notebook/Coronavirus_Korea_Distribution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Coronavirus-Korea-Distribution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9atowQsvbIe",
        "colab_type": "text"
      },
      "source": [
        "# Import Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQ5k695hvYCR",
        "colab_type": "code",
        "outputId": "5ba1903d-41d1-40b0-c822-71db28e8540b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "import os\n",
        "\n",
        "!git clone https://github.com/jihoo-kim/Coronavirus-Dataset\n",
        "PATIENT_PATH = \"/content/Coronavirus-Dataset/patient.csv\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Coronavirus-Dataset'...\n",
            "remote: Enumerating objects: 46, done.\u001b[K\n",
            "remote: Counting objects:   2% (1/46)\u001b[K\rremote: Counting objects:   4% (2/46)\u001b[K\rremote: Counting objects:   6% (3/46)\u001b[K\rremote: Counting objects:   8% (4/46)\u001b[K\rremote: Counting objects:  10% (5/46)\u001b[K\rremote: Counting objects:  13% (6/46)\u001b[K\rremote: Counting objects:  15% (7/46)\u001b[K\rremote: Counting objects:  17% (8/46)\u001b[K\rremote: Counting objects:  19% (9/46)\u001b[K\rremote: Counting objects:  21% (10/46)\u001b[K\rremote: Counting objects:  23% (11/46)\u001b[K\rremote: Counting objects:  26% (12/46)\u001b[K\rremote: Counting objects:  28% (13/46)\u001b[K\rremote: Counting objects:  30% (14/46)\u001b[K\rremote: Counting objects:  32% (15/46)\u001b[K\rremote: Counting objects:  34% (16/46)\u001b[K\rremote: Counting objects:  36% (17/46)\u001b[K\rremote: Counting objects:  39% (18/46)\u001b[K\rremote: Counting objects:  41% (19/46)\u001b[K\rremote: Counting objects:  43% (20/46)\u001b[K\rremote: Counting objects:  45% (21/46)\u001b[K\rremote: Counting objects:  47% (22/46)\u001b[K\rremote: Counting objects:  50% (23/46)\u001b[K\rremote: Counting objects:  52% (24/46)\u001b[K\rremote: Counting objects:  54% (25/46)\u001b[K\rremote: Counting objects:  56% (26/46)\u001b[K\rremote: Counting objects:  58% (27/46)\u001b[K\rremote: Counting objects:  60% (28/46)\u001b[K\rremote: Counting objects:  63% (29/46)\u001b[K\rremote: Counting objects:  65% (30/46)\u001b[K\rremote: Counting objects:  67% (31/46)\u001b[K\rremote: Counting objects:  69% (32/46)\u001b[K\rremote: Counting objects:  71% (33/46)\u001b[K\rremote: Counting objects:  73% (34/46)\u001b[K\rremote: Counting objects:  76% (35/46)\u001b[K\rremote: Counting objects:  78% (36/46)\u001b[K\rremote: Counting objects:  80% (37/46)\u001b[K\rremote: Counting objects:  82% (38/46)\u001b[K\rremote: Counting objects:  84% (39/46)\u001b[K\rremote: Counting objects:  86% (40/46)\u001b[K\rremote: Counting objects:  89% (41/46)\u001b[K\rremote: Counting objects:  91% (42/46)\u001b[K\rremote: Counting objects:  93% (43/46)\u001b[K\rremote: Counting objects:  95% (44/46)\u001b[K\rremote: Counting objects:  97% (45/46)\u001b[K\rremote: Counting objects: 100% (46/46)\u001b[K\rremote: Counting objects: 100% (46/46), done.\u001b[K\n",
            "remote: Compressing objects:   2% (1/46)\u001b[K\rremote: Compressing objects:   4% (2/46)\u001b[K\rremote: Compressing objects:   6% (3/46)\u001b[K\rremote: Compressing objects:   8% (4/46)\u001b[K\rremote: Compressing objects:  10% (5/46)\u001b[K\rremote: Compressing objects:  13% (6/46)\u001b[K\rremote: Compressing objects:  15% (7/46)\u001b[K\rremote: Compressing objects:  17% (8/46)\u001b[K\rremote: Compressing objects:  19% (9/46)\u001b[K\rremote: Compressing objects:  21% (10/46)\u001b[K\rremote: Compressing objects:  23% (11/46)\u001b[K\rremote: Compressing objects:  26% (12/46)\u001b[K\rremote: Compressing objects:  28% (13/46)\u001b[K\rremote: Compressing objects:  30% (14/46)\u001b[K\rremote: Compressing objects:  32% (15/46)\u001b[K\rremote: Compressing objects:  34% (16/46)\u001b[K\rremote: Compressing objects:  36% (17/46)\u001b[K\rremote: Compressing objects:  39% (18/46)\u001b[K\rremote: Compressing objects:  41% (19/46)\u001b[K\rremote: Compressing objects:  43% (20/46)\u001b[K\rremote: Compressing objects:  45% (21/46)\u001b[K\rremote: Compressing objects:  47% (22/46)\u001b[K\rremote: Compressing objects:  50% (23/46)\u001b[K\rremote: Compressing objects:  52% (24/46)\u001b[K\rremote: Compressing objects:  54% (25/46)\u001b[K\rremote: Compressing objects:  56% (26/46)\u001b[K\rremote: Compressing objects:  58% (27/46)\u001b[K\rremote: Compressing objects:  60% (28/46)\u001b[K\rremote: Compressing objects:  63% (29/46)\u001b[K\rremote: Compressing objects:  65% (30/46)\u001b[K\rremote: Compressing objects:  67% (31/46)\u001b[K\rremote: Compressing objects:  69% (32/46)\u001b[K\rremote: Compressing objects:  71% (33/46)\u001b[K\rremote: Compressing objects:  73% (34/46)\u001b[K\rremote: Compressing objects:  76% (35/46)\u001b[K\rremote: Compressing objects:  78% (36/46)\u001b[K\rremote: Compressing objects:  80% (37/46)\u001b[K\rremote: Compressing objects:  82% (38/46)\u001b[K\rremote: Compressing objects:  84% (39/46)\u001b[K\rremote: Compressing objects:  86% (40/46)\u001b[K\rremote: Compressing objects:  89% (41/46)\u001b[K\rremote: Compressing objects:  91% (42/46)\u001b[K\rremote: Compressing objects:  93% (43/46)\u001b[K\rremote: Compressing objects:  95% (44/46)\u001b[K\rremote: Compressing objects:  97% (45/46)\u001b[K\rremote: Compressing objects: 100% (46/46)\u001b[K\rremote: Compressing objects: 100% (46/46), done.\u001b[K\n",
            "Receiving objects:   0% (1/456)   \rReceiving objects:   1% (5/456)   \rReceiving objects:   2% (10/456)   \rReceiving objects:   3% (14/456)   \rReceiving objects:   4% (19/456)   \rReceiving objects:   5% (23/456)   \rReceiving objects:   6% (28/456)   \rReceiving objects:   7% (32/456)   \rReceiving objects:   8% (37/456)   \rReceiving objects:   9% (42/456)   \rReceiving objects:  10% (46/456)   \rReceiving objects:  11% (51/456)   \rReceiving objects:  12% (55/456)   \rReceiving objects:  13% (60/456)   \rReceiving objects:  14% (64/456)   \rReceiving objects:  15% (69/456)   \rReceiving objects:  16% (73/456)   \rReceiving objects:  17% (78/456)   \rReceiving objects:  18% (83/456)   \rReceiving objects:  19% (87/456)   \rReceiving objects:  20% (92/456)   \rReceiving objects:  21% (96/456)   \rReceiving objects:  22% (101/456)   \rReceiving objects:  23% (105/456)   \rReceiving objects:  24% (110/456)   \rReceiving objects:  25% (114/456)   \rReceiving objects:  26% (119/456)   \rReceiving objects:  27% (124/456)   \rReceiving objects:  28% (128/456)   \rReceiving objects:  29% (133/456)   \rReceiving objects:  30% (137/456)   \rReceiving objects:  31% (142/456)   \rReceiving objects:  32% (146/456)   \rReceiving objects:  33% (151/456)   \rReceiving objects:  34% (156/456)   \rReceiving objects:  35% (160/456)   \rReceiving objects:  36% (165/456)   \rReceiving objects:  37% (169/456)   \rReceiving objects:  38% (174/456)   \rReceiving objects:  39% (178/456)   \rReceiving objects:  40% (183/456)   \rReceiving objects:  41% (187/456)   \rReceiving objects:  42% (192/456)   \rReceiving objects:  43% (197/456)   \rReceiving objects:  44% (201/456)   \rReceiving objects:  45% (206/456)   \rReceiving objects:  46% (210/456)   \rReceiving objects:  47% (215/456)   \rReceiving objects:  48% (219/456)   \rReceiving objects:  49% (224/456)   \rReceiving objects:  50% (228/456)   \rReceiving objects:  51% (233/456)   \rReceiving objects:  52% (238/456)   \rReceiving objects:  53% (242/456)   \rReceiving objects:  54% (247/456)   \rReceiving objects:  55% (251/456)   \rReceiving objects:  56% (256/456)   \rReceiving objects:  57% (260/456)   \rReceiving objects:  58% (265/456)   \rReceiving objects:  59% (270/456)   \rReceiving objects:  60% (274/456)   \rReceiving objects:  61% (279/456)   \rReceiving objects:  62% (283/456)   \rReceiving objects:  63% (288/456)   \rReceiving objects:  64% (292/456)   \rReceiving objects:  65% (297/456)   \rReceiving objects:  66% (301/456)   \rReceiving objects:  67% (306/456)   \rReceiving objects:  68% (311/456)   \rReceiving objects:  69% (315/456)   \rReceiving objects:  70% (320/456)   \rReceiving objects:  71% (324/456)   \rReceiving objects:  72% (329/456)   \rremote: Total 456 (delta 22), reused 0 (delta 0), pack-reused 410\u001b[K\n",
            "Receiving objects:  73% (333/456)   \rReceiving objects:  74% (338/456)   \rReceiving objects:  75% (342/456)   \rReceiving objects:  76% (347/456)   \rReceiving objects:  77% (352/456)   \rReceiving objects:  78% (356/456)   \rReceiving objects:  79% (361/456)   \rReceiving objects:  80% (365/456)   \rReceiving objects:  81% (370/456)   \rReceiving objects:  82% (374/456)   \rReceiving objects:  83% (379/456)   \rReceiving objects:  84% (384/456)   \rReceiving objects:  85% (388/456)   \rReceiving objects:  86% (393/456)   \rReceiving objects:  87% (397/456)   \rReceiving objects:  88% (402/456)   \rReceiving objects:  89% (406/456)   \rReceiving objects:  90% (411/456)   \rReceiving objects:  91% (415/456)   \rReceiving objects:  92% (420/456)   \rReceiving objects:  93% (425/456)   \rReceiving objects:  94% (429/456)   \rReceiving objects:  95% (434/456)   \rReceiving objects:  96% (438/456)   \rReceiving objects:  97% (443/456)   \rReceiving objects:  98% (447/456)   \rReceiving objects:  99% (452/456)   \rReceiving objects: 100% (456/456)   \rReceiving objects: 100% (456/456), 246.06 KiB | 2.03 MiB/s, done.\n",
            "Resolving deltas:   0% (0/276)   \rResolving deltas:   5% (14/276)   \rResolving deltas:   6% (18/276)   \rResolving deltas:  36% (100/276)   \rResolving deltas:  38% (107/276)   \rResolving deltas:  41% (115/276)   \rResolving deltas:  44% (124/276)   \rResolving deltas:  46% (128/276)   \rResolving deltas:  48% (133/276)   \rResolving deltas:  72% (201/276)   \rResolving deltas:  73% (203/276)   \rResolving deltas:  74% (205/276)   \rResolving deltas:  81% (225/276)   \rResolving deltas:  85% (235/276)   \rResolving deltas:  87% (241/276)   \rResolving deltas:  89% (247/276)   \rResolving deltas:  90% (249/276)   \rResolving deltas: 100% (276/276)   \rResolving deltas: 100% (276/276), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0NXOMeUw7eF",
        "colab_type": "code",
        "outputId": "391c8187-2753-4469-d4f0-bb6cb5f91c5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "source": [
        "import os\n",
        "\n",
        "!git clone https://github.com/ClementBM/Experiment_Coronavius.git\n",
        "PYRAMID_PATH = \"/content/Experiment_Coronavius/data/population-pyramid-south-korea.csv\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Experiment_Coronavius'...\n",
            "remote: Enumerating objects: 37, done.\u001b[K\n",
            "remote: Counting objects:   2% (1/37)\u001b[K\rremote: Counting objects:   5% (2/37)\u001b[K\rremote: Counting objects:   8% (3/37)\u001b[K\rremote: Counting objects:  10% (4/37)\u001b[K\rremote: Counting objects:  13% (5/37)\u001b[K\rremote: Counting objects:  16% (6/37)\u001b[K\rremote: Counting objects:  18% (7/37)\u001b[K\rremote: Counting objects:  21% (8/37)\u001b[K\rremote: Counting objects:  24% (9/37)\u001b[K\rremote: Counting objects:  27% (10/37)\u001b[K\rremote: Counting objects:  29% (11/37)\u001b[K\rremote: Counting objects:  32% (12/37)\u001b[K\rremote: Counting objects:  35% (13/37)\u001b[K\rremote: Counting objects:  37% (14/37)\u001b[K\rremote: Counting objects:  40% (15/37)\u001b[K\rremote: Counting objects:  43% (16/37)\u001b[K\rremote: Counting objects:  45% (17/37)\u001b[K\rremote: Counting objects:  48% (18/37)\u001b[K\rremote: Counting objects:  51% (19/37)\u001b[K\rremote: Counting objects:  54% (20/37)\u001b[K\rremote: Counting objects:  56% (21/37)\u001b[K\rremote: Counting objects:  59% (22/37)\u001b[K\rremote: Counting objects:  62% (23/37)\u001b[K\rremote: Counting objects:  64% (24/37)\u001b[K\rremote: Counting objects:  67% (25/37)\u001b[K\rremote: Counting objects:  70% (26/37)\u001b[K\rremote: Counting objects:  72% (27/37)\u001b[K\rremote: Counting objects:  75% (28/37)\u001b[K\rremote: Counting objects:  78% (29/37)\u001b[K\rremote: Counting objects:  81% (30/37)\u001b[K\rremote: Counting objects:  83% (31/37)\u001b[K\rremote: Counting objects:  86% (32/37)\u001b[K\rremote: Counting objects:  89% (33/37)\u001b[K\rremote: Counting objects:  91% (34/37)\u001b[K\rremote: Counting objects:  94% (35/37)\u001b[K\rremote: Counting objects:  97% (36/37)\u001b[K\rremote: Counting objects: 100% (37/37)\u001b[K\rremote: Counting objects: 100% (37/37), done.\u001b[K\n",
            "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
            "remote: Total 37 (delta 12), reused 25 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (37/37), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOSYF6uqvZEE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df_korea_patients = pd.read_csv(PATIENT_PATH)\n",
        "df_korea_population_pyramid = pd.read_csv(PYRAMID_PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25Dz4zDcves2",
        "colab_type": "text"
      },
      "source": [
        "# EDA Patients"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYsDazRovgjI",
        "colab_type": "code",
        "outputId": "3a7531e6-a3d4-425e-a347-b05e9cb03d74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "df_korea_patients.head()\n",
        "display(df_korea_patients.head())\n",
        "display(df_korea_patients.shape)\n",
        "display(df_korea_patients.columns)\n",
        "display(df_korea_patients.iloc[:10,:10].dtypes)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<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>patient_id</th>\n",
              "      <th>sex</th>\n",
              "      <th>birth_year</th>\n",
              "      <th>country</th>\n",
              "      <th>region</th>\n",
              "      <th>disease</th>\n",
              "      <th>group</th>\n",
              "      <th>infection_reason</th>\n",
              "      <th>infection_order</th>\n",
              "      <th>infected_by</th>\n",
              "      <th>contact_number</th>\n",
              "      <th>confirmed_date</th>\n",
              "      <th>released_date</th>\n",
              "      <th>deceased_date</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>China</td>\n",
              "      <td>filtered at airport</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2020-01-20</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>filtered at airport</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>1966.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>male</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95.0</td>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>2020-02-09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>male</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   patient_id     sex  birth_year  ... released_date deceased_date     state\n",
              "0           1  female      1984.0  ...    2020-02-06           NaN  released\n",
              "1           2    male      1964.0  ...    2020-02-05           NaN  released\n",
              "2           3    male      1966.0  ...    2020-02-12           NaN  released\n",
              "3           4    male      1964.0  ...    2020-02-09           NaN  released\n",
              "4           5    male      1987.0  ...    2020-03-02           NaN  released\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(7754, 15)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['patient_id', 'sex', 'birth_year', 'country', 'region', 'disease',\n",
              "       'group', 'infection_reason', 'infection_order', 'infected_by',\n",
              "       'contact_number', 'confirmed_date', 'released_date', 'deceased_date',\n",
              "       'state'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "patient_id            int64\n",
              "sex                  object\n",
              "birth_year          float64\n",
              "country              object\n",
              "region               object\n",
              "disease             float64\n",
              "group                object\n",
              "infection_reason     object\n",
              "infection_order     float64\n",
              "infected_by          object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtGrVLuwxhz9",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7H-XsFgvhq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# drop sample if sex or birth_year is NaN\n",
        "not_nan = df_korea_patients['birth_year'].notna() & df_korea_patients['sex'].notna()\n",
        "df_korea_patients = df_korea_patients[not_nan]\n",
        "# typo\n",
        "df_korea_patients[\"sex\"] = df_korea_patients[\"sex\"].replace(\"feamle\", \"female\")\n",
        "\n",
        "df_korea_patients['age'] = 2020 - df_korea_patients['birth_year'] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vthGDEC9Kyf_",
        "colab_type": "code",
        "outputId": "130efb04-b74e-4d1b-a83e-b1e05fd555c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "df_korea_patients.head()\n",
        "display(df_korea_patients.head())\n",
        "display(df_korea_patients.shape)\n",
        "display(df_korea_patients.columns)\n",
        "display(df_korea_patients.iloc[:10,:10].dtypes)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "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>patient_id</th>\n",
              "      <th>sex</th>\n",
              "      <th>birth_year</th>\n",
              "      <th>country</th>\n",
              "      <th>region</th>\n",
              "      <th>disease</th>\n",
              "      <th>group</th>\n",
              "      <th>infection_reason</th>\n",
              "      <th>infection_order</th>\n",
              "      <th>infected_by</th>\n",
              "      <th>contact_number</th>\n",
              "      <th>confirmed_date</th>\n",
              "      <th>released_date</th>\n",
              "      <th>deceased_date</th>\n",
              "      <th>state</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>China</td>\n",
              "      <td>filtered at airport</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2020-01-20</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>filtered at airport</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>1966.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>male</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95.0</td>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>2020-02-09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>male</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>visit to Wuhan</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>released</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   patient_id     sex  birth_year  ... deceased_date     state   age\n",
              "0           1  female      1984.0  ...           NaN  released  36.0\n",
              "1           2    male      1964.0  ...           NaN  released  56.0\n",
              "2           3    male      1966.0  ...           NaN  released  54.0\n",
              "3           4    male      1964.0  ...           NaN  released  56.0\n",
              "4           5    male      1987.0  ...           NaN  released  33.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(710, 16)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['patient_id', 'sex', 'birth_year', 'country', 'region', 'disease',\n",
              "       'group', 'infection_reason', 'infection_order', 'infected_by',\n",
              "       'contact_number', 'confirmed_date', 'released_date', 'deceased_date',\n",
              "       'state', 'age'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "patient_id            int64\n",
              "sex                  object\n",
              "birth_year          float64\n",
              "country              object\n",
              "region               object\n",
              "disease             float64\n",
              "group                object\n",
              "infection_reason     object\n",
              "infection_order     float64\n",
              "infected_by          object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_mRjVDOJ1SH",
        "colab_type": "text"
      },
      "source": [
        "## Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwARObPGHs3f",
        "colab_type": "code",
        "outputId": "309748ca-bfb3-48fc-ce68-cf6c49c29469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(14,10))\n",
        "sns.violinplot(x=\"state\", y=\"age\", hue=\"sex\", data=df_korea_patients,\n",
        "               order=[\"deceased\", \"isolated\", \"released\"], \n",
        "               palette={\"female\": \"#d98b5f\", \n",
        "                        \"male\": \"#597dbf\"}, \n",
        "               split=True)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ROrdmSFVV/cIQEVHUYBkiIqKod+GBqwCnyRERUduwDBERUdS7cIocwDJERERtwzJERERR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p133nlauHAhU+8CR+Hz+XT99deroKBAa9askc+kCTVlXpclSYoEP1AksEtfWLBAZWWZURO8QcsQ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\nAAAgLxGGAAAAAOQlwhAAAACAvPT/AQYRArd/zCpMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s5MPLQ8B_lt",
        "colab_type": "code",
        "outputId": "d0430fc2-afb8-445f-eb1c-5652b72635c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        }
      },
      "source": [
        "df_korea_patients_by_age_sex = df_korea_patients.groupby([\"state\", \"sex\"], \n",
        "                                                         as_index=False)[\"state\", \"sex\"].size()\n",
        "\n",
        "df_korea_patients_by_age_sex.unstack().plot(kind='bar', \n",
        "                                            color=['#d98b5f', '#597dbf'],\n",
        "                                            figsize=(14, 10))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qAACAoYkaAABgaKIGAAAYmqgBAACGJmoAAIChiRoAAGBoogYAABiaqAEAAIYmagAAgKGJ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\nwwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3hmfOA5KMjD",
        "colab_type": "text"
      },
      "source": [
        "## Age pyramid"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LS3Wpu2nKMJZ",
        "colab_type": "code",
        "outputId": "a73979f2-4c3d-4347-cb65-14b1cbbe9574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "df_korea_population_pyramid.plot(kind='barh',\n",
        "                                 x=\"age_range\",\n",
        "                                 color=['#597dbf', '#d98b5f'],\n",
        "                                 figsize=(14, 10))\n",
        "plt.xlabel(\"Population\")\n",
        "plt.ylabel(\"Age range\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DkyRJkiS1MDRJkiRJUgtDkyRJkiS1MDRJkiRJUgtDkyRJkiS1MDRJkiRJUgtD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\n9v5oQQfJWAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZFaf6znxr10",
        "colab_type": "text"
      },
      "source": [
        "### Adding age range "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpIBnsA6xore",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def group_age(age, window):\n",
        "  if age > 99:\n",
        "    return \"100+\"\n",
        "  if age % 5 != 0:\n",
        "      lower = int(math.floor(age / float(window))) * window\n",
        "      upper = int(math.ceil(age / float(window))) * window - 1\n",
        "      return f\"{lower}-{upper}\"\n",
        "  else:\n",
        "      lower = int(age)\n",
        "      upper = int(age + window - 1) \n",
        "      return f\"{lower}-{upper}\"\n",
        "\n",
        "def group_age_by_5(age):\n",
        "  return group_age(age, 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0M-ROFFxqTB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_korea_patients[\"age_range\"] = df_korea_patients[\"age\"].apply(group_age_by_5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBRFHtDhxnqf",
        "colab_type": "text"
      },
      "source": [
        "### Age range dictionary for ordering values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1rveQSBxnMa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "age_range_order = df_korea_population_pyramid[\"age_range\"].to_dict()\n",
        "age_range_order = {v: k for k, v in age_range_order.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMQVL-UXCyLH",
        "colab_type": "text"
      },
      "source": [
        "### Age pyramid proportion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpkTf3f9xkd6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_male = sum(df_korea_population_pyramid.loc[:,'male'])\n",
        "total_female = sum(df_korea_population_pyramid.loc[:,'female'])\n",
        "\n",
        "df_korea_population_pyramid[\"male_prop\"] = df_korea_population_pyramid[\"male\"] / total_male\n",
        "df_korea_population_pyramid[\"female_prop\"] = df_korea_population_pyramid[\"female\"] / total_female"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4D5YMuXjDEfV",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0sIgnuMC4e2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def infected_population_normed(df):\n",
        "  result = df.groupby([\"age_range\", \"sex\"], as_index=False)[\"age_range\", \"sex\"].size()\n",
        "  \n",
        "  result = (\n",
        "    pd.DataFrame(result)\n",
        "    .pivot_table(index=[\"age_range\"], columns=[\"sex\"], fill_value=0.0)\n",
        "    .reset_index()\n",
        "  )\n",
        "\n",
        "  result = result.set_index(\"age_range\")\n",
        "  \n",
        "  result = result[0] # unstack() ?\n",
        "  result[\"order\"] = [age_range_order[x] for x in result.index]\n",
        "  result = result.sort_values(by=\"order\")\n",
        "  \n",
        "  indices = [age_range_order[x] for x in result.index]\n",
        "  df_normed = pd.DataFrame(index=df_korea_population_pyramid[\"age_range\"], columns=[\"male\",\"female\"])\n",
        "\n",
        "  df_normed.loc[result.index, \"male\"] = result[\"male\"] / df_korea_population_pyramid.loc[indices][\"male_prop\"].values\n",
        "  df_normed.loc[result.index, \"female\"] = result[\"female\"] / df_korea_population_pyramid.loc[indices][\"female_prop\"].values\n",
        "\n",
        "  df_normed = df_normed.replace(np.inf, 0)\n",
        "  df_normed = df_normed.replace(np.nan, 0)\n",
        "\n",
        "  df_normed[\"male\"] = df_normed[\"male\"] * 100 / sum(df_normed[\"male\"])\n",
        "  df_normed[\"female\"] = df_normed[\"female\"] * 100 / sum(df_normed[\"female\"])\n",
        "\n",
        "  distribution_normed = pd.DataFrame({'male': df_normed[\"male\"], 'female': df_normed[\"female\"]})\n",
        "  distribution_normed = distribution_normed.reset_index()\n",
        "\n",
        "  return distribution_normed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TctcCon6QD9a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_normed_distribution(df):\n",
        "  distribution_normed = infected_population_normed(df)\n",
        "\n",
        "  df = pd.DataFrame({'male': distribution_normed['male'].values, 'female': distribution_normed['female'].values}, index=distribution_normed[\"age_range\"])\n",
        "  df.plot.barh(figsize=(14, 10), color=['#597dbf', '#d98b5f'])\n",
        "\n",
        "  plt.xlabel(\"Proportion %\")\n",
        "  plt.ylabel(\"Age range\")\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsPvfkF3POLs",
        "colab_type": "text"
      },
      "source": [
        "## Normed distribution of all"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtR8UxS_EFAp",
        "colab_type": "code",
        "outputId": "615b6155-5c94-481e-b26d-2b92b37b0cac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "source": [
        "display(df_korea_patients.shape[0])\n",
        "plot_normed_distribution(df_korea_patients)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "710"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJEmS1KnWjSCaZXB/XlVnAj8DThvn/V9YVWuT7ANcl+Se4SerqpLUGLVn0QtV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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBMKgWTHbVS1",
        "colab_type": "code",
        "outputId": "604889ca-b52c-479f-edc1-85116e10ff20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        }
      },
      "source": [
        "display(df_korea_patients[df_korea_patients[\"age_range\"] == \"100+\"])\n",
        "display(df_korea_population_pyramid[df_korea_population_pyramid[\"age_range\"] == \"100+\"])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "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>patient_id</th>\n",
              "      <th>sex</th>\n",
              "      <th>birth_year</th>\n",
              "      <th>country</th>\n",
              "      <th>region</th>\n",
              "      <th>disease</th>\n",
              "      <th>group</th>\n",
              "      <th>infection_reason</th>\n",
              "      <th>infection_order</th>\n",
              "      <th>infected_by</th>\n",
              "      <th>contact_number</th>\n",
              "      <th>confirmed_date</th>\n",
              "      <th>released_date</th>\n",
              "      <th>deceased_date</th>\n",
              "      <th>state</th>\n",
              "      <th>age</th>\n",
              "      <th>age_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5659</th>\n",
              "      <td>5661</td>\n",
              "      <td>female</td>\n",
              "      <td>1905.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>contact with patient</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>isolated</td>\n",
              "      <td>115.0</td>\n",
              "      <td>100+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6068</th>\n",
              "      <td>6070</td>\n",
              "      <td>male</td>\n",
              "      <td>1903.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>isolated</td>\n",
              "      <td>117.0</td>\n",
              "      <td>100+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6843</th>\n",
              "      <td>6845</td>\n",
              "      <td>female</td>\n",
              "      <td>1913.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>contact with patient</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>isolated</td>\n",
              "      <td>107.0</td>\n",
              "      <td>100+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>7219</td>\n",
              "      <td>female</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>Korea</td>\n",
              "      <td>capital area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>contact with patient</td>\n",
              "      <td>7103.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>isolated</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100+</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_id     sex  birth_year  ...     state    age  age_range\n",
              "5659        5661  female      1905.0  ...  isolated  115.0       100+\n",
              "6068        6070    male      1903.0  ...  isolated  117.0       100+\n",
              "6843        6845  female      1913.0  ...  isolated  107.0       100+\n",
              "7217        7219  female      1920.0  ...  isolated  100.0       100+\n",
              "\n",
              "[4 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "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_range</th>\n",
              "      <th>male</th>\n",
              "      <th>female</th>\n",
              "      <th>male_prop</th>\n",
              "      <th>female_prop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>100+</td>\n",
              "      <td>517</td>\n",
              "      <td>3350</td>\n",
              "      <td>0.00002</td>\n",
              "      <td>0.000131</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age_range  male  female  male_prop  female_prop\n",
              "20      100+   517    3350    0.00002     0.000131"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRu53ZxNbZHw",
        "colab_type": "code",
        "outputId": "1e197672-d8ff-4111-9fa9-5928f5f2cbf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "source": [
        "display(df_korea_patients.shape[0])\n",
        "plot_normed_distribution(df_korea_patients[df_korea_patients[\"age_range\"] != \"100+\"])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "710"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JktTC0CRJkiRJLQxNkiRJktTC0CRJkiRJLQxNkiRJktTC0CRJkiRJLQxNkiRJ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\nJEmSpBYmTZIkSZLU4v8AmIg4KnMKK8sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTpkrW-VPVQM",
        "colab_type": "text"
      },
      "source": [
        "## Normed proportion of **deceased**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9O93QQvaaovi",
        "colab_type": "code",
        "outputId": "248f3fa5-a00f-4de5-8e54-bcc30c0d7982",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "source": [
        "df_deceased = df_korea_patients[df_korea_patients[\"state\"] == \"deceased\"]\n",
        "display(df_deceased.shape[0])\n",
        "plot_normed_distribution(df_deceased)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N2GlJljRjC5K8fIjaQc9LsmWSc5vrLkwyvct7kCRJkrRpG4nH8w6tql8OGDuj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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoxbBKdnPfU2",
        "colab_type": "text"
      },
      "source": [
        "## Normed proportion of **released**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5O2u_kFPotq",
        "colab_type": "code",
        "outputId": "17c29673-bf9d-496a-e695-c9e1f2d32e3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "source": [
        "df_released = df_korea_patients[df_korea_patients[\"state\"] == \"released\"]\n",
        "display(df_released.shape[0])\n",
        "plot_normed_distribution(df_released)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E4ICquqm51peAlwNU1cNVdVxVTQc+0oyt7P/jVTWvqnqqqmfixIkd3qYkSZKkjdm4Dq/9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\nD5HZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1XaR6WJGSN5",
        "colab_type": "text"
      },
      "source": [
        "## Normed proportion of **isolated**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmXapceYGWDK",
        "colab_type": "code",
        "outputId": "025d81d7-7502-4f95-aee1-8367167c60ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        }
      },
      "source": [
        "df_released = df_korea_patients[(df_korea_patients[\"state\"] == \"isolated\") & (df_korea_patients[\"age_range\"] != \"100+\")]\n",
        "display(df_released.shape[0])\n",
        "plot_normed_distribution(df_released)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "615"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJElqYWiSJEmSpBaGJkmSJElqYWiSJEmSpBZjRrqB4bBq+WIWzZk50m1IkrTZ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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}