00:00       41  ...       6.0                NaN\n",
              "4   14/3/2020 0:00:00       42  ...       6.0                NaN\n",
              "5   14/3/2020 0:00:00       43  ...       6.0                NaN\n",
              "6   14/3/2020 0:00:00       44  ...       6.0                NaN\n",
              "7   17/3/2020 0:00:00       90  ...       6.0                NaN\n",
              "8   19/3/2020 0:00:00      124  ...       6.0                NaN\n",
              "9   21/3/2020 0:00:00      196  ...       6.0                NaN\n",
              "10  23/3/2020 0:00:00      286  ...       6.0                NaN\n",
              "11  24/3/2020 0:00:00      341  ...       6.0                NaN\n",
              "12  24/3/2020 0:00:00      353  ...       6.0                NaN\n",
              "13  24/3/2020 0:00:00      359  ...       6.0                NaN\n",
              "14  29/3/2020 0:00:00      659  ...       6.0                NaN\n",
              "15  29/3/2020 0:00:00      660  ...       6.0                NaN\n",
              "16  30/3/2020 0:00:00      709  ...       6.0                NaN\n",
              "17  30/3/2020 0:00:00      710  ...       6.0                NaN\n",
              "18  30/3/2020 0:00:00      711  ...       6.0                NaN\n",
              "19  30/3/2020 0:00:00      712  ...       6.0                NaN\n",
              "\n",
              "[20 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_gn7jCllaRJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b29d2466-5c39-4725-b043-db19c4e98dcf"
      },
      "source": [
        "\n",
        "print(df.columns)\n",
        "english_columns = list()\n",
        "english_columns = [\n",
        "  'web_date_report',\n",
        "  'case_id',\n",
        "  'notification_date',\n",
        "  'davipola_departament_code',\n",
        "  'department_name',\n",
        "  'davipola_municipality_code',\n",
        "  'municipality_name',\n",
        "  'age',\n",
        "  'age_measurement_unit',\n",
        "  'sex',\n",
        "  'contagion_type',\n",
        "  'case_location',\n",
        "  'state',\n",
        "  'iso_country_code',\n",
        "  'country_name',\n",
        "  'recuperated',\n",
        "  'beginning_symptoms_date',\n",
        "  'date_of_death',\n",
        "  'date_of_diagnosis',\n",
        "  'date_of_recovery',\n",
        "  'recovery_type',\n",
        "  'ethnicity',\n",
        "  'ethnic_group_name',\n",
        "]\n",
        "\n",
        "df.columns = english_columns\n",
        "print(\"-----\")\n",
        "print(df.columns)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['web_date_report', 'case_id', 'notification_date',\n",
            "       'davipola_departament_code', 'department_name',\n",
            "       'davipola_municipality_code', 'municipality_name', 'age',\n",
            "       'age_measurement_unit', 'sex', 'contagion_type', 'case_location',\n",
            "       'state', 'iso_country_code', 'country_name', 'recuperated',\n",
            "       'beginning_symptoms_date', 'date_of_death', 'date_of_diagnosis',\n",
            "       'date_of_recovery', 'recovery_type', 'ethnicity', 'ethnic_group_name'],\n",
            "      dtype='object')\n",
            "-----\n",
            "Index(['web_date_report', 'case_id', 'notification_date',\n",
            "       'davipola_departament_code', 'department_name',\n",
            "       'davipola_municipality_code', 'municipality_name', 'age',\n",
            "       'age_measurement_unit', 'sex', 'contagion_type', 'case_location',\n",
            "       'state', 'iso_country_code', 'country_name', 'recuperated',\n",
            "       'beginning_symptoms_date', 'date_of_death', 'date_of_diagnosis',\n",
            "       'date_of_recovery', 'recovery_type', 'ethnicity', 'ethnic_group_name'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Jm1DuQvKBRWo",
        "outputId": "42f5bcba-2449-4ebf-96cd-d01d8affd41a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>web_date_report</th>\n",
              "      <th>case_id</th>\n",
              "      <th>notification_date</th>\n",
              "      <th>davipola_departament_code</th>\n",
              "      <th>department_name</th>\n",
              "      <th>davipola_municipality_code</th>\n",
              "      <th>municipality_name</th>\n",
              "      <th>age</th>\n",
              "      <th>age_measurement_unit</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>iso_country_code</th>\n",
              "      <th>country_name</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>beginning_symptoms_date</th>\n",
              "      <th>date_of_death</th>\n",
              "      <th>date_of_diagnosis</th>\n",
              "      <th>date_of_recovery</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>ethnic_group_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>12</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>380.0</td>\n",
              "      <td>ITALIA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>9/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>13</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>29</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>42</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     web_date_report  case_id  ... ethnicity  ethnic_group_name\n",
              "0  12/3/2020 0:00:00       12  ...       6.0                NaN\n",
              "1  12/3/2020 0:00:00       13  ...       6.0                NaN\n",
              "2  12/3/2020 0:00:00       29  ...       6.0                NaN\n",
              "3  14/3/2020 0:00:00       41  ...       6.0                NaN\n",
              "4  14/3/2020 0:00:00       42  ...       6.0                NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAMa-iZYkVQ8"
      },
      "source": [
        "df.astype = {\n",
        "  \"web_date_report\": np.str,\n",
        "  \"case_id\": np.str, #ordinal\n",
        "  \"notification_date\": np.str,\n",
        "  \"davipola_departament_code\": np.int, #categorical\n",
        "  \"department_name\": np.str, #categorical\n",
        "  \"davipola_municipality_code\": np.str, #categorical\n",
        "  \"municipality_name\": np.str, #categorical\n",
        "  \"age\": np.int,\n",
        "  \"age_measurement_unit\": np.int,\n",
        "  \"sex\": np.str, #nominal\n",
        "  \"contagion_type\": np.str, #categorical\n",
        "  \"case_location\": np.str, #categorical\n",
        "  \"state\": np.str, #ordinal\n",
        "  \"iso_country_code\": np.int, #categorical\n",
        "  \"country_name\": np.str, #categorical\n",
        "  \"recuperated\": np.str, ##categorical\n",
        "  \"beginning_symptoms_date\": np.str, #\n",
        "  \"date_of_diagnosis\": np.str,\n",
        "  \"date_of_recovery\": np.str,\n",
        "  \"recovery_type\": np.str, #categorica\n",
        "  \"ethnicity\": np.int, #nominal\n",
        "  \"ethnic_group_name\": np.str, #nominal\n",
        "  \"date_of_death\": np.str,\n",
        "}\n",
        "\n",
        "#change column type in df"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWw8ufEwjs92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbd2e7d5-604f-4764-ca64-4d295c622a44"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 81572 entries, 0 to 81571\n",
            "Data columns (total 23 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   web_date_report             81572 non-null  object \n",
            " 1   case_id                     81572 non-null  int64  \n",
            " 2   notification_date           81572 non-null  object \n",
            " 3   davipola_departament_code   81572 non-null  int64  \n",
            " 4   department_name             81572 non-null  object \n",
            " 5   davipola_municipality_code  81572 non-null  int64  \n",
            " 6   municipality_name           81572 non-null  object \n",
            " 7   age                         81572 non-null  int64  \n",
            " 8   age_measurement_unit        81572 non-null  int64  \n",
            " 9   sex                         81572 non-null  object \n",
            " 10  contagion_type              81572 non-null  object \n",
            " 11  case_location               81277 non-null  object \n",
            " 12  state                       81277 non-null  object \n",
            " 13  iso_country_code            22 non-null     float64\n",
            " 14  country_name                22 non-null     object \n",
            " 15  recuperated                 81331 non-null  object \n",
            " 16  beginning_symptoms_date     81083 non-null  object \n",
            " 17  date_of_death               3029 non-null   object \n",
            " 18  date_of_diagnosis           81561 non-null  object \n",
            " 19  date_of_recovery            75725 non-null  object \n",
            " 20  recovery_type               75725 non-null  object \n",
            " 21  ethnicity                   80579 non-null  float64\n",
            " 22  ethnic_group_name           716 non-null    object \n",
            "dtypes: float64(2), int64(5), object(16)\n",
            "memory usage: 14.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNekwrfDin8u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a65f0e5b-6e57-47ae-8654-51d02efc3b88"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>case_id</th>\n",
              "      <th>davipola_departament_code</th>\n",
              "      <th>davipola_municipality_code</th>\n",
              "      <th>age</th>\n",
              "      <th>age_measurement_unit</th>\n",
              "      <th>iso_country_code</th>\n",
              "      <th>ethnicity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.157200e+04</td>\n",
              "      <td>81572.0</td>\n",
              "      <td>81572.000000</td>\n",
              "      <td>81572.000000</td>\n",
              "      <td>81572.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>80579.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.229969e+06</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41165.728216</td>\n",
              "      <td>40.493723</td>\n",
              "      <td>1.003923</td>\n",
              "      <td>537.090909</td>\n",
              "      <td>5.953896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.355254e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>255.512829</td>\n",
              "      <td>17.786207</td>\n",
              "      <td>0.068856</td>\n",
              "      <td>254.366610</td>\n",
              "      <td>0.471003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41001.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.022241e+06</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41001.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>276.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.898528e+06</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41001.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>544.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.554832e+06</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41306.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>811.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.639482e+06</td>\n",
              "      <td>41.0</td>\n",
              "      <td>41885.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>840.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            case_id  davipola_departament_code  ...  iso_country_code     ethnicity\n",
              "count  8.157200e+04                    81572.0  ...         22.000000  80579.000000\n",
              "mean   2.229969e+06                       41.0  ...        537.090909      5.953896\n",
              "std    1.355254e+06                        0.0  ...        254.366610      0.471003\n",
              "min    1.200000e+01                       41.0  ...        214.000000      1.000000\n",
              "25%    1.022241e+06                       41.0  ...        276.000000      6.000000\n",
              "50%    1.898528e+06                       41.0  ...        544.000000      6.000000\n",
              "75%    3.554832e+06                       41.0  ...        811.000000      6.000000\n",
              "max    4.639482e+06                       41.0  ...        840.000000      6.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4cG8R8jqH5O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00205569-5a32-4f7b-a301-74414903a75a"
      },
      "source": [
        "np.shape(df)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(81572, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYWhEm1ETIGR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f9ebc02-e887-4795-c743-54386d58928f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['web_date_report', 'case_id', 'notification_date',\n",
              "       'davipola_departament_code', 'department_name',\n",
              "       'davipola_municipality_code', 'municipality_name', 'age',\n",
              "       'age_measurement_unit', 'sex', 'contagion_type', 'case_location',\n",
              "       'state', 'iso_country_code', 'country_name', 'recuperated',\n",
              "       'beginning_symptoms_date', 'date_of_death', 'date_of_diagnosis',\n",
              "       'date_of_recovery', 'recovery_type', 'ethnicity', 'ethnic_group_name'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0Yj_EMOkPqX"
      },
      "source": [
        "uppercase to each str row"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8d-WoPAmkTio",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "4644c56d-17c5-4218-8c38-3894766bd94f"
      },
      "source": [
        "df.select_dtypes(include='object')"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>web_date_report</th>\n",
              "      <th>notification_date</th>\n",
              "      <th>department_name</th>\n",
              "      <th>municipality_name</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>country_name</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>beginning_symptoms_date</th>\n",
              "      <th>date_of_death</th>\n",
              "      <th>date_of_diagnosis</th>\n",
              "      <th>date_of_recovery</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnic_group_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ITALIA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>9/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81567</th>\n",
              "      <td>18/7/2021 0:00:00</td>\n",
              "      <td>2/7/2021 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>29/6/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13/7/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81568</th>\n",
              "      <td>18/7/2021 0:00:00</td>\n",
              "      <td>2/7/2021 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>TESALIA</td>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>29/6/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13/7/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81569</th>\n",
              "      <td>18/7/2021 0:00:00</td>\n",
              "      <td>3/7/2021 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>30/6/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/7/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81570</th>\n",
              "      <td>18/7/2021 0:00:00</td>\n",
              "      <td>3/7/2021 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>30/6/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/7/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81571</th>\n",
              "      <td>18/7/2021 0:00:00</td>\n",
              "      <td>3/7/2021 0:00:00</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>F</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>30/6/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/7/2021 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81572 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         web_date_report  notification_date  ... recovery_type ethnic_group_name\n",
              "0      12/3/2020 0:00:00  10/3/2020 0:00:00  ...           PCR               NaN\n",
              "1      12/3/2020 0:00:00  10/3/2020 0:00:00  ...           PCR               NaN\n",
              "2      12/3/2020 0:00:00  12/3/2020 0:00:00  ...           PCR               NaN\n",
              "3      14/3/2020 0:00:00  14/3/2020 0:00:00  ...           PCR               NaN\n",
              "4      14/3/2020 0:00:00  14/3/2020 0:00:00  ...           PCR               NaN\n",
              "...                  ...                ...  ...           ...               ...\n",
              "81567  18/7/2021 0:00:00   2/7/2021 0:00:00  ...           NaN               NaN\n",
              "81568  18/7/2021 0:00:00   2/7/2021 0:00:00  ...           NaN               NaN\n",
              "81569  18/7/2021 0:00:00   3/7/2021 0:00:00  ...           NaN               NaN\n",
              "81570  18/7/2021 0:00:00   3/7/2021 0:00:00  ...           NaN               NaN\n",
              "81571  18/7/2021 0:00:00   3/7/2021 0:00:00  ...           NaN               NaN\n",
              "\n",
              "[81572 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1EbCKOHA50G"
      },
      "source": [
        "verifying nulls\n",
        "\n",
        "\n",
        "```\n",
        "print(df.isnull().values.any())\n",
        "```\n",
        "[source](https://towardsdatascience.com/a-complete-guide-to-principal-component-analysis-pca-in-machine-learning-664f34fc3e5a)\n",
        "\n",
        "After use this function, the result of the code is false.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DZ6zuozBE1u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e46b829a-f28d-4748-f46a-8f5461e30722"
      },
      "source": [
        "print(df.isnull().values.any())"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mn6u-_6NiPqp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b295ba5e-5fa7-41fa-cd27-8ec50d5c31c5"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 81572 entries, 0 to 81571\n",
            "Data columns (total 23 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   web_date_report             81572 non-null  object \n",
            " 1   case_id                     81572 non-null  int64  \n",
            " 2   notification_date           81572 non-null  object \n",
            " 3   davipola_departament_code   81572 non-null  int64  \n",
            " 4   department_name             81572 non-null  object \n",
            " 5   davipola_municipality_code  81572 non-null  int64  \n",
            " 6   municipality_name           81572 non-null  object \n",
            " 7   age                         81572 non-null  int64  \n",
            " 8   age_measurement_unit        81572 non-null  int64  \n",
            " 9   sex                         81572 non-null  object \n",
            " 10  contagion_type              81572 non-null  object \n",
            " 11  case_location               81277 non-null  object \n",
            " 12  state                       81277 non-null  object \n",
            " 13  iso_country_code            22 non-null     float64\n",
            " 14  country_name                22 non-null     object \n",
            " 15  recuperated                 81331 non-null  object \n",
            " 16  beginning_symptoms_date     81083 non-null  object \n",
            " 17  date_of_death               3029 non-null   object \n",
            " 18  date_of_diagnosis           81561 non-null  object \n",
            " 19  date_of_recovery            75725 non-null  object \n",
            " 20  recovery_type               75725 non-null  object \n",
            " 21  ethnicity                   80579 non-null  float64\n",
            " 22  ethnic_group_name           716 non-null    object \n",
            "dtypes: float64(2), int64(5), object(16)\n",
            "memory usage: 14.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzevG-kZIBgA"
      },
      "source": [
        "Similarity mesurement (radio basics funciotn)\n",
        "1.  \n",
        "*   ML / rbf -| knn graph / \n",
        "*   train to know about who would most highly survive\n",
        "*   KNN graph (find the most near neibough\n",
        "2.  \n",
        "*   svm (support vector machine)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Adjency matrix. to build a graph after apply rbf and knn aproach?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6lbFCjoVeYm"
      },
      "source": [
        "for i in df.select_dtypes(include='object'):\n",
        "  df[i] = df[i].str.upper()"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdZ6fSw7fvvV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "868aa4d1-6565-498b-fcaf-8443976d79f8"
      },
      "source": [
        "compression_opts = dict(method='zip',\n",
        "                        archive_name='covid_downloaded_huila1.csv')#compression parameters if needed  \n",
        "\n",
        "excel_df = df[df[\"department_name\"]==\"HUILA\"] # taking just the desired data\n",
        "excel_df.head() # check if desired data is the expected\n",
        "#excel_df.to_csv('/content/drive/MyDrive/Salisbury/data/covid_downloaded_huila1.csv',index=False) # path to save the generated csv document\n",
        "#excel_df.to_excel('/content/drive/MyDrive/Salisbury/data/output.xlsx',sheet_name='Sheet_name_2') # path to save the generated excel document"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>web_date_report</th>\n",
              "      <th>case_id</th>\n",
              "      <th>notification_date</th>\n",
              "      <th>davipola_departament_code</th>\n",
              "      <th>department_name</th>\n",
              "      <th>davipola_municipality_code</th>\n",
              "      <th>municipality_name</th>\n",
              "      <th>age</th>\n",
              "      <th>age_measurement_unit</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>iso_country_code</th>\n",
              "      <th>country_name</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>beginning_symptoms_date</th>\n",
              "      <th>date_of_death</th>\n",
              "      <th>date_of_diagnosis</th>\n",
              "      <th>date_of_recovery</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>ethnic_group_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>12</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>380.0</td>\n",
              "      <td>ITALIA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>9/4/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>13</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>6/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>30/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>29</td>\n",
              "      <td>12/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>25/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>28/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>42</td>\n",
              "      <td>14/3/2020 0:00:00</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>10/3/2020 0:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15/3/2020 0:00:00</td>\n",
              "      <td>24/3/2020 0:00:00</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     web_date_report  case_id  ... ethnicity  ethnic_group_name\n",
              "0  12/3/2020 0:00:00       12  ...       6.0                NaN\n",
              "1  12/3/2020 0:00:00       13  ...       6.0                NaN\n",
              "2  12/3/2020 0:00:00       29  ...       6.0                NaN\n",
              "3  14/3/2020 0:00:00       41  ...       6.0                NaN\n",
              "4  14/3/2020 0:00:00       42  ...       6.0                NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPM7orWqgfIE"
      },
      "source": [
        "df[\"beginning_symptoms_date\"] = pd.to_datetime(df[\"beginning_symptoms_date\"],format='%d/%m/%Y %H:%M:%S') #change date format from '%d/%m/%Y %H:%M:%S' to '%%Y-d-%m %H:%M:%S'\n",
        "df[\"date_of_death\"] = pd.to_datetime(df[\"date_of_death\"],format='%d/%m/%Y %H:%M:%S')\n",
        "df[\"web_date_report\"] = pd.to_datetime(df[\"web_date_report\"],format='%d/%m/%Y %H:%M:%S')\n",
        "df[\"date_of_recovery\"] = pd.to_datetime(df[\"date_of_recovery\"],format='%d/%m/%Y %H:%M:%S')\n",
        "df[\"date_of_diagnosis\"] = pd.to_datetime(df[\"date_of_diagnosis\"],format='%d/%m/%Y %H:%M:%S')\n",
        "df[\"notification_date\"] = pd.to_datetime(df[\"notification_date\"],format='%d/%m/%Y %H:%M:%S')"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2hKjT1D750o"
      },
      "source": [
        "Does is first greather than second?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OniBUS0LyT_V",
        "outputId": "0b7b0b08-83aa-4349-ef2c-2b3cccd7a2f6"
      },
      "source": [
        "df[\"beginning_symptoms_date\"][0] > df[\"beginning_symptoms_date\"][1]"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TS07MeQFPtc"
      },
      "source": [
        "This let find categories, regardless upper or lower case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TAnau-WwoaT2"
      },
      "source": [
        "Analisis de componentes principales (APC)\n",
        "\n",
        "quitar lo que se puede.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R_mL1pUp_Cp"
      },
      "source": [
        "desviación estandar de  para analizar causas de muerte"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40F9oHJIdgcL"
      },
      "source": [
        "*italicized text*.fit\n",
        "\n",
        "se realiza conversión a todos los que tengan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-ayFfxrMmEh"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkydYBByUQDC"
      },
      "source": [
        "#////////////////////////////////////////////////////////#\n",
        "#                                                        /\n",
        "#      Binarize Sex column values to avoid errors        /\n",
        "#                                                        /\n",
        "#////////////////////////////////////////////////////////#\n",
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "#/-////////////////////////////////////////////////////////#\n",
        "#                                                         /\n",
        "#              average of recovery time per genre         /\n",
        "#                                                         /\n",
        "#/////////////////////////////////////////////////////////#\n",
        "\n",
        "#////////////////////////////////////////////////////////#\n",
        "#                                                        /\n",
        "#     Capitalize Sex column values to avoid errors       /\n",
        "#                                                        /\n",
        "#////////////////////////////////////////////////////////#\n",
        "\n",
        "#////////////////////////////////////////////////////////#\n",
        "#                                                        /\n",
        "#  substract betweeen two dates and store it the answer  /\n",
        "#                                                        /\n",
        "#////////////////////////////////////////////////////////#\n",
        "\n",
        "def get_duration_of_illness(x:any):\n",
        "  time = 0\n",
        "  z = list()\n",
        "  count = 0\n",
        "  for e in x.index:\n",
        "    count += 1\n",
        "    if x.date_of_recovery[e]:\n",
        "      #time delta - .dt.days to integer\n",
        "      z.append(x.date_of_recovery[e] - x.beginning_symptoms_date[e])\n",
        "    elif x.date_of_death[e]:\n",
        "      z.append(x.date_of_death[e] - x.beginning_symptoms_date[e])\n",
        "    elif x.date_of_death[e] == 0 and x.date_of_recovery[e] == 0:\n",
        "      print(\"Unknown\")\n",
        "      x.loc[e, \"illness_time\"] = None\n",
        "\n",
        "  print(\"The total of patients is: \"+ str(count))\n",
        "\n",
        "  return x\n",
        "  \n",
        "#array_dates_new_dates = get_duration_of_illness(df)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQm-H4C3IPhL"
      },
      "source": [
        "Replacing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytLrt03f_Zgn"
      },
      "source": [
        "#df.boxplot('age','contagion_type',rot = 0,figsize=(5,6))"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE4mOkukhxNS"
      },
      "source": [
        "# Data Cleaning\n",
        "Label or one hot enconding or replacing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pY3J6Y0vrszr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b53dd16-0a41-40fe-a877-dba1b4cdba93"
      },
      "source": [
        "df.dtypes.value_counts()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "object            10\n",
              "datetime64[ns]     6\n",
              "int64              5\n",
              "float64            2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k36LrmMwUUVx",
        "outputId": "0b61c732-5df5-4edd-f6e5-b50027032976"
      },
      "source": [
        "df['sex'].describe()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     81572\n",
              "unique        2\n",
              "top           F\n",
              "freq      41803\n",
              "Name: sex, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlO8tbkQoQV2"
      },
      "source": [
        "*  Fill empty spaces with 0.\n",
        "*  Determine unneccessary feateres (columns).\n",
        "*  Delete departament name && municipality name.\n",
        "*  Binarize sex column.\n",
        "*  Delete country name.\n",
        "*  Normalize recovery type.\n",
        "*  Write documentation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50YQp67DfC6L"
      },
      "source": [
        "## Fill empty spaces with 0.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbWGM0JuYKI7"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLA1L0PLSk6V"
      },
      "source": [
        "#df = getCsvDataSet(path_huila_csv)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lE709nOU5wLG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5ff7912-07cc-4e4d-999b-1f467f78e529"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "web_date_report                   0\n",
              "case_id                           0\n",
              "notification_date                 0\n",
              "davipola_departament_code         0\n",
              "department_name                   0\n",
              "davipola_municipality_code        0\n",
              "municipality_name                 0\n",
              "age                               0\n",
              "age_measurement_unit              0\n",
              "sex                               0\n",
              "contagion_type                    0\n",
              "case_location                   295\n",
              "state                           295\n",
              "iso_country_code              81550\n",
              "country_name                  81550\n",
              "recuperated                     241\n",
              "beginning_symptoms_date         489\n",
              "date_of_death                 78543\n",
              "date_of_diagnosis                11\n",
              "date_of_recovery               5847\n",
              "recovery_type                  5847\n",
              "ethnicity                       993\n",
              "ethnic_group_name             80856\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RQxCRMmAsfO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "f1f3f90a-5b5b-4f7a-dc37-0641c3af1cf2"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>web_date_report</th>\n",
              "      <th>case_id</th>\n",
              "      <th>notification_date</th>\n",
              "      <th>davipola_departament_code</th>\n",
              "      <th>department_name</th>\n",
              "      <th>davipola_municipality_code</th>\n",
              "      <th>municipality_name</th>\n",
              "      <th>age</th>\n",
              "      <th>age_measurement_unit</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>iso_country_code</th>\n",
              "      <th>country_name</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>beginning_symptoms_date</th>\n",
              "      <th>date_of_death</th>\n",
              "      <th>date_of_diagnosis</th>\n",
              "      <th>date_of_recovery</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>ethnic_group_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>12</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>380.0</td>\n",
              "      <td>ITALIA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>13</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>29</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>41</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>2020-03-28</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>42</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>43</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>44</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>90</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>124</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>196</td>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>41</td>\n",
              "      <td>HUILA</td>\n",
              "      <td>41001</td>\n",
              "      <td>NEIVA</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>724.0</td>\n",
              "      <td>ESPAÑA</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>2020-04-02</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  web_date_report  case_id  ... ethnicity  ethnic_group_name\n",
              "0      2020-03-12       12  ...       6.0                NaN\n",
              "1      2020-03-12       13  ...       6.0                NaN\n",
              "2      2020-03-12       29  ...       6.0                NaN\n",
              "3      2020-03-14       41  ...       6.0                NaN\n",
              "4      2020-03-14       42  ...       6.0                NaN\n",
              "5      2020-03-14       43  ...       6.0                NaN\n",
              "6      2020-03-14       44  ...       6.0                NaN\n",
              "7      2020-03-17       90  ...       6.0                NaN\n",
              "8      2020-03-19      124  ...       6.0                NaN\n",
              "9      2020-03-21      196  ...       6.0                NaN\n",
              "\n",
              "[10 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUXKWPd4RoPS"
      },
      "source": [
        "#Data Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aolMCqlu0sVq"
      },
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "import xgboost\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from math import sqrt\n",
        "\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler #,OrdinalEncoder"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEvDBQjRCe_v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a443a66b-9952-452e-cf02-91bf9da29ce0"
      },
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "# from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "!pip install feature-engine # to install CategoricalImputer\n",
        "from feature_engine.imputation import CategoricalImputer"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: feature-engine in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.7/dist-packages (from feature-engine) (1.19.5)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from feature-engine) (1.4.1)\n",
            "Requirement already satisfied: statsmodels>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from feature-engine) (0.12.2)\n",
            "Requirement already satisfied: scikit-learn>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from feature-engine) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas>=1.0.3 in /usr/local/lib/python3.7/dist-packages (from feature-engine) (1.1.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.3->feature-engine) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.3->feature-engine) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.3->feature-engine) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.2->feature-engine) (1.0.1)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.11.1->feature-engine) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TAolkndhrXX"
      },
      "source": [
        "##Categorical Variables\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUc9Afx6bUGu"
      },
      "source": [
        "\n",
        "\n",
        "> Information about: Dinesh Yadav, https://towardsdatascience.com/categorical-encoding-using-label-encoding-and-one-hot-encoder-911ef77fb5bd.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkGsiwK3Bubi"
      },
      "source": [
        "Posible ways to replace\n",
        "[Pandas Documentation \n",
        "](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html)\n",
        "```\n",
        "df.replace({'A': {0: 100, 4: 400}})\n",
        "Result:\n",
        "        A  B  C\n",
        "0  100  5  a\n",
        "1    1  6  b\n",
        "2    2  7  c\n",
        "3    3  8  d\n",
        "4  400  9  e\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2htjEN9NaAhY"
      },
      "source": [
        "standarization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "otss_huJ6-v5"
      },
      "source": [
        "## Data import for test\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSVQ8OA0z2hz"
      },
      "source": [
        "#storage test data\n",
        "#path = '/content/drive/MyDrive/Salisbury/data/covid_downloaded_1000.csv' #1000 rows from datos abiertos\n",
        "#train_csv_url = 'https://www.datos.gov.co/resource/gt2j-8ykr.csv'\n",
        "# downloadDataCsv(train_csv_url,path)"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwzSV-QCxX3X"
      },
      "source": [
        "#reading storaged test data\n",
        "#df_to_test = getCsvDataSet(path)\n",
        "#df_to_test.columns = english_columns # replace the columns name to english one\n",
        "#df_to_test = df_to_test.iloc[:, lambda df_to_test: [7,9,10,11,12,13,15,16,17,18,20,21]].copy()"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcMJ31BA0TEz"
      },
      "source": [
        "# print(df_to_test.info)\n",
        "# df_to_test.head()"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HYzrPFnPFeV"
      },
      "source": [
        "# for i in df_to_test.select_dtypes(include='object'):\n",
        "#   df_to_test[i] = df_to_test[i].str.upper()"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jps0Xs6CP5e7"
      },
      "source": [
        "#df_to_test.head()"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtEotC7nFMdl"
      },
      "source": [
        "##Train_data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u52V2JsNEpTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7daff69-3d01-4db4-d0d4-cf3e63266bea"
      },
      "source": [
        "df_to_train = df.iloc[:, lambda df: [7,9,10,11,12,\n",
        "                                     #13,\n",
        "                                     15,\n",
        "                                     #16,17,18,\n",
        "                                     20,\n",
        "                                     21\n",
        "                                     ]].copy()\n",
        "df_to_train.info()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 81572 entries, 0 to 81571\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             81572 non-null  int64  \n",
            " 1   sex             81572 non-null  object \n",
            " 2   contagion_type  81572 non-null  object \n",
            " 3   case_location   81277 non-null  object \n",
            " 4   state           81277 non-null  object \n",
            " 5   recuperated     81331 non-null  object \n",
            " 6   recovery_type   75725 non-null  object \n",
            " 7   ethnicity       80579 non-null  float64\n",
            "dtypes: float64(1), int64(1), object(6)\n",
            "memory usage: 5.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qFRmusOP3d6"
      },
      "source": [
        "##Preprocesing\n",
        "\n",
        "\n",
        "**Take** in mind\n",
        "\n",
        "*   https://towardsdatascience.com/categorical-encoding-using-label-encoding-and-one-hot-encoder-911ef77fb5bd\n",
        "\n",
        "*   https://plotly.com/python/knn-classification/\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhvLxnVPYaJf"
      },
      "source": [
        "##functions\n",
        "\n",
        "issue: It is not posible to work with NaN, so, it is neccessary to replace NaN values per something else, in order to be able to apply fit_transform to this ordinal variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nFIx4_fQL0XU",
        "outputId": "0c77f1d3-15a3-46f9-dafe-b6f1de91b1d4"
      },
      "source": [
        "df_to_train.head(60)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>74</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>79</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>71</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>53</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>76</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>50</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>83</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>68</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>41</td>\n",
              "      <td>M</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>42</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>26</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>21</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>64</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>27</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>36</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>39</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>50</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>COMUNITARIA</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>59</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>56</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>41</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>53</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>31</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>68</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>74</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>77</td>\n",
              "      <td>M</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>50</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>94</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>44</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>27</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>89</td>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>78</td>\n",
              "      <td>F</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>79</td>\n",
              "      <td>M</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>FALLECIDO</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>24</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>60</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>44</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>26</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>20</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>42</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>44</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>60</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>73</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>36</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age sex contagion_type  ... recuperated recovery_type ethnicity\n",
              "0    74   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "1    68   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "2    55   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "3    79   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "4    52   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "5    71   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "6    24   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "7    53   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "8    76   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "9    50   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "10   83   F    RELACIONADO  ...   FALLECIDO           NaN       6.0\n",
              "11   68   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "12   41   M      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "13   42   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "14   26   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "15   21   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "16   32   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "17   64   F    RELACIONADO  ...   FALLECIDO           NaN       6.0\n",
              "18   27   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "19   45   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "20   33   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "21   36   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "22   33   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "23   61   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "24   26   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "25   45   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "26   39   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "27   50   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "28   47   M    COMUNITARIA  ...  RECUPERADO           PCR       6.0\n",
              "29   59   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "30   56   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "31   47   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "32   41   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "33   53   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "34   31   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "35   68   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "36   74   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "37   54   M      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "38   77   M      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "39   50   M    RELACIONADO  ...  RECUPERADO        TIEMPO       6.0\n",
              "40   94   F    RELACIONADO  ...  RECUPERADO        TIEMPO       6.0\n",
              "41   44   M    RELACIONADO  ...  RECUPERADO           PCR       5.0\n",
              "42   27   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "43   89   M     EN ESTUDIO  ...  RECUPERADO           PCR       6.0\n",
              "44   78   F     EN ESTUDIO  ...  RECUPERADO           PCR       6.0\n",
              "45   30   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "46   60   M    RELACIONADO  ...   FALLECIDO           NaN       6.0\n",
              "47   79   M      IMPORTADO  ...   FALLECIDO           NaN       6.0\n",
              "48   40   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "49   24   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "50   60   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "51   44   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "52   26   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "53   20   M    RELACIONADO  ...  RECUPERADO        TIEMPO       6.0\n",
              "54   42   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "55   44   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "56   60   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "57   61   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "58   73   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "59   36   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "\n",
              "[60 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRX98danNf_9",
        "outputId": "286ae5f5-fdd3-4e6e-b16a-e5be84e4f7ff"
      },
      "source": [
        "df_to_train[\"ethnicity\"].astype('str').dtypes"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aZ72B4olU3r"
      },
      "source": [
        "##Categorical Imputer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ks-6FlgS5h4Y"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "#!pip install feature-engine # to install CategoricalImputer\n",
        "from feature_engine.imputation import CategoricalImputer\n"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrwjImcrYEQy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d55e61ee-706a-4514-b65d-554c333b3eb3"
      },
      "source": [
        "numeric_features = ['age']\n",
        "categorical_features = ['sex','contagion_type','case_location','state','recovery_type']\n",
        "\n",
        "X = df_to_train.drop('recuperated', axis = 1)\n",
        "y = df_to_train['recuperated']\n",
        "\n",
        "imputer = CategoricalImputer(variables=categorical_features)\n",
        "imputer.fit(df_to_train.select_dtypes(include=\"object\"))"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoricalImputer(fill_value='Missing', ignore_format=False,\n",
              "                   imputation_method='missing', return_object=False,\n",
              "                   variables=['sex', 'contagion_type', 'case_location', 'state',\n",
              "                              'recovery_type'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RXKMMPyc7R6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "be4235eb-63b7-4a64-a5bb-3a6e408c7047"
      },
      "source": [
        "df_to_train.head(7)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>74</td>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>55</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>79</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>71</td>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age sex contagion_type  ... recuperated recovery_type ethnicity\n",
              "0   74   F      IMPORTADO  ...  RECUPERADO           PCR       6.0\n",
              "1   68   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "2   55   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "3   79   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "4   52   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "5   71   M    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "6   24   F    RELACIONADO  ...  RECUPERADO           PCR       6.0\n",
              "\n",
              "[7 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6dRa1SzRK34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af343fee-9c3c-4bbf-eed2-a96053b2859f"
      },
      "source": [
        "for i in df_to_train.columns:\n",
        "  print(i)\n",
        "  df_to_train[i] = df_to_train[i].fillna(\"MISSING_DATA\")"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age\n",
            "sex\n",
            "contagion_type\n",
            "case_location\n",
            "state\n",
            "recuperated\n",
            "recovery_type\n",
            "ethnicity\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyZhbhpeG3zb",
        "outputId": "0420ea53-63c0-4e1a-c506-fd578f54353c"
      },
      "source": [
        "print(df_to_train.isnull().sum())\n",
        "\n",
        "#imputer = CategoricalImputer(variables=['Alley', 'MasVnrType'])"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age               0\n",
            "sex               0\n",
            "contagion_type    0\n",
            "case_location     0\n",
            "state             0\n",
            "recuperated       0\n",
            "recovery_type     0\n",
            "ethnicity         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2ZKfsVc-yny"
      },
      "source": [
        "# # day\n",
        "# df_to_train['date_of_death_day'] = df_to_train['date_of_death'].dt.day\n",
        "# # month\n",
        "# df_to_train['date_of_death_month'] = df_to_train['date_of_death'].dt.month\n",
        "# # year\n",
        "# df_to_train['date_of_death_year'] = df_to_train['date_of_death'].dt.year"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAoHOAqKWsLW",
        "outputId": "054ab0cd-0c71-4c23-ead4-24e1cb73a6fa"
      },
      "source": [
        "(df_to_train['state'].unique())\n",
        "# state_categories = [ 0,'LEVE','MODERADO', 'GRAVE', 'FALLECIDO']\n",
        "# print(state_categories)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['LEVE', 'FALLECIDO', 'MISSING_DATA', 'MODERADO', 'GRAVE'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGMuqMI7WTBG"
      },
      "source": [
        "#oe = OrdinalEncoder(categories = [state_categories])\n",
        "ohe = OneHotEncoder(sparse = False)"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skOaph7mr-rN"
      },
      "source": [
        "numeric_features = ['age']\n",
        "categorical_features = ['sex','contagion_type','case_location','state','recuperated',\n",
        "                        #'ethnicity'\n",
        "                        ]"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFICvz0W8zH4"
      },
      "source": [
        "##applying data preprocesing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUFhWU4RT4wa"
      },
      "source": [
        "X = df_to_train.drop('recovery_type', axis = 1)\n",
        "y = df_to_train['recovery_type']\n",
        "\n",
        "column_transform = ColumnTransformer([\n",
        "                                      ('ohe',ohe, categorical_features),\n",
        "                                      ]    \n",
        "    )\n",
        "\n",
        "#['sex','contagion_type','case_location','state','recuperated','recovery_type']"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9Ui0u7ghNJ0"
      },
      "source": [
        "X = column_transform.fit_transform(X)"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfftoOTnBWA3",
        "outputId": "c9575f0b-91b8-4f53-886a-ba5fa260dae1"
      },
      "source": [
        "X[0]"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "       0., 0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5xx-8Pt86PF",
        "outputId": "26058c7c-5bc9-414e-8069-7957ae883f2a"
      },
      "source": [
        "df_to_train['age']"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        74\n",
              "1        68\n",
              "2        55\n",
              "3        79\n",
              "4        52\n",
              "         ..\n",
              "81567    41\n",
              "81568    37\n",
              "81569    21\n",
              "81570    17\n",
              "81571    31\n",
              "Name: age, Length: 81572, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLfquJhl7MYm"
      },
      "source": [
        "#for i in range(1,len(X)):\n",
        "  #X[i] = np.append(X[i],float(df_to_train['age'][i]))\n",
        "\n",
        "k_means_df = pd.DataFrame(X, columns = ['a', 'b', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n",
        "       'q', 'r', 's'])"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IB1PIgweCi6n"
      },
      "source": [
        "k_means_df['age'] = df_to_train['age']"
      ],
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4Ai5MJOwCwxK",
        "outputId": "b345af98-81c7-414a-a734-b10802bf54ab"
      },
      "source": [
        "k_means_df"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "      <th>f</th>\n",
              "      <th>g</th>\n",
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              "      <th>l</th>\n",
              "      <th>m</th>\n",
              "      <th>n</th>\n",
              "      <th>o</th>\n",
              "      <th>p</th>\n",
              "      <th>q</th>\n",
              "      <th>r</th>\n",
              "      <th>s</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.0</td>\n",
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              "      <th>81567</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81569</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81570</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81571</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81572 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         a    b    b    c    d    e    f  ...    n    o    p    q    r    s  age\n",
              "0      1.0  0.0  0.0  0.0  1.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  1.0   74\n",
              "1      1.0  0.0  0.0  0.0  0.0  1.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  1.0   68\n",
              "2      1.0  0.0  0.0  0.0  0.0  1.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  1.0   55\n",
              "3      0.0  1.0  0.0  0.0  0.0  1.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  1.0   79\n",
              "4      1.0  0.0  0.0  0.0  0.0  1.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  1.0   52\n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
              "81567  0.0  1.0  0.0  1.0  0.0  0.0  1.0  ...  0.0  0.0  1.0  0.0  0.0  0.0   41\n",
              "81568  0.0  1.0  0.0  1.0  0.0  0.0  1.0  ...  0.0  0.0  1.0  0.0  0.0  0.0   37\n",
              "81569  1.0  0.0  0.0  1.0  0.0  0.0  1.0  ...  0.0  0.0  1.0  0.0  0.0  0.0   21\n",
              "81570  0.0  1.0  0.0  1.0  0.0  0.0  1.0  ...  0.0  0.0  1.0  0.0  0.0  0.0   17\n",
              "81571  1.0  0.0  0.0  1.0  0.0  0.0  1.0  ...  0.0  0.0  1.0  0.0  0.0  0.0   31\n",
              "\n",
              "[81572 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isv1HlNOC27S"
      },
      "source": [
        ""
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DntulxSrtL1B"
      },
      "source": [
        "num_clusters = 3\n",
        "Kmean = KMeans(n_clusters=num_clusters)  \n",
        "predictions = Kmean.fit_predict(k_means_df[['a', 'b', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p',\n",
        "       'q', 'r', 's','age']])\n"
      ],
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVZBmfRHDtyk"
      },
      "source": [
        "k_means_df['predictions']=predictions"
      ],
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "JNIFSo0vHd-L",
        "outputId": "076f6093-c99e-42b6-97dd-9fcc2479ae4b"
      },
      "source": [
        "k_means_df"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
              "</table>\n",
              "<p>81572 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         a    b    b    c    d    e  ...    p    q    r    s  age  predictions\n",
              "0      1.0  0.0  0.0  0.0  1.0  0.0  ...  0.0  0.0  0.0  1.0   74            2\n",
              "1      1.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  1.0   68            2\n",
              "2      1.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  1.0   55            0\n",
              "3      0.0  1.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  1.0   79            2\n",
              "4      1.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  1.0   52            0\n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...          ...\n",
              "81567  0.0  1.0  0.0  1.0  0.0  0.0  ...  1.0  0.0  0.0  0.0   41            0\n",
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              "81571  1.0  0.0  0.0  1.0  0.0  0.0  ...  1.0  0.0  0.0  0.0   31            1\n",
              "\n",
              "[81572 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCdiDulXt1J-",
        "outputId": "317cf621-8f39-4056-ea53-abff7bc29e5f"
      },
      "source": [
        "Kmean.cluster_centers_"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.08576914e-01, 4.91423086e-01, 1.76244736e-01, 4.91423086e-01,\n",
              "        1.76244736e-01, 6.67946495e-01, 3.09635868e-04, 1.55499133e-01,\n",
              "        9.72163735e-01, 1.47077037e-02, 1.06514739e-02, 9.90834778e-04,\n",
              "        1.48625217e-03, 1.47077037e-02, 9.90834778e-04, 9.72163735e-01,\n",
              "        1.48625217e-03, 1.06514739e-02, 3.49888531e-02, 1.47077037e-02,\n",
              "        1.33143423e-03, 9.48972009e-01, 4.34334283e+01],\n",
              "       [5.24502534e-01, 4.75497466e-01, 1.78253615e-01, 4.75497466e-01,\n",
              "        1.78253615e-01, 6.47911259e-01, 2.16289705e-04, 1.73618836e-01,\n",
              "        9.89309109e-01, 2.03930293e-03, 7.01396614e-03, 4.32579409e-04,\n",
              "        1.20504264e-03, 2.03930293e-03, 4.32579409e-04, 9.89309109e-01,\n",
              "        1.20504264e-03, 7.01396614e-03, 3.18872822e-02, 2.03930293e-03,\n",
              "        8.96057348e-04, 9.65177358e-01, 2.36883574e+01],\n",
              "       [4.96866131e-01, 5.03133869e-01, 1.89865184e-01, 5.03133869e-01,\n",
              "        1.89865184e-01, 6.79931410e-01, 2.95648061e-04, 1.29907758e-01,\n",
              "        8.34614475e-01, 1.29671239e-01, 2.15823084e-02, 1.83301798e-03,\n",
              "        1.22989593e-02, 1.29671239e-01, 1.83301798e-03, 8.34614475e-01,\n",
              "        1.22989593e-02, 2.15823084e-02, 4.19820246e-02, 1.29671239e-01,\n",
              "        9.99290445e-03, 8.18353832e-01, 6.70398534e+01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fTl9qgYt3Ji",
        "outputId": "a2dfd3db-818d-4473-9c3c-c975e823273c"
      },
      "source": [
        "Kmean.labels_"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 0, ..., 1, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "Pah1ikdoEOlQ",
        "outputId": "0fe20ff3-9e22-41e9-a38b-4f935d8fc77b"
      },
      "source": [
        "three_d = plt.figure().gca(projection='3d')\n",
        "df_graph = [k_means_df[k_means_df.predictions==0]]\n",
        "\n",
        "for i in range(1,num_clusters):\n",
        "  df_graph.append(k_means_df[k_means_df.predictions==i])\n",
        "colors=['b', 'g', 'r', 'c', 'm', 'y', 'k','lightgreen','yellow','pink','grey','lightyellow','lightpink','lightgrey', 'purple','green']\n",
        "df_graph[0]\n",
        "for i in range(0,num_clusters):\n",
        "  three_d.scatter(df_graph[i].f,df_graph[i].s,df_graph[i].age,color=colors[i])\n",
        "three_d.scatter(Kmean.cluster_centers_[:,4],Kmean.cluster_centers_[:,21],Kmean.cluster_centers_[:,22],color='k',marker='X',label='centroid')\n",
        "three_d.legend()"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f89d569a610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfA8_u5OMbf7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "215c1a95-8f01-40c3-db0f-36016067b794"
      },
      "source": [
        "df_to_train.recovery_type.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 541
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GD2DImAgBjx9"
      },
      "source": [
        "##Column Transformer with Mixed Types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XmrqWplm2wm"
      },
      "source": [
        "#pipeeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHi0r-YABkxQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5f11b3f-4786-4e16-eff5-3a56c19e0204"
      },
      "source": [
        "numeric_features = ['age']\n",
        "categorical_features = ['sex',\n",
        "'contagion_type',\n",
        "'case_location',\n",
        "'state',\n",
        "'recovery_type',\n",
        "#'ethnicity'\n",
        "]\n",
        "\n",
        "X = df_to_train.drop('recuperated', axis = 1)\n",
        "y = df_to_train['recuperated']\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', categorical_transformer, categorical_features)\n",
        "        ]\n",
        "      )\n",
        "\n",
        "# Append classifier to preprocessing pipeline.\n",
        "# Now we have a full prediction pipeline.\n",
        "clf = Pipeline(steps=[\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('classifier', KMeans(n_clusters=5, random_state=0)),\n",
        "                      #('classifier', KNeighborsClassifier(n_neighbors=2)),\n",
        "                      ])\n",
        "                       \n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.3,\n",
        "    random_state=0)\n",
        "\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('preprocessor',\n",
              "                 ColumnTransformer(n_jobs=None, remainder='drop',\n",
              "                                   sparse_threshold=0.3,\n",
              "                                   transformer_weights=None,\n",
              "                                   transformers=[('num',\n",
              "                                                  Pipeline(memory=None,\n",
              "                                                           steps=[('imputer',\n",
              "                                                                   SimpleImputer(add_indicator=False,\n",
              "                                                                                 copy=True,\n",
              "                                                                                 fill_value=None,\n",
              "                                                                                 missing_values=nan,\n",
              "                                                                                 strategy='median',\n",
              "                                                                                 verbose=0)),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler(copy=True,\n",
              "                                                                                  with_mean...\n",
              "                                                                drop=None,\n",
              "                                                                dtype=<class 'numpy.float64'>,\n",
              "                                                                handle_unknown='ignore',\n",
              "                                                                sparse=True),\n",
              "                                                  ['sex', 'contagion_type',\n",
              "                                                   'case_location', 'state',\n",
              "                                                   'recovery_type'])],\n",
              "                                   verbose=False)),\n",
              "                ('classifier',\n",
              "                 KMeans(algorithm='auto', copy_x=True, init='k-means++',\n",
              "                        max_iter=300, n_clusters=5, n_init=10, n_jobs=None,\n",
              "                        precompute_distances='auto', random_state=0, tol=0.0001,\n",
              "                        verbose=0))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 465
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCwKYSqobT2n",
        "outputId": "9e23fbce-5609-4780-d29a-6c9a68b7f784"
      },
      "source": [
        "print(\"model score: %.3f\" % clf.score(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model score: -27371.785\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "NmDiDqPypZyQ",
        "outputId": "76b62647-2866-46e9-89fd-6e8896258c5c"
      },
      "source": [
        "y_train.value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0a95927b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 466
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "8hyJkPtPs3Oa",
        "outputId": "b5b2e302-70b0-4006-f9a3-20e839447fec"
      },
      "source": [
        "df_to_train['age'].value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0a9a8cc250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 476
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aDa9pLUuNkS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3724d57a-5af3-4f08-988b-212311401e49"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15993</th>\n",
              "      <td>29</td>\n",
              "      <td>F</td>\n",
              "      <td>COMUNITARIA</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6912</th>\n",
              "      <td>38</td>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73373</th>\n",
              "      <td>20</td>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13659</th>\n",
              "      <td>31</td>\n",
              "      <td>F</td>\n",
              "      <td>COMUNITARIA</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22173</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>COMUNITARIA</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>TIEMPO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       age sex contagion_type case_location state recovery_type ethnicity\n",
              "15993   29   F    COMUNITARIA          CASA  LEVE        TIEMPO         6\n",
              "6912    38   F    RELACIONADO          CASA  LEVE        TIEMPO         6\n",
              "73373   20   M     EN ESTUDIO          CASA  LEVE        TIEMPO         6\n",
              "13659   31   F    COMUNITARIA          CASA  LEVE        TIEMPO         6\n",
              "22173    3   M    COMUNITARIA          CASA  LEVE        TIEMPO         6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 468
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcwPMs6X506j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4248096c-e344-4b3a-d104-f10026183a26"
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 57100 entries, 15993 to 68268\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             57100 non-null  int64 \n",
            " 1   sex             57100 non-null  object\n",
            " 2   contagion_type  57100 non-null  object\n",
            " 3   case_location   57100 non-null  object\n",
            " 4   state           57100 non-null  object\n",
            " 5   recovery_type   57100 non-null  object\n",
            " 6   ethnicity       57100 non-null  object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 3.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGH_tJxP1auA"
      },
      "source": [
        "##Machine Learning Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLbtX0TAuOzB"
      },
      "source": [
        "# df_to_train\n",
        "# merge_data = df_to_train.append(df_to_train)\n",
        "# test_count = len(df_to_train)\n",
        "# count = len(merge_data)-test_count\n",
        "# X_cat = merge_data.copy()\n",
        "# X_cat = merge_data.select_dtypes(include=['object'])\n",
        "# X_enc = X_cat.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RfxzZl8rV0j"
      },
      "source": [
        "X_cat = df_to_train.select_dtypes(include=['object'])\n",
        "X_enc = X_cat.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pg3BZiyBkdin",
        "outputId": "59b54c07-7d03-4721-d3cf-8b7f16efb044"
      },
      "source": [
        "X_enc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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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>sex</th>\n",
              "      <th>contagion_type</th>\n",
              "      <th>case_location</th>\n",
              "      <th>state</th>\n",
              "      <th>recuperated</th>\n",
              "      <th>recovery_type</th>\n",
              "      <th>ethnicity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F</td>\n",
              "      <td>IMPORTADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>RELACIONADO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>RECUPERADO</td>\n",
              "      <td>PCR</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81567</th>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81568</th>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81569</th>\n",
              "      <td>F</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81570</th>\n",
              "      <td>M</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81571</th>\n",
              "      <td>F</td>\n",
              "      <td>EN ESTUDIO</td>\n",
              "      <td>CASA</td>\n",
              "      <td>LEVE</td>\n",
              "      <td>ACTIVO</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "      <td>MISSING_DATA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81572 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      sex contagion_type case_location  ... recuperated recovery_type     ethnicity\n",
              "0       F      IMPORTADO          CASA  ...  RECUPERADO           PCR             6\n",
              "1       F    RELACIONADO          CASA  ...  RECUPERADO           PCR             6\n",
              "2       F    RELACIONADO          CASA  ...  RECUPERADO           PCR             6\n",
              "3       M    RELACIONADO          CASA  ...  RECUPERADO           PCR             6\n",
              "4       F    RELACIONADO          CASA  ...  RECUPERADO           PCR             6\n",
              "...    ..            ...           ...  ...         ...           ...           ...\n",
              "81567   M     EN ESTUDIO          CASA  ...      ACTIVO  MISSING_DATA  MISSING_DATA\n",
              "81568   M     EN ESTUDIO          CASA  ...      ACTIVO  MISSING_DATA  MISSING_DATA\n",
              "81569   F     EN ESTUDIO          CASA  ...      ACTIVO  MISSING_DATA  MISSING_DATA\n",
              "81570   M     EN ESTUDIO          CASA  ...      ACTIVO  MISSING_DATA  MISSING_DATA\n",
              "81571   F     EN ESTUDIO          CASA  ...      ACTIVO  MISSING_DATA  MISSING_DATA\n",
              "\n",
              "[81572 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 472
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p06AJZhrry_T"
      },
      "source": [
        "categorical_features = ['sex','contagion_type','case_location','state','recovery_type','recuperated','ethnicity']\n",
        "\n",
        "#ONEHOT ENCODING BLOCK\n",
        "X_enc = pd.get_dummies(X_enc, columns = categorical_features)\n",
        "df_to_train = df_to_train.drop(categorical_features, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGEwlKU123a9"
      },
      "source": [
        "X_enc = df_to_train.append(X_enc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "xqzy5KBMpCS3",
        "outputId": "cae9f2c0-ba42-47bd-aa7d-3c90b9dfdfc9"
      },
      "source": [
        "X_enc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>sex_F</th>\n",
              "      <th>sex_M</th>\n",
              "      <th>contagion_type_COMUNITARIA</th>\n",
              "      <th>contagion_type_EN ESTUDIO</th>\n",
              "      <th>contagion_type_IMPORTADO</th>\n",
              "      <th>contagion_type_RELACIONADO</th>\n",
              "      <th>case_location_CASA</th>\n",
              "      <th>case_location_FALLECIDO</th>\n",
              "      <th>case_location_HOSPITAL</th>\n",
              "      <th>case_location_HOSPITAL UCI</th>\n",
              "      <th>case_location_MISSING_DATA</th>\n",
              "      <th>state_FALLECIDO</th>\n",
              "      <th>state_GRAVE</th>\n",
              "      <th>state_LEVE</th>\n",
              "      <th>state_MISSING_DATA</th>\n",
              "      <th>state_MODERADO</th>\n",
              "      <th>recovery_type_MISSING_DATA</th>\n",
              "      <th>recovery_type_PCR</th>\n",
              "      <th>recovery_type_TIEMPO</th>\n",
              "      <th>recuperated_ACTIVO</th>\n",
              "      <th>recuperated_FALLECIDO</th>\n",
              "      <th>recuperated_MISSING_DATA</th>\n",
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              "      <th>ethnicity_1.0</th>\n",
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              "      <th>ethnicity_6.0</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81569</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81570</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81571</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407860 rows × 29 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        age  sex_F  sex_M  ...  ethnicity_5.0  ethnicity_6.0  ethnicity_MISSING_DATA\n",
              "0      74.0    NaN    NaN  ...            NaN            NaN                     NaN\n",
              "1      68.0    NaN    NaN  ...            NaN            NaN                     NaN\n",
              "2      55.0    NaN    NaN  ...            NaN            NaN                     NaN\n",
              "3      79.0    NaN    NaN  ...            NaN            NaN                     NaN\n",
              "4      52.0    NaN    NaN  ...            NaN            NaN                     NaN\n",
              "...     ...    ...    ...  ...            ...            ...                     ...\n",
              "81567   NaN    0.0    1.0  ...            0.0            0.0                     1.0\n",
              "81568   NaN    0.0    1.0  ...            0.0            0.0                     1.0\n",
              "81569   NaN    1.0    0.0  ...            0.0            0.0                     1.0\n",
              "81570   NaN    0.0    1.0  ...            0.0            0.0                     1.0\n",
              "81571   NaN    1.0    0.0  ...            0.0            0.0                     1.0\n",
              "\n",
              "[407860 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 411
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlO3CkeHnvOw"
      },
      "source": [
        "#ONEHOT ENCODING BLOCK#X_enc = pd.get_dummies(X_enc, columns=['sex','region','smoker'])\n",
        "\n",
        "#mergedata = mergedata.drop(['sex','region','smoker'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Lv4LlE01sEF"
      },
      "source": [
        "##Pipeline\n",
        "[Using scikit-learn](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETrp2DtRZka3"
      },
      "source": [
        "#API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ezeh3drdQ6y"
      },
      "source": [
        "##Compromiso\n",
        "\n",
        "1. Servicio que colecta la data por departamento y la guarda en la base de datos\n",
        "    * Eliminar registros por departamento y volver a consultarlos y almacenarlos\n",
        "2. Servicio que recibe por parametro el id del departamento \n",
        "3. Servicio que recupera la info por municipio.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJg4HPW9Qa6j"
      },
      "source": [
        "##Commitment\n",
        "\n",
        "1. Service which collect the data from Colombia according to each departament (state)\n",
        "    * Delete records per departament and request it again\n",
        "2. Service which get by parameter the departament_id and return the information\n",
        "3. Service get municipality info\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWy7AsVgd7i6"
      },
      "source": [
        "#Documentation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BO2F88Rnch8C"
      },
      "source": [
        "request\n",
        "[location and count](https://datos.gov.co/resource/gt2j-8ykr.json?$select=%20count(id_de_caso),ubicacion&$group=ubicacion)\n",
        "\n",
        "Json\n",
        ">```\n",
        "count_id_de_caso\t\"11015\"\n",
        "ubicacion\t\"casa\"\n",
        "count_id_de_caso\t\"4028443\"\n",
        "ubicacion\t\"Casa\"\n",
        "count_id_de_caso\t\"7\"\n",
        "ubicacion\t\"CASA\"\n",
        "count_id_de_caso\t\"105326\"\n",
        "ubicacion\t\"Fallecido\"\n",
        "count_id_de_caso\t\"22865\"\n",
        "ubicacion\t\"Hospital\"\n",
        "count_id_de_caso\t\"4963\"\n",
        "ubicacion\t\"Hospital UCI\"\n",
        "count_id_de_caso\t\"14575\"\n",
        "ubicacion\t\"N/A\"\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F069Cqmqv-TC"
      },
      "source": [
        "Patient State [request](https://datos.gov.co/resource/gt2j-8ykr.json?$select=%20estado&$group=estado)\n",
        "\n",
        "\n",
        "***Patient state *** \n",
        "\n",
        ">```\n",
        "[{\"estado\":\"Fallecido\"}\n",
        ",{\"estado\":\"Grave\"}\n",
        ",{\"estado\":\"leve\"}\n",
        ",{\"estado\":\"Leve\"}\n",
        ",{\"estado\":\"LEVE\"}\n",
        ",{\"estado\":\"Moderado\"}\n",
        ",{\"estado\":\"N/A\"}]\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8Wch07qpLlb"
      },
      "source": [
        "Contagion types\n",
        "\n",
        "\n",
        "**source contagion type**\n",
        "[request](https://datos.gov.co/resource/gt2j-8ykr.json?$select=%20fuente_tipo_contagio&$group=fuente_tipo_contagio)\n",
        "\n",
        "Json\n",
        ">```\n",
        "[\n",
        "{\"fuente_tipo_contagio\":\"Comunitaria\"}\n",
        ",{\"fuente_tipo_contagio\":\"En estudio\"}\n",
        ",{\"fuente_tipo_contagio\":\"Importado\"}\n",
        ",{\"fuente_tipo_contagio\":\"Relacionado\"}\n",
        "]\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U8KQcFTI7rk"
      },
      "source": [
        "#Bayesian Algoritm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puqAa15EJN2y"
      },
      "source": [
        "#Clustering\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZ0j3tLWJho9"
      },
      "source": [
        "Advising by Ian Tomas, after meeting with Michael M. and Sebastian Romero L.\n",
        "\n",
        ">A good place to start would be a similarity measure in your data, which you can implement with a radial basis function. This function inputs two records from your data set and outputs a single number between 0 and 1 which represents how similar the two records are, where a value closer to 1 means they are more similar, closer to 0 means they are different. This is a good idea to use if you want to represent your data as a graph as typically the similarity measure can work as an edge weight in a graph.\n",
        "\n",
        "```\n",
        " void vectorsubtraction(vector<float> v1, vector<float> v2, vector<float> &v3){\n",
        "\n",
        "    int i;\n",
        "    //vectors must be same size\n",
        "    if(v1.size() != v2.size()) return;\n",
        "    for(i = 0; i < v1.size(); i++){\n",
        "        //if symbolic attribute, push 1 to represent not equal, 0 to represent equal\n",
        "        if(symbolic(i)){\n",
        "          v3.push_back(v1[i] != v2[i]);\n",
        "          //if continous, subtract values\n",
        "        } else {\n",
        "          v3.push_back((v1[i] - v2[i]));\n",
        "        }\n",
        "    }\n",
        "}\n",
        "\n",
        "float normalizevector(vector<float> v){\n",
        "\n",
        "    int i;\n",
        "    float total = 0.0;\n",
        "    for(i = 0; i < v.size(); i++){\n",
        "        total += (v[i] * v[i]);\n",
        "    }\n",
        "\n",
        "    return sqrt(total);\n",
        "}\n",
        "\n",
        "double edgeweight(vector<float> v1, vector<float> v2){\n",
        "\n",
        "    vector<float> v3;\n",
        "    vectorsubtraction(v1, v2, v3);\n",
        "\n",
        "    float normalized = normalizevector(v3);\n",
        "    double base = exp(normalized);\n",
        "    return (1 / base);\n",
        "}\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9-pXfRlJrCN"
      },
      "source": [
        "#Python code of barycentric clustering\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGKUs1tFJ49t"
      },
      "source": [
        "#from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TTNEw8sRGuy"
      },
      "source": [
        "#df_to_use_in_pca = df_to_use_in_pca.apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fj7KRFRUX_Iq"
      },
      "source": [
        "#knn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wu1PVXW_YCRA"
      },
      "source": [
        "> Step 1: Determine the value for K\n",
        "\n",
        "> Step 2: Calculate the distances between the new input (test data) and all the training data. The most commonly used metrics for calculating distance are Euclidean, Manhattan and Minkowski\n",
        "\n",
        "> Step 3: Sort the distance and determine k nearest neighbors based on minimum distance values\n",
        "\n",
        "> Step 4: Analyze the category of those neighbors and assign the category for the test data based on majority vote\n",
        "\n",
        "> Step 5: Return the predicted class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_h2Wa42Na26"
      },
      "source": [
        "#Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4Tc_CwuN_En",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "09089865-bb06-4aec-becd-af9537d7e203"
      },
      "source": [
        "dfn['recuperated']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-361-d889b65ec346>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdfn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'recuperated'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'dfn' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuiFP6oAJ7wP"
      },
      "source": [
        "#for i in df_to_use_in_pca:\n",
        "#  df_to_use_in_pca[i].astype(int)\n",
        "\n",
        "enc = OneHotEncoder(handle_unknown='ignore')\n",
        "\n",
        "enc_df = pd.DataFrame(\n",
        "    enc.fit_transform(\n",
        "        dfn['recuperated']).toarray())\n",
        "\n",
        "# converted = df_to_use_in_pca.to_numpy()\n",
        "# print(converted)\n",
        "# enc.fit(converted)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bklvczk6Zhzr"
      },
      "source": [
        "#Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQhgSbu0ZhY-"
      },
      "source": [
        "#from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUxgQHWEatZo"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KYe7xzKbZlfZ"
      },
      "source": [
        "df['case_location_encoded'] = LabelEncoder().fit_transform(df['case_location'])\n",
        "df[['case_location', 'case_location_encoded']] # special syntax to get just these two columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ruuo7h5zbh8w"
      },
      "source": [
        "df.head(200)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg97PUTgfC6t"
      },
      "source": [
        "print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7lX4VnXwKuC"
      },
      "source": [
        "Statistics # Statistics to develop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6xlAXJwUSLI"
      },
      "source": [
        "References:\n",
        "\n",
        "\n",
        "1.   [Categorical Imputer](https://feature-engine.readthedocs.io/en/latest/imputation/CategoricalImputer.html#categoricalimputer)\n",
        "2.   [another reference](https://)\n",
        "3.   another reference\n",
        "\n"
      ]
    }
  ]
}