{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Covid-19-Data-Exploration.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyODWzVdwiPqT33ss7JyNHvW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aiformankind/covid-19-hackathon/blob/master/Covid_19_Data_Exploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvCZpv2VODDd",
        "colab_type": "text"
      },
      "source": [
        "Covid-19 Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZApSQ53eMEj",
        "colab_type": "code",
        "outputId": "c7569840-8f0f-4597-95eb-fb13e4f9ef83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.25.3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEZzJw2-Z7N9",
        "colab_type": "text"
      },
      "source": [
        "Since elderly population is a high risk group, we compute the population distribution of people > 60 year old by counties in California.\n",
        "\n",
        "We also would like to examine how many ICU beds availble by counties in California and determine how many percent of elderly population can afford an ICU bed before they have to start sharing one."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MA85ovJiW-7A",
        "colab_type": "text"
      },
      "source": [
        "To do the study, we found a dataset from California Health and Human Service Open Data Portal which shows the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. Data updated: March 10, 2020. You can find the data from below link.\n",
        "\n",
        "https://data.chhs.ca.gov/dataset/healthcare-facility-bed-types-and-counts/resource/0997fa8e-ef7c-43f2-8b9a-94672935fa60\n",
        "\n",
        "Disclaimer: Since we do not know the accuray of the data, we assumes no responsibility for errors or omissions. Please use this study as your own risk. AI For Mankind shall not be held liable for any use or misuse of the data. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obgpdY-gCcXu",
        "colab_type": "code",
        "outputId": "4f95a749-104b-47e4-947b-5755737fa370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "hospital_beds_df = pd.read_csv('https://raw.githubusercontent.com/aiformankind/covid-19-hackathon/master/HEALTH_FAC_BEDS_20200310.csv', skiprows=0, thousands=',')\n",
        "hospital_beds_df.head(10)"
      ],
      "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>FACID</th>\n",
              "      <th>FACNAME</th>\n",
              "      <th>FAC_FDR</th>\n",
              "      <th>BED_CAPACITY_TYPE</th>\n",
              "      <th>BED_CAPACITY</th>\n",
              "      <th>COUNTY_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000001</td>\n",
              "      <td>VINEYARD POST ACUTE</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>99</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000003</td>\n",
              "      <td>CREEKSIDE REHABILITATION &amp; BEHAVIORAL HEALTH</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SPECIAL TREATMENT PROGRAM</td>\n",
              "      <td>58</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10000003</td>\n",
              "      <td>CREEKSIDE REHABILITATION &amp; BEHAVIORAL HEALTH</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>123</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10000004</td>\n",
              "      <td>CRESCENT CITY SKILLED NURSING</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>99</td>\n",
              "      <td>DEL NORTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10000005</td>\n",
              "      <td>WINDSOR CARE CENTER OF PETALUMA</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>79</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10000022</td>\n",
              "      <td>FRIENDS HOUSE</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>34</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10000024</td>\n",
              "      <td>GRANADA REHAB &amp; WELLNESS CENTER, LP</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>87</td>\n",
              "      <td>HUMBOLDT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10000026</td>\n",
              "      <td>APPLE VALLEY POST-ACUTE REHAB</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>95</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10000028</td>\n",
              "      <td>EMPRES POST ACUTE REHABILITATION</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>98</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10000029</td>\n",
              "      <td>HEALDSBURG SENIOR LIVING COMMUNITY</td>\n",
              "      <td>SKILLED NURSING FACILITY</td>\n",
              "      <td>SKILLED NURSING</td>\n",
              "      <td>38</td>\n",
              "      <td>SONOMA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      FACID  ... COUNTY_NAME\n",
              "0  10000001  ...      SONOMA\n",
              "1  10000003  ...      SONOMA\n",
              "2  10000003  ...      SONOMA\n",
              "3  10000004  ...   DEL NORTE\n",
              "4  10000005  ...      SONOMA\n",
              "5  10000022  ...      SONOMA\n",
              "6  10000024  ...    HUMBOLDT\n",
              "7  10000026  ...      SONOMA\n",
              "8  10000028  ...      SONOMA\n",
              "9  10000029  ...      SONOMA\n",
              "\n",
              "[10 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-V-zBQIab-8",
        "colab_type": "text"
      },
      "source": [
        "Show Different Bed Types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MRrjTM-xLGaz",
        "colab_type": "code",
        "outputId": "697b9fd9-4c97-4d3a-8384-0e89e74a33c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "hospital_beds_df['BED_CAPACITY_TYPE'].unique()\n"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SKILLED NURSING', 'SPECIAL TREATMENT PROGRAM',\n",
              "       'INTERMEDIATE CARE/DD HABILITATIVE',\n",
              "       'INTERMEDIATE CARE/DD NURSING',\n",
              "       'CONGREGATE LIVING HEALTH FACILITY', 'INTERMEDIATE CARE',\n",
              "       'HOSPICE', 'CORONARY CARE', 'INTENSIVE CARE',\n",
              "       'INTENSIVE CARE NEWBORN NURSERY', 'PERINATAL',\n",
              "       'UNSPECIFIED GENERAL ACUTE CARE', 'BURN', 'PEDIATRIC',\n",
              "       'RENAL TRANSPLANT', 'REHABILITATION', 'ACUTE RESPIRATORY CARE',\n",
              "       'ACUTE PSYCHIATRIC CARE', 'CHEMICAL DEPENDENCY RECOVERY',\n",
              "       'PEDIATRIC INTENSIVE CARE UNIT', 'LABOR AND DELIVERY',\n",
              "       'INTERMEDIATE CARE/DD', 'PSYCHIATRIC HEALTH',\n",
              "       'PEDI. DAY & RESPITE CARE', 'DIALYSIS STATIONS',\n",
              "       'CORRECTIONAL TREATMENT CENTER'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_L2QCiOCZNHb",
        "colab_type": "text"
      },
      "source": [
        "Select Intensive Care or Acute Respiratory Care beds"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGWnmBSsLuVW",
        "colab_type": "code",
        "outputId": "b996348e-7290-41c6-abbb-4fe4fbd25820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "icu_beds_df = hospital_beds_df[(hospital_beds_df.BED_CAPACITY_TYPE=='INTENSIVE CARE') | (hospital_beds_df.BED_CAPACITY_TYPE=='ACUTE RESPIRATORY CARE') ]\n",
        "icu_beds_df"
      ],
      "execution_count": 146,
      "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>FACID</th>\n",
              "      <th>FACNAME</th>\n",
              "      <th>FAC_FDR</th>\n",
              "      <th>BED_CAPACITY_TYPE</th>\n",
              "      <th>BED_CAPACITY</th>\n",
              "      <th>COUNTY_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>30000037</td>\n",
              "      <td>METHODIST HOSPITAL OF SACRAMENTO</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>10</td>\n",
              "      <td>SACRAMENTO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>30000108</td>\n",
              "      <td>SUTTER AMADOR HOSPITAL</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>6</td>\n",
              "      <td>AMADOR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>30000109</td>\n",
              "      <td>SUTTER AUBURN FAITH HOSPITAL</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>4</td>\n",
              "      <td>PLACER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>30000113</td>\n",
              "      <td>UNIVERSITY OF CALIFORNIA DAVIS MEDICAL CENTER</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>116</td>\n",
              "      <td>SACRAMENTO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>30000114</td>\n",
              "      <td>BARTON MEMORIAL HOSPITAL</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>8</td>\n",
              "      <td>EL DORADO</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4137</th>\n",
              "      <td>930000290</td>\n",
              "      <td>KAISER FOUNDATION HOSPITAL - WOODLAND HILLS</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>22</td>\n",
              "      <td>LOS ANGELES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4198</th>\n",
              "      <td>930000912</td>\n",
              "      <td>KECK HOSPITAL OF USC</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>84</td>\n",
              "      <td>LOS ANGELES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4223</th>\n",
              "      <td>930001543</td>\n",
              "      <td>KAISER FOUNDATION HOSPITAL - BALDWIN PARK</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>12</td>\n",
              "      <td>LOS ANGELES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4240</th>\n",
              "      <td>930001607</td>\n",
              "      <td>KINDRED HOSPITAL - SANTA ANA</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>7</td>\n",
              "      <td>ORANGE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4265</th>\n",
              "      <td>930001709</td>\n",
              "      <td>MEMORIALCARE MILLER CHILDREN'S &amp; WOMEN'S HOSPI...</td>\n",
              "      <td>GENERAL ACUTE CARE HOSPITAL</td>\n",
              "      <td>INTENSIVE CARE</td>\n",
              "      <td>30</td>\n",
              "      <td>LOS ANGELES</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>353 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          FACID  ...  COUNTY_NAME\n",
              "191    30000037  ...   SACRAMENTO\n",
              "212    30000108  ...       AMADOR\n",
              "216    30000109  ...       PLACER\n",
              "220    30000113  ...   SACRAMENTO\n",
              "227    30000114  ...    EL DORADO\n",
              "...         ...  ...          ...\n",
              "4137  930000290  ...  LOS ANGELES\n",
              "4198  930000912  ...  LOS ANGELES\n",
              "4223  930001543  ...  LOS ANGELES\n",
              "4240  930001607  ...       ORANGE\n",
              "4265  930001709  ...  LOS ANGELES\n",
              "\n",
              "[353 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP6XVvSIZchv",
        "colab_type": "text"
      },
      "source": [
        "Group ICU Beds by County\n",
        "\n",
        "As the numbers show below: we only have limited ICU beds. For Santa Clara County, we only have 438 ICU beds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tzq8vq5SCoHU",
        "colab_type": "code",
        "outputId": "908eb440-748a-482c-87ba-f32b44a4569f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "icu_beds_grouped_by_county = icu_beds_df.groupby('COUNTY_NAME')['BED_CAPACITY'].sum()\n",
        "sorted_icu_beds_grouped_by_county = icu_beds_grouped_by_county.sort_values(ascending=False)\n",
        "sorted_icu_beds_grouped_by_county.index = sorted_icu_beds_grouped_by_county.index.str.lower()\n",
        "sorted_icu_beds_grouped_by_county.index = (sorted_icu_beds_grouped_by_county.index +' county').astype(str)\n",
        "sorted_icu_beds_grouped_by_county"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COUNTY_NAME\n",
              "los angeles county        2145\n",
              "orange county              614\n",
              "san diego county           605\n",
              "san bernardino county      486\n",
              "santa clara county         438\n",
              "riverside county           378\n",
              "sacramento county          376\n",
              "san francisco county       326\n",
              "alameda county             291\n",
              "contra costa county        169\n",
              "kern county                155\n",
              "fresno county              149\n",
              "ventura county             123\n",
              "san mateo county            96\n",
              "stanislaus county           92\n",
              "san joaquin county          90\n",
              "solano county               82\n",
              "santa barbara county        79\n",
              "sonoma county               72\n",
              "shasta county               70\n",
              "tulare county               65\n",
              "placer county               58\n",
              "madera county               52\n",
              "napa county                 48\n",
              "san luis obispo county      48\n",
              "butte county                47\n",
              "marin county                30\n",
              "monterey county             30\n",
              "el dorado county            28\n",
              "imperial county             28\n",
              "humboldt county             26\n",
              "merced county               24\n",
              "yuba county                 24\n",
              "kings county                22\n",
              "mendocino county            16\n",
              "yolo county                 14\n",
              "santa cruz county           13\n",
              "lake county                  8\n",
              "nevada county                8\n",
              "tehama county                8\n",
              "calaveras county             8\n",
              "siskiyou county              8\n",
              "tuolumne county              6\n",
              "del norte county             6\n",
              "colusa county                6\n",
              "amador county                6\n",
              "inyo county                  4\n",
              "san benito county            4\n",
              "mono county                  2\n",
              "Name: BED_CAPACITY, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOHyNzxhfh55",
        "colab_type": "text"
      },
      "source": [
        "Plot of Number of ICU Beds across Counties in California"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82NBYtROfTA6",
        "colab_type": "code",
        "outputId": "26576ac7-c701-45f2-80df-203cbda89f05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,7))\n",
        "sorted_icu_beds_grouped_by_county.iloc[0:15].plot.bar(ax=ax)\n",
        "ax.set_xlabel('Counties in California', fontsize=14)\n",
        "ax.set_title('Number of ICU Beds Across Counties in California')\n",
        "ax.set_ylabel('Number of ICU Beds', fontsize=14)\n",
        "plt.xticks(fontsize=14, rotation=90)\n",
        "plt.yticks(fontsize=14)"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   0.,  500., 1000., 1500., 2000., 2500.]),\n",
              " <a list of 6 Text yticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PTk7W5s2b9emnn9ZhdQAAAADg3Ortpa0AAAAAgJpRL4Nk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\nIiIiIiKiVvk/5okFdqRONnYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwXi5elqOPK_",
        "colab_type": "text"
      },
      "source": [
        "Since elderly population is a high risk group, we compute the population distribution of people > 60 year old in California. This will help us in emergency planning.\n",
        "\n",
        "We found a population projection dataset from California's Department of Finance. http://www.dof.ca.gov/Forecasting/Demographics/Projections/\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNAgPHDKeUKu",
        "colab_type": "code",
        "outputId": "53a967da-2b13-47d8-c399-db73fe08981e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "county_age_df = pd.read_csv('https://raw.githubusercontent.com/aiformankind/covid-19-hackathon/master/County_Age_Projection.csv', skiprows=2, thousands=',')\n",
        "county_age_df.head(10)"
      ],
      "execution_count": 149,
      "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>County</th>\n",
              "      <th>Age</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>2023</th>\n",
              "      <th>2024</th>\n",
              "      <th>2025</th>\n",
              "      <th>2026</th>\n",
              "      <th>2027</th>\n",
              "      <th>2028</th>\n",
              "      <th>2029</th>\n",
              "      <th>2030</th>\n",
              "      <th>2031</th>\n",
              "      <th>2032</th>\n",
              "      <th>2033</th>\n",
              "      <th>2034</th>\n",
              "      <th>2035</th>\n",
              "      <th>2036</th>\n",
              "      <th>2037</th>\n",
              "      <th>2038</th>\n",
              "      <th>2039</th>\n",
              "      <th>2040</th>\n",
              "      <th>2041</th>\n",
              "      <th>2042</th>\n",
              "      <th>2043</th>\n",
              "      <th>2044</th>\n",
              "      <th>2045</th>\n",
              "      <th>2046</th>\n",
              "      <th>2047</th>\n",
              "      <th>...</th>\n",
              "      <th>Column163</th>\n",
              "      <th>Column164</th>\n",
              "      <th>Column165</th>\n",
              "      <th>Column166</th>\n",
              "      <th>Column167</th>\n",
              "      <th>Column168</th>\n",
              "      <th>Column169</th>\n",
              "      <th>Column170</th>\n",
              "      <th>Column171</th>\n",
              "      <th>Column172</th>\n",
              "      <th>Column173</th>\n",
              "      <th>Column174</th>\n",
              "      <th>Column175</th>\n",
              "      <th>Column176</th>\n",
              "      <th>Column177</th>\n",
              "      <th>Column178</th>\n",
              "      <th>Column179</th>\n",
              "      <th>Column180</th>\n",
              "      <th>Column181</th>\n",
              "      <th>Column182</th>\n",
              "      <th>Column183</th>\n",
              "      <th>Column184</th>\n",
              "      <th>Column185</th>\n",
              "      <th>Column186</th>\n",
              "      <th>Column187</th>\n",
              "      <th>Column188</th>\n",
              "      <th>Column189</th>\n",
              "      <th>Column190</th>\n",
              "      <th>Column191</th>\n",
              "      <th>Column192</th>\n",
              "      <th>Column193</th>\n",
              "      <th>Column194</th>\n",
              "      <th>Column195</th>\n",
              "      <th>Column196</th>\n",
              "      <th>Column197</th>\n",
              "      <th>Column198</th>\n",
              "      <th>Column199</th>\n",
              "      <th>Column200</th>\n",
              "      <th>Column201</th>\n",
              "      <th>Column202</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>0</td>\n",
              "      <td>19169.0</td>\n",
              "      <td>19413.0</td>\n",
              "      <td>19093.0</td>\n",
              "      <td>19398.0</td>\n",
              "      <td>19250.0</td>\n",
              "      <td>19290.0</td>\n",
              "      <td>19332.0</td>\n",
              "      <td>19182.0</td>\n",
              "      <td>18680.0</td>\n",
              "      <td>18878.0</td>\n",
              "      <td>18309.0</td>\n",
              "      <td>17939.0</td>\n",
              "      <td>17708.0</td>\n",
              "      <td>17488.0</td>\n",
              "      <td>17402.0</td>\n",
              "      <td>17344.0</td>\n",
              "      <td>17341.0</td>\n",
              "      <td>17358.0</td>\n",
              "      <td>17325.0</td>\n",
              "      <td>17234.0</td>\n",
              "      <td>17203.0</td>\n",
              "      <td>17218.0</td>\n",
              "      <td>17340.0</td>\n",
              "      <td>17338.0</td>\n",
              "      <td>17365.0</td>\n",
              "      <td>17538.0</td>\n",
              "      <td>17668.0</td>\n",
              "      <td>17854.0</td>\n",
              "      <td>18057.0</td>\n",
              "      <td>18289.0</td>\n",
              "      <td>18517.0</td>\n",
              "      <td>18617.0</td>\n",
              "      <td>18799.0</td>\n",
              "      <td>19002.0</td>\n",
              "      <td>19112.0</td>\n",
              "      <td>19084.0</td>\n",
              "      <td>18957.0</td>\n",
              "      <td>18848.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>1</td>\n",
              "      <td>19387.0</td>\n",
              "      <td>19157.0</td>\n",
              "      <td>19400.0</td>\n",
              "      <td>19092.0</td>\n",
              "      <td>19395.0</td>\n",
              "      <td>19229.0</td>\n",
              "      <td>19287.0</td>\n",
              "      <td>19331.0</td>\n",
              "      <td>19192.0</td>\n",
              "      <td>18680.0</td>\n",
              "      <td>18849.0</td>\n",
              "      <td>18285.0</td>\n",
              "      <td>17899.0</td>\n",
              "      <td>17682.0</td>\n",
              "      <td>17465.0</td>\n",
              "      <td>17386.0</td>\n",
              "      <td>17331.0</td>\n",
              "      <td>17325.0</td>\n",
              "      <td>17343.0</td>\n",
              "      <td>17305.0</td>\n",
              "      <td>17223.0</td>\n",
              "      <td>17177.0</td>\n",
              "      <td>17200.0</td>\n",
              "      <td>17330.0</td>\n",
              "      <td>17328.0</td>\n",
              "      <td>17366.0</td>\n",
              "      <td>17534.0</td>\n",
              "      <td>17652.0</td>\n",
              "      <td>17840.0</td>\n",
              "      <td>18042.0</td>\n",
              "      <td>18288.0</td>\n",
              "      <td>18517.0</td>\n",
              "      <td>18610.0</td>\n",
              "      <td>18791.0</td>\n",
              "      <td>19001.0</td>\n",
              "      <td>19111.0</td>\n",
              "      <td>19089.0</td>\n",
              "      <td>18956.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>2</td>\n",
              "      <td>20156.0</td>\n",
              "      <td>19098.0</td>\n",
              "      <td>18955.0</td>\n",
              "      <td>19247.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>19098.0</td>\n",
              "      <td>18663.0</td>\n",
              "      <td>18658.0</td>\n",
              "      <td>18823.0</td>\n",
              "      <td>18727.0</td>\n",
              "      <td>18384.0</td>\n",
              "      <td>18594.0</td>\n",
              "      <td>18297.0</td>\n",
              "      <td>17922.0</td>\n",
              "      <td>17643.0</td>\n",
              "      <td>17334.0</td>\n",
              "      <td>17129.0</td>\n",
              "      <td>17083.0</td>\n",
              "      <td>16916.0</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>16799.0</td>\n",
              "      <td>16726.0</td>\n",
              "      <td>16680.0</td>\n",
              "      <td>16642.0</td>\n",
              "      <td>16746.0</td>\n",
              "      <td>16763.0</td>\n",
              "      <td>16763.0</td>\n",
              "      <td>16890.0</td>\n",
              "      <td>17076.0</td>\n",
              "      <td>17302.0</td>\n",
              "      <td>17402.0</td>\n",
              "      <td>17697.0</td>\n",
              "      <td>17951.0</td>\n",
              "      <td>18064.0</td>\n",
              "      <td>18228.0</td>\n",
              "      <td>18436.0</td>\n",
              "      <td>18458.0</td>\n",
              "      <td>18469.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>3</td>\n",
              "      <td>19614.0</td>\n",
              "      <td>20756.0</td>\n",
              "      <td>19791.0</td>\n",
              "      <td>19702.0</td>\n",
              "      <td>20129.0</td>\n",
              "      <td>19653.0</td>\n",
              "      <td>19770.0</td>\n",
              "      <td>19199.0</td>\n",
              "      <td>19221.0</td>\n",
              "      <td>19363.0</td>\n",
              "      <td>19202.0</td>\n",
              "      <td>18901.0</td>\n",
              "      <td>19021.0</td>\n",
              "      <td>18845.0</td>\n",
              "      <td>18476.0</td>\n",
              "      <td>18215.0</td>\n",
              "      <td>17888.0</td>\n",
              "      <td>17687.0</td>\n",
              "      <td>17689.0</td>\n",
              "      <td>17485.0</td>\n",
              "      <td>17297.0</td>\n",
              "      <td>17304.0</td>\n",
              "      <td>17295.0</td>\n",
              "      <td>17207.0</td>\n",
              "      <td>17140.0</td>\n",
              "      <td>17251.0</td>\n",
              "      <td>17321.0</td>\n",
              "      <td>17369.0</td>\n",
              "      <td>17404.0</td>\n",
              "      <td>17629.0</td>\n",
              "      <td>17909.0</td>\n",
              "      <td>17873.0</td>\n",
              "      <td>18253.0</td>\n",
              "      <td>18474.0</td>\n",
              "      <td>18584.0</td>\n",
              "      <td>18790.0</td>\n",
              "      <td>18998.0</td>\n",
              "      <td>19001.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>4</td>\n",
              "      <td>19589.0</td>\n",
              "      <td>19669.0</td>\n",
              "      <td>20873.0</td>\n",
              "      <td>19961.0</td>\n",
              "      <td>19844.0</td>\n",
              "      <td>20323.0</td>\n",
              "      <td>19911.0</td>\n",
              "      <td>20086.0</td>\n",
              "      <td>19315.0</td>\n",
              "      <td>19308.0</td>\n",
              "      <td>19465.0</td>\n",
              "      <td>19295.0</td>\n",
              "      <td>19015.0</td>\n",
              "      <td>19062.0</td>\n",
              "      <td>18974.0</td>\n",
              "      <td>18621.0</td>\n",
              "      <td>18354.0</td>\n",
              "      <td>18105.0</td>\n",
              "      <td>17927.0</td>\n",
              "      <td>17924.0</td>\n",
              "      <td>17794.0</td>\n",
              "      <td>17573.0</td>\n",
              "      <td>17512.0</td>\n",
              "      <td>17673.0</td>\n",
              "      <td>17622.0</td>\n",
              "      <td>17543.0</td>\n",
              "      <td>17535.0</td>\n",
              "      <td>17585.0</td>\n",
              "      <td>17720.0</td>\n",
              "      <td>17732.0</td>\n",
              "      <td>18015.0</td>\n",
              "      <td>18273.0</td>\n",
              "      <td>18219.0</td>\n",
              "      <td>18638.0</td>\n",
              "      <td>18822.0</td>\n",
              "      <td>18937.0</td>\n",
              "      <td>19148.0</td>\n",
              "      <td>19392.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>5</td>\n",
              "      <td>19225.0</td>\n",
              "      <td>19634.0</td>\n",
              "      <td>20018.0</td>\n",
              "      <td>21054.0</td>\n",
              "      <td>20145.0</td>\n",
              "      <td>20036.0</td>\n",
              "      <td>20519.0</td>\n",
              "      <td>20065.0</td>\n",
              "      <td>20250.0</td>\n",
              "      <td>19546.0</td>\n",
              "      <td>19492.0</td>\n",
              "      <td>19637.0</td>\n",
              "      <td>19333.0</td>\n",
              "      <td>19051.0</td>\n",
              "      <td>19136.0</td>\n",
              "      <td>19168.0</td>\n",
              "      <td>18944.0</td>\n",
              "      <td>18576.0</td>\n",
              "      <td>18413.0</td>\n",
              "      <td>18444.0</td>\n",
              "      <td>18327.0</td>\n",
              "      <td>18249.0</td>\n",
              "      <td>18031.0</td>\n",
              "      <td>17902.0</td>\n",
              "      <td>18083.0</td>\n",
              "      <td>18014.0</td>\n",
              "      <td>18039.0</td>\n",
              "      <td>18045.0</td>\n",
              "      <td>18034.0</td>\n",
              "      <td>18120.0</td>\n",
              "      <td>18128.0</td>\n",
              "      <td>18527.0</td>\n",
              "      <td>18710.0</td>\n",
              "      <td>18636.0</td>\n",
              "      <td>19121.0</td>\n",
              "      <td>19264.0</td>\n",
              "      <td>19466.0</td>\n",
              "      <td>19638.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>6</td>\n",
              "      <td>19002.0</td>\n",
              "      <td>18691.0</td>\n",
              "      <td>19309.0</td>\n",
              "      <td>19654.0</td>\n",
              "      <td>20682.0</td>\n",
              "      <td>19707.0</td>\n",
              "      <td>19617.0</td>\n",
              "      <td>20287.0</td>\n",
              "      <td>19834.0</td>\n",
              "      <td>19706.0</td>\n",
              "      <td>18970.0</td>\n",
              "      <td>18849.0</td>\n",
              "      <td>19027.0</td>\n",
              "      <td>18759.0</td>\n",
              "      <td>18441.0</td>\n",
              "      <td>18563.0</td>\n",
              "      <td>18745.0</td>\n",
              "      <td>18541.0</td>\n",
              "      <td>18149.0</td>\n",
              "      <td>18134.0</td>\n",
              "      <td>18307.0</td>\n",
              "      <td>18129.0</td>\n",
              "      <td>17983.0</td>\n",
              "      <td>17711.0</td>\n",
              "      <td>17456.0</td>\n",
              "      <td>17729.0</td>\n",
              "      <td>17624.0</td>\n",
              "      <td>17579.0</td>\n",
              "      <td>17616.0</td>\n",
              "      <td>17628.0</td>\n",
              "      <td>17575.0</td>\n",
              "      <td>17683.0</td>\n",
              "      <td>18096.0</td>\n",
              "      <td>18264.0</td>\n",
              "      <td>18239.0</td>\n",
              "      <td>18661.0</td>\n",
              "      <td>18799.0</td>\n",
              "      <td>19035.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>7</td>\n",
              "      <td>19002.0</td>\n",
              "      <td>18945.0</td>\n",
              "      <td>18869.0</td>\n",
              "      <td>19514.0</td>\n",
              "      <td>19794.0</td>\n",
              "      <td>20793.0</td>\n",
              "      <td>19814.0</td>\n",
              "      <td>19698.0</td>\n",
              "      <td>20437.0</td>\n",
              "      <td>19967.0</td>\n",
              "      <td>19889.0</td>\n",
              "      <td>19155.0</td>\n",
              "      <td>18985.0</td>\n",
              "      <td>19202.0</td>\n",
              "      <td>18945.0</td>\n",
              "      <td>18639.0</td>\n",
              "      <td>18697.0</td>\n",
              "      <td>18889.0</td>\n",
              "      <td>18738.0</td>\n",
              "      <td>18245.0</td>\n",
              "      <td>18353.0</td>\n",
              "      <td>18612.0</td>\n",
              "      <td>18304.0</td>\n",
              "      <td>18252.0</td>\n",
              "      <td>17963.0</td>\n",
              "      <td>17694.0</td>\n",
              "      <td>17913.0</td>\n",
              "      <td>17786.0</td>\n",
              "      <td>17693.0</td>\n",
              "      <td>17762.0</td>\n",
              "      <td>17733.0</td>\n",
              "      <td>17704.0</td>\n",
              "      <td>17825.0</td>\n",
              "      <td>18228.0</td>\n",
              "      <td>18346.0</td>\n",
              "      <td>18389.0</td>\n",
              "      <td>18820.0</td>\n",
              "      <td>18938.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>8</td>\n",
              "      <td>18578.0</td>\n",
              "      <td>19031.0</td>\n",
              "      <td>19001.0</td>\n",
              "      <td>19011.0</td>\n",
              "      <td>19630.0</td>\n",
              "      <td>19886.0</td>\n",
              "      <td>20885.0</td>\n",
              "      <td>19991.0</td>\n",
              "      <td>19839.0</td>\n",
              "      <td>20537.0</td>\n",
              "      <td>20087.0</td>\n",
              "      <td>19961.0</td>\n",
              "      <td>19270.0</td>\n",
              "      <td>19078.0</td>\n",
              "      <td>19291.0</td>\n",
              "      <td>19034.0</td>\n",
              "      <td>18692.0</td>\n",
              "      <td>18858.0</td>\n",
              "      <td>18974.0</td>\n",
              "      <td>18911.0</td>\n",
              "      <td>18427.0</td>\n",
              "      <td>18573.0</td>\n",
              "      <td>18808.0</td>\n",
              "      <td>18525.0</td>\n",
              "      <td>18463.0</td>\n",
              "      <td>18143.0</td>\n",
              "      <td>17798.0</td>\n",
              "      <td>18124.0</td>\n",
              "      <td>17901.0</td>\n",
              "      <td>17836.0</td>\n",
              "      <td>17895.0</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>17843.0</td>\n",
              "      <td>17903.0</td>\n",
              "      <td>18294.0</td>\n",
              "      <td>18469.0</td>\n",
              "      <td>18448.0</td>\n",
              "      <td>18869.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>9</td>\n",
              "      <td>18738.0</td>\n",
              "      <td>18532.0</td>\n",
              "      <td>19156.0</td>\n",
              "      <td>19162.0</td>\n",
              "      <td>19176.0</td>\n",
              "      <td>19715.0</td>\n",
              "      <td>19968.0</td>\n",
              "      <td>20869.0</td>\n",
              "      <td>20030.0</td>\n",
              "      <td>20021.0</td>\n",
              "      <td>20775.0</td>\n",
              "      <td>20274.0</td>\n",
              "      <td>20194.0</td>\n",
              "      <td>19398.0</td>\n",
              "      <td>19276.0</td>\n",
              "      <td>19407.0</td>\n",
              "      <td>19096.0</td>\n",
              "      <td>18696.0</td>\n",
              "      <td>18900.0</td>\n",
              "      <td>18914.0</td>\n",
              "      <td>18866.0</td>\n",
              "      <td>18428.0</td>\n",
              "      <td>18655.0</td>\n",
              "      <td>18898.0</td>\n",
              "      <td>18620.0</td>\n",
              "      <td>18553.0</td>\n",
              "      <td>18228.0</td>\n",
              "      <td>17842.0</td>\n",
              "      <td>18156.0</td>\n",
              "      <td>17995.0</td>\n",
              "      <td>17888.0</td>\n",
              "      <td>17872.0</td>\n",
              "      <td>17803.0</td>\n",
              "      <td>17868.0</td>\n",
              "      <td>17925.0</td>\n",
              "      <td>18290.0</td>\n",
              "      <td>18445.0</td>\n",
              "      <td>18431.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 255 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           County Age     2010  ...  Column200  Column201  Column202\n",
              "0  Alameda County   0  19169.0  ...        NaN        NaN        NaN\n",
              "1  Alameda County   1  19387.0  ...        NaN        NaN        NaN\n",
              "2  Alameda County   2  20156.0  ...        NaN        NaN        NaN\n",
              "3  Alameda County   3  19614.0  ...        NaN        NaN        NaN\n",
              "4  Alameda County   4  19589.0  ...        NaN        NaN        NaN\n",
              "5  Alameda County   5  19225.0  ...        NaN        NaN        NaN\n",
              "6  Alameda County   6  19002.0  ...        NaN        NaN        NaN\n",
              "7  Alameda County   7  19002.0  ...        NaN        NaN        NaN\n",
              "8  Alameda County   8  18578.0  ...        NaN        NaN        NaN\n",
              "9  Alameda County   9  18738.0  ...        NaN        NaN        NaN\n",
              "\n",
              "[10 rows x 255 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlNI-fWwiOuc",
        "colab_type": "code",
        "outputId": "e66d0bc3-9141-4ed1-c021-e66f34015080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "import pandas as pd\n",
        "county_age_df = county_age_df.replace({'Age': '100+'}, '100')\n",
        "pd.to_numeric(county_age_df['Age'])\n",
        "pd.to_numeric(county_age_df['2020'])\n",
        "county_age_df.head(202)"
      ],
      "execution_count": 150,
      "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>County</th>\n",
              "      <th>Age</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>2023</th>\n",
              "      <th>2024</th>\n",
              "      <th>2025</th>\n",
              "      <th>2026</th>\n",
              "      <th>2027</th>\n",
              "      <th>2028</th>\n",
              "      <th>2029</th>\n",
              "      <th>2030</th>\n",
              "      <th>2031</th>\n",
              "      <th>2032</th>\n",
              "      <th>2033</th>\n",
              "      <th>2034</th>\n",
              "      <th>2035</th>\n",
              "      <th>2036</th>\n",
              "      <th>2037</th>\n",
              "      <th>2038</th>\n",
              "      <th>2039</th>\n",
              "      <th>2040</th>\n",
              "      <th>2041</th>\n",
              "      <th>2042</th>\n",
              "      <th>2043</th>\n",
              "      <th>2044</th>\n",
              "      <th>2045</th>\n",
              "      <th>2046</th>\n",
              "      <th>2047</th>\n",
              "      <th>...</th>\n",
              "      <th>Column163</th>\n",
              "      <th>Column164</th>\n",
              "      <th>Column165</th>\n",
              "      <th>Column166</th>\n",
              "      <th>Column167</th>\n",
              "      <th>Column168</th>\n",
              "      <th>Column169</th>\n",
              "      <th>Column170</th>\n",
              "      <th>Column171</th>\n",
              "      <th>Column172</th>\n",
              "      <th>Column173</th>\n",
              "      <th>Column174</th>\n",
              "      <th>Column175</th>\n",
              "      <th>Column176</th>\n",
              "      <th>Column177</th>\n",
              "      <th>Column178</th>\n",
              "      <th>Column179</th>\n",
              "      <th>Column180</th>\n",
              "      <th>Column181</th>\n",
              "      <th>Column182</th>\n",
              "      <th>Column183</th>\n",
              "      <th>Column184</th>\n",
              "      <th>Column185</th>\n",
              "      <th>Column186</th>\n",
              "      <th>Column187</th>\n",
              "      <th>Column188</th>\n",
              "      <th>Column189</th>\n",
              "      <th>Column190</th>\n",
              "      <th>Column191</th>\n",
              "      <th>Column192</th>\n",
              "      <th>Column193</th>\n",
              "      <th>Column194</th>\n",
              "      <th>Column195</th>\n",
              "      <th>Column196</th>\n",
              "      <th>Column197</th>\n",
              "      <th>Column198</th>\n",
              "      <th>Column199</th>\n",
              "      <th>Column200</th>\n",
              "      <th>Column201</th>\n",
              "      <th>Column202</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>0</td>\n",
              "      <td>19169.0</td>\n",
              "      <td>19413.0</td>\n",
              "      <td>19093.0</td>\n",
              "      <td>19398.0</td>\n",
              "      <td>19250.0</td>\n",
              "      <td>19290.0</td>\n",
              "      <td>19332.0</td>\n",
              "      <td>19182.0</td>\n",
              "      <td>18680.0</td>\n",
              "      <td>18878.0</td>\n",
              "      <td>18309.0</td>\n",
              "      <td>17939.0</td>\n",
              "      <td>17708.0</td>\n",
              "      <td>17488.0</td>\n",
              "      <td>17402.0</td>\n",
              "      <td>17344.0</td>\n",
              "      <td>17341.0</td>\n",
              "      <td>17358.0</td>\n",
              "      <td>17325.0</td>\n",
              "      <td>17234.0</td>\n",
              "      <td>17203.0</td>\n",
              "      <td>17218.0</td>\n",
              "      <td>17340.0</td>\n",
              "      <td>17338.0</td>\n",
              "      <td>17365.0</td>\n",
              "      <td>17538.0</td>\n",
              "      <td>17668.0</td>\n",
              "      <td>17854.0</td>\n",
              "      <td>18057.0</td>\n",
              "      <td>18289.0</td>\n",
              "      <td>18517.0</td>\n",
              "      <td>18617.0</td>\n",
              "      <td>18799.0</td>\n",
              "      <td>19002.0</td>\n",
              "      <td>19112.0</td>\n",
              "      <td>19084.0</td>\n",
              "      <td>18957.0</td>\n",
              "      <td>18848.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>1</td>\n",
              "      <td>19387.0</td>\n",
              "      <td>19157.0</td>\n",
              "      <td>19400.0</td>\n",
              "      <td>19092.0</td>\n",
              "      <td>19395.0</td>\n",
              "      <td>19229.0</td>\n",
              "      <td>19287.0</td>\n",
              "      <td>19331.0</td>\n",
              "      <td>19192.0</td>\n",
              "      <td>18680.0</td>\n",
              "      <td>18849.0</td>\n",
              "      <td>18285.0</td>\n",
              "      <td>17899.0</td>\n",
              "      <td>17682.0</td>\n",
              "      <td>17465.0</td>\n",
              "      <td>17386.0</td>\n",
              "      <td>17331.0</td>\n",
              "      <td>17325.0</td>\n",
              "      <td>17343.0</td>\n",
              "      <td>17305.0</td>\n",
              "      <td>17223.0</td>\n",
              "      <td>17177.0</td>\n",
              "      <td>17200.0</td>\n",
              "      <td>17330.0</td>\n",
              "      <td>17328.0</td>\n",
              "      <td>17366.0</td>\n",
              "      <td>17534.0</td>\n",
              "      <td>17652.0</td>\n",
              "      <td>17840.0</td>\n",
              "      <td>18042.0</td>\n",
              "      <td>18288.0</td>\n",
              "      <td>18517.0</td>\n",
              "      <td>18610.0</td>\n",
              "      <td>18791.0</td>\n",
              "      <td>19001.0</td>\n",
              "      <td>19111.0</td>\n",
              "      <td>19089.0</td>\n",
              "      <td>18956.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>2</td>\n",
              "      <td>20156.0</td>\n",
              "      <td>19098.0</td>\n",
              "      <td>18955.0</td>\n",
              "      <td>19247.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>19098.0</td>\n",
              "      <td>18663.0</td>\n",
              "      <td>18658.0</td>\n",
              "      <td>18823.0</td>\n",
              "      <td>18727.0</td>\n",
              "      <td>18384.0</td>\n",
              "      <td>18594.0</td>\n",
              "      <td>18297.0</td>\n",
              "      <td>17922.0</td>\n",
              "      <td>17643.0</td>\n",
              "      <td>17334.0</td>\n",
              "      <td>17129.0</td>\n",
              "      <td>17083.0</td>\n",
              "      <td>16916.0</td>\n",
              "      <td>16765.0</td>\n",
              "      <td>16799.0</td>\n",
              "      <td>16726.0</td>\n",
              "      <td>16680.0</td>\n",
              "      <td>16642.0</td>\n",
              "      <td>16746.0</td>\n",
              "      <td>16763.0</td>\n",
              "      <td>16763.0</td>\n",
              "      <td>16890.0</td>\n",
              "      <td>17076.0</td>\n",
              "      <td>17302.0</td>\n",
              "      <td>17402.0</td>\n",
              "      <td>17697.0</td>\n",
              "      <td>17951.0</td>\n",
              "      <td>18064.0</td>\n",
              "      <td>18228.0</td>\n",
              "      <td>18436.0</td>\n",
              "      <td>18458.0</td>\n",
              "      <td>18469.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>3</td>\n",
              "      <td>19614.0</td>\n",
              "      <td>20756.0</td>\n",
              "      <td>19791.0</td>\n",
              "      <td>19702.0</td>\n",
              "      <td>20129.0</td>\n",
              "      <td>19653.0</td>\n",
              "      <td>19770.0</td>\n",
              "      <td>19199.0</td>\n",
              "      <td>19221.0</td>\n",
              "      <td>19363.0</td>\n",
              "      <td>19202.0</td>\n",
              "      <td>18901.0</td>\n",
              "      <td>19021.0</td>\n",
              "      <td>18845.0</td>\n",
              "      <td>18476.0</td>\n",
              "      <td>18215.0</td>\n",
              "      <td>17888.0</td>\n",
              "      <td>17687.0</td>\n",
              "      <td>17689.0</td>\n",
              "      <td>17485.0</td>\n",
              "      <td>17297.0</td>\n",
              "      <td>17304.0</td>\n",
              "      <td>17295.0</td>\n",
              "      <td>17207.0</td>\n",
              "      <td>17140.0</td>\n",
              "      <td>17251.0</td>\n",
              "      <td>17321.0</td>\n",
              "      <td>17369.0</td>\n",
              "      <td>17404.0</td>\n",
              "      <td>17629.0</td>\n",
              "      <td>17909.0</td>\n",
              "      <td>17873.0</td>\n",
              "      <td>18253.0</td>\n",
              "      <td>18474.0</td>\n",
              "      <td>18584.0</td>\n",
              "      <td>18790.0</td>\n",
              "      <td>18998.0</td>\n",
              "      <td>19001.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alameda County</td>\n",
              "      <td>4</td>\n",
              "      <td>19589.0</td>\n",
              "      <td>19669.0</td>\n",
              "      <td>20873.0</td>\n",
              "      <td>19961.0</td>\n",
              "      <td>19844.0</td>\n",
              "      <td>20323.0</td>\n",
              "      <td>19911.0</td>\n",
              "      <td>20086.0</td>\n",
              "      <td>19315.0</td>\n",
              "      <td>19308.0</td>\n",
              "      <td>19465.0</td>\n",
              "      <td>19295.0</td>\n",
              "      <td>19015.0</td>\n",
              "      <td>19062.0</td>\n",
              "      <td>18974.0</td>\n",
              "      <td>18621.0</td>\n",
              "      <td>18354.0</td>\n",
              "      <td>18105.0</td>\n",
              "      <td>17927.0</td>\n",
              "      <td>17924.0</td>\n",
              "      <td>17794.0</td>\n",
              "      <td>17573.0</td>\n",
              "      <td>17512.0</td>\n",
              "      <td>17673.0</td>\n",
              "      <td>17622.0</td>\n",
              "      <td>17543.0</td>\n",
              "      <td>17535.0</td>\n",
              "      <td>17585.0</td>\n",
              "      <td>17720.0</td>\n",
              "      <td>17732.0</td>\n",
              "      <td>18015.0</td>\n",
              "      <td>18273.0</td>\n",
              "      <td>18219.0</td>\n",
              "      <td>18638.0</td>\n",
              "      <td>18822.0</td>\n",
              "      <td>18937.0</td>\n",
              "      <td>19148.0</td>\n",
              "      <td>19392.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>Alpine County</td>\n",
              "      <td>96</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>2.0</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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>Alpine County</td>\n",
              "      <td>97</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>2.0</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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Alpine County</td>\n",
              "      <td>98</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>2.0</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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Alpine County</td>\n",
              "      <td>99</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>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>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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Alpine County</td>\n",
              "      <td>100</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>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>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>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>202 rows × 255 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             County  Age     2010  ...  Column200  Column201  Column202\n",
              "0    Alameda County    0  19169.0  ...        NaN        NaN        NaN\n",
              "1    Alameda County    1  19387.0  ...        NaN        NaN        NaN\n",
              "2    Alameda County    2  20156.0  ...        NaN        NaN        NaN\n",
              "3    Alameda County    3  19614.0  ...        NaN        NaN        NaN\n",
              "4    Alameda County    4  19589.0  ...        NaN        NaN        NaN\n",
              "..              ...  ...      ...  ...        ...        ...        ...\n",
              "197   Alpine County   96      0.0  ...        NaN        NaN        NaN\n",
              "198   Alpine County   97      0.0  ...        NaN        NaN        NaN\n",
              "199   Alpine County   98      0.0  ...        NaN        NaN        NaN\n",
              "200   Alpine County   99      0.0  ...        NaN        NaN        NaN\n",
              "201   Alpine County  100      0.0  ...        NaN        NaN        NaN\n",
              "\n",
              "[202 rows x 255 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJIhMbGfhkV9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "county_age_df['Age']=pd.to_numeric(county_age_df['Age'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dWJj3DIxiH4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "greater_than_sixty = county_age_df[county_age_df['Age'] >= 60]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nV_3YlL6x2TS",
        "colab_type": "code",
        "outputId": "cc8ce69f-4e9a-40c9-a394-f1dcae656645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "greater_than_sixty[greater_than_sixty['County'].str.match('Alpine County')]['2020'].sum()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "418.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhBPSWNimyE-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grouped_by_county = county_age_df[county_age_df['Age'] >= 60][['County','Age','2020']].groupby('County')['2020'].sum()\n",
        "sorted_grouped_by_county = grouped_by_county.sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrBJVu7vPGN7",
        "colab_type": "text"
      },
      "source": [
        "Distribution Plot of Population >= 60 year old by Counties in California"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwjRrAZp2ys0",
        "colab_type": "code",
        "outputId": "5eb131e8-c54d-454b-a2b2-ad884f368d3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,7))\n",
        "sorted_grouped_by_county.iloc[0:15].plot.bar(ax=ax)"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f19dc460f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5OnLkiGbMmBHYXqNGDY0dO9ZiZd5wvT+Xzz3J7eP32Wef6dNPP9WhQ4eKfbKR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\nhJBG/T8vVt7ThQACZQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjPT7XHcxCpQ",
        "colab_type": "code",
        "outputId": "78ffdfd3-556f-4bd2-fac1-cc216b591763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sorted_grouped_by_county"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "County\n",
              "Los Angeles County        2152960.0\n",
              "Orange County              717395.0\n",
              "San Diego County           688677.0\n",
              "Riverside County           525411.0\n",
              "Santa Clara County         417912.0\n",
              "San Bernardino County      392687.0\n",
              "Alameda County             377472.0\n",
              "Sacramento County          327733.0\n",
              "Contra Costa County        285941.0\n",
              "San Francisco County       205190.0\n",
              "San Mateo County           197665.0\n",
              "Ventura County             197639.0\n",
              "Fresno County              182237.0\n",
              "Kern County                156139.0\n",
              "San Joaquin County         148223.0\n",
              "Sonoma County              141367.0\n",
              "Placer County              107029.0\n",
              "Stanislaus County          104865.0\n",
              "Solano County              104407.0\n",
              "Santa Barbara County        96753.0\n",
              "Monterey County             87896.0\n",
              "Marin County                86143.0\n",
              "Tulare County               82689.0\n",
              "San Luis Obispo County      76867.0\n",
              "Santa Cruz County           67628.0\n",
              "El Dorado County            61438.0\n",
              "Butte County                57513.0\n",
              "Shasta County               48541.0\n",
              "Merced County               46804.0\n",
              "Yolo County                 40207.0\n",
              "Nevada County               36661.0\n",
              "Imperial County             36547.0\n",
              "Napa County                 36326.0\n",
              "Humboldt County             34673.0\n",
              "Madera County               32477.0\n",
              "Mendocino County            26010.0\n",
              "Kings County                23677.0\n",
              "Sutter County               22564.0\n",
              "Lake County                 19845.0\n",
              "Tuolumne County             19047.0\n",
              "Calaveras County            17413.0\n",
              "Tehama County               17078.0\n",
              "Siskiyou County             15133.0\n",
              "Yuba County                 14552.0\n",
              "San Benito County           13594.0\n",
              "Amador County               13393.0\n",
              "Plumas County                7465.0\n",
              "Mariposa County              6965.0\n",
              "Del Norte County             6855.0\n",
              "Lassen County                6438.0\n",
              "Glenn County                 6353.0\n",
              "Inyo County                  6068.0\n",
              "Trinity County               5162.0\n",
              "Colusa County                4619.0\n",
              "Mono County                  3591.0\n",
              "Modoc County                 3265.0\n",
              "Sierra County                1332.0\n",
              "Alpine County                 418.0\n",
              "Name: 2020, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbbqFY7DxY4Q",
        "colab_type": "text"
      },
      "source": [
        "We saved and uploaded the above distribution of elderly population age 60 or more across counties in CA in the repo. Checkout https://github.com/aiformankind/covid-19-hackathon/blob/master/elderly_population_by_counties_ca.csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx5uDGZCEolH",
        "colab_type": "code",
        "outputId": "7ff66aeb-ad1b-46ad-cb81-8256d235027c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "sorted_icu_beds_grouped_by_county"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "COUNTY_NAME\n",
              "los angeles county        2145\n",
              "orange county              614\n",
              "san diego county           605\n",
              "san bernardino county      486\n",
              "santa clara county         438\n",
              "riverside county           378\n",
              "sacramento county          376\n",
              "san francisco county       326\n",
              "alameda county             291\n",
              "contra costa county        169\n",
              "kern county                155\n",
              "fresno county              149\n",
              "ventura county             123\n",
              "san mateo county            96\n",
              "stanislaus county           92\n",
              "san joaquin county          90\n",
              "solano county               82\n",
              "santa barbara county        79\n",
              "sonoma county               72\n",
              "shasta county               70\n",
              "tulare county               65\n",
              "placer county               58\n",
              "madera county               52\n",
              "napa county                 48\n",
              "san luis obispo county      48\n",
              "butte county                47\n",
              "marin county                30\n",
              "monterey county             30\n",
              "el dorado county            28\n",
              "imperial county             28\n",
              "humboldt county             26\n",
              "merced county               24\n",
              "yuba county                 24\n",
              "kings county                22\n",
              "mendocino county            16\n",
              "yolo county                 14\n",
              "santa cruz county           13\n",
              "lake county                  8\n",
              "nevada county                8\n",
              "tehama county                8\n",
              "calaveras county             8\n",
              "siskiyou county              8\n",
              "tuolumne county              6\n",
              "del norte county             6\n",
              "colusa county                6\n",
              "amador county                6\n",
              "inyo county                  4\n",
              "san benito county            4\n",
              "mono county                  2\n",
              "Name: BED_CAPACITY, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhrCEKqOGLP_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sorted_grouped_by_county_lowercase = sorted_grouped_by_county"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwQQVBJFFi8M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sorted_grouped_by_county_lowercase.index  = sorted_grouped_by_county.index.str.lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXjyl-7UDS4T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "beds_for_elderly_population = pd.merge(sorted_grouped_by_county_lowercase, sorted_icu_beds_grouped_by_county, left_index=True, right_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuWSknGWPBDB",
        "colab_type": "code",
        "outputId": "5fc6991a-92d0-43f8-880c-205d8a2f72db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "beds_for_elderly_population = beds_for_elderly_population.rename(columns={\"2020\": \"elderly_population\", \"BED_CAPACITY\": \"num_beds\"})\n",
        "beds_for_elderly_population"
      ],
      "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>elderly_population</th>\n",
              "      <th>num_beds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>los angeles county</th>\n",
              "      <td>2152960.0</td>\n",
              "      <td>2145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orange county</th>\n",
              "      <td>717395.0</td>\n",
              "      <td>614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san diego county</th>\n",
              "      <td>688677.0</td>\n",
              "      <td>605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riverside county</th>\n",
              "      <td>525411.0</td>\n",
              "      <td>378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa clara county</th>\n",
              "      <td>417912.0</td>\n",
              "      <td>438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san bernardino county</th>\n",
              "      <td>392687.0</td>\n",
              "      <td>486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alameda county</th>\n",
              "      <td>377472.0</td>\n",
              "      <td>291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sacramento county</th>\n",
              "      <td>327733.0</td>\n",
              "      <td>376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contra costa county</th>\n",
              "      <td>285941.0</td>\n",
              "      <td>169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san francisco county</th>\n",
              "      <td>205190.0</td>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san mateo county</th>\n",
              "      <td>197665.0</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventura county</th>\n",
              "      <td>197639.0</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fresno county</th>\n",
              "      <td>182237.0</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kern county</th>\n",
              "      <td>156139.0</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san joaquin county</th>\n",
              "      <td>148223.0</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sonoma county</th>\n",
              "      <td>141367.0</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>placer county</th>\n",
              "      <td>107029.0</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stanislaus county</th>\n",
              "      <td>104865.0</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>solano county</th>\n",
              "      <td>104407.0</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa barbara county</th>\n",
              "      <td>96753.0</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>monterey county</th>\n",
              "      <td>87896.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marin county</th>\n",
              "      <td>86143.0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tulare county</th>\n",
              "      <td>82689.0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san luis obispo county</th>\n",
              "      <td>76867.0</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa cruz county</th>\n",
              "      <td>67628.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>el dorado county</th>\n",
              "      <td>61438.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>butte county</th>\n",
              "      <td>57513.0</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shasta county</th>\n",
              "      <td>48541.0</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>merced county</th>\n",
              "      <td>46804.0</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yolo county</th>\n",
              "      <td>40207.0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nevada county</th>\n",
              "      <td>36661.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imperial county</th>\n",
              "      <td>36547.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>napa county</th>\n",
              "      <td>36326.0</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>humboldt county</th>\n",
              "      <td>34673.0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>madera county</th>\n",
              "      <td>32477.0</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mendocino county</th>\n",
              "      <td>26010.0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kings county</th>\n",
              "      <td>23677.0</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lake county</th>\n",
              "      <td>19845.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tuolumne county</th>\n",
              "      <td>19047.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calaveras county</th>\n",
              "      <td>17413.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tehama county</th>\n",
              "      <td>17078.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>siskiyou county</th>\n",
              "      <td>15133.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuba county</th>\n",
              "      <td>14552.0</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san benito county</th>\n",
              "      <td>13594.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amador county</th>\n",
              "      <td>13393.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>del norte county</th>\n",
              "      <td>6855.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inyo county</th>\n",
              "      <td>6068.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colusa county</th>\n",
              "      <td>4619.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mono county</th>\n",
              "      <td>3591.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        elderly_population  num_beds\n",
              "los angeles county               2152960.0      2145\n",
              "orange county                     717395.0       614\n",
              "san diego county                  688677.0       605\n",
              "riverside county                  525411.0       378\n",
              "santa clara county                417912.0       438\n",
              "san bernardino county             392687.0       486\n",
              "alameda county                    377472.0       291\n",
              "sacramento county                 327733.0       376\n",
              "contra costa county               285941.0       169\n",
              "san francisco county              205190.0       326\n",
              "san mateo county                  197665.0        96\n",
              "ventura county                    197639.0       123\n",
              "fresno county                     182237.0       149\n",
              "kern county                       156139.0       155\n",
              "san joaquin county                148223.0        90\n",
              "sonoma county                     141367.0        72\n",
              "placer county                     107029.0        58\n",
              "stanislaus county                 104865.0        92\n",
              "solano county                     104407.0        82\n",
              "santa barbara county               96753.0        79\n",
              "monterey county                    87896.0        30\n",
              "marin county                       86143.0        30\n",
              "tulare county                      82689.0        65\n",
              "san luis obispo county             76867.0        48\n",
              "santa cruz county                  67628.0        13\n",
              "el dorado county                   61438.0        28\n",
              "butte county                       57513.0        47\n",
              "shasta county                      48541.0        70\n",
              "merced county                      46804.0        24\n",
              "yolo county                        40207.0        14\n",
              "nevada county                      36661.0         8\n",
              "imperial county                    36547.0        28\n",
              "napa county                        36326.0        48\n",
              "humboldt county                    34673.0        26\n",
              "madera county                      32477.0        52\n",
              "mendocino county                   26010.0        16\n",
              "kings county                       23677.0        22\n",
              "lake county                        19845.0         8\n",
              "tuolumne county                    19047.0         6\n",
              "calaveras county                   17413.0         8\n",
              "tehama county                      17078.0         8\n",
              "siskiyou county                    15133.0         8\n",
              "yuba county                        14552.0        24\n",
              "san benito county                  13594.0         4\n",
              "amador county                      13393.0         6\n",
              "del norte county                    6855.0         6\n",
              "inyo county                         6068.0         4\n",
              "colusa county                       4619.0         6\n",
              "mono county                         3591.0         2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMyVRLSpbXlF",
        "colab_type": "text"
      },
      "source": [
        "This shows the max percent of elderly population who can find an available ICU bed. This is the threshold before they have to start sharing ICU beds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AncT7xV1DnD3",
        "colab_type": "code",
        "outputId": "ad2ea6c9-d7d2-40c8-a50a-22c2daefa5c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "beds_for_elderly_population['max_percent_elderly'] =  beds_for_elderly_population['num_beds'] / beds_for_elderly_population['elderly_population']*100.0\n",
        "beds_for_elderly_population"
      ],
      "execution_count": 162,
      "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>elderly_population</th>\n",
              "      <th>num_beds</th>\n",
              "      <th>max_percent_elderly</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>los angeles county</th>\n",
              "      <td>2152960.0</td>\n",
              "      <td>2145</td>\n",
              "      <td>0.099630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orange county</th>\n",
              "      <td>717395.0</td>\n",
              "      <td>614</td>\n",
              "      <td>0.085587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san diego county</th>\n",
              "      <td>688677.0</td>\n",
              "      <td>605</td>\n",
              "      <td>0.087850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riverside county</th>\n",
              "      <td>525411.0</td>\n",
              "      <td>378</td>\n",
              "      <td>0.071944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa clara county</th>\n",
              "      <td>417912.0</td>\n",
              "      <td>438</td>\n",
              "      <td>0.104807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san bernardino county</th>\n",
              "      <td>392687.0</td>\n",
              "      <td>486</td>\n",
              "      <td>0.123763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alameda county</th>\n",
              "      <td>377472.0</td>\n",
              "      <td>291</td>\n",
              "      <td>0.077092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sacramento county</th>\n",
              "      <td>327733.0</td>\n",
              "      <td>376</td>\n",
              "      <td>0.114728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contra costa county</th>\n",
              "      <td>285941.0</td>\n",
              "      <td>169</td>\n",
              "      <td>0.059103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san francisco county</th>\n",
              "      <td>205190.0</td>\n",
              "      <td>326</td>\n",
              "      <td>0.158877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san mateo county</th>\n",
              "      <td>197665.0</td>\n",
              "      <td>96</td>\n",
              "      <td>0.048567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventura county</th>\n",
              "      <td>197639.0</td>\n",
              "      <td>123</td>\n",
              "      <td>0.062235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fresno county</th>\n",
              "      <td>182237.0</td>\n",
              "      <td>149</td>\n",
              "      <td>0.081762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kern county</th>\n",
              "      <td>156139.0</td>\n",
              "      <td>155</td>\n",
              "      <td>0.099271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san joaquin county</th>\n",
              "      <td>148223.0</td>\n",
              "      <td>90</td>\n",
              "      <td>0.060719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sonoma county</th>\n",
              "      <td>141367.0</td>\n",
              "      <td>72</td>\n",
              "      <td>0.050931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>placer county</th>\n",
              "      <td>107029.0</td>\n",
              "      <td>58</td>\n",
              "      <td>0.054191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stanislaus county</th>\n",
              "      <td>104865.0</td>\n",
              "      <td>92</td>\n",
              "      <td>0.087732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>solano county</th>\n",
              "      <td>104407.0</td>\n",
              "      <td>82</td>\n",
              "      <td>0.078539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa barbara county</th>\n",
              "      <td>96753.0</td>\n",
              "      <td>79</td>\n",
              "      <td>0.081651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>monterey county</th>\n",
              "      <td>87896.0</td>\n",
              "      <td>30</td>\n",
              "      <td>0.034131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marin county</th>\n",
              "      <td>86143.0</td>\n",
              "      <td>30</td>\n",
              "      <td>0.034826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tulare county</th>\n",
              "      <td>82689.0</td>\n",
              "      <td>65</td>\n",
              "      <td>0.078608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san luis obispo county</th>\n",
              "      <td>76867.0</td>\n",
              "      <td>48</td>\n",
              "      <td>0.062446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa cruz county</th>\n",
              "      <td>67628.0</td>\n",
              "      <td>13</td>\n",
              "      <td>0.019223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>el dorado county</th>\n",
              "      <td>61438.0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.045574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>butte county</th>\n",
              "      <td>57513.0</td>\n",
              "      <td>47</td>\n",
              "      <td>0.081721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shasta county</th>\n",
              "      <td>48541.0</td>\n",
              "      <td>70</td>\n",
              "      <td>0.144208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>merced county</th>\n",
              "      <td>46804.0</td>\n",
              "      <td>24</td>\n",
              "      <td>0.051278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yolo county</th>\n",
              "      <td>40207.0</td>\n",
              "      <td>14</td>\n",
              "      <td>0.034820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nevada county</th>\n",
              "      <td>36661.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.021822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>imperial county</th>\n",
              "      <td>36547.0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.076614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>napa county</th>\n",
              "      <td>36326.0</td>\n",
              "      <td>48</td>\n",
              "      <td>0.132137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>humboldt county</th>\n",
              "      <td>34673.0</td>\n",
              "      <td>26</td>\n",
              "      <td>0.074986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>madera county</th>\n",
              "      <td>32477.0</td>\n",
              "      <td>52</td>\n",
              "      <td>0.160113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mendocino county</th>\n",
              "      <td>26010.0</td>\n",
              "      <td>16</td>\n",
              "      <td>0.061515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kings county</th>\n",
              "      <td>23677.0</td>\n",
              "      <td>22</td>\n",
              "      <td>0.092917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lake county</th>\n",
              "      <td>19845.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.040312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tuolumne county</th>\n",
              "      <td>19047.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.031501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calaveras county</th>\n",
              "      <td>17413.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.045943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tehama county</th>\n",
              "      <td>17078.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.046844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>siskiyou county</th>\n",
              "      <td>15133.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.052865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuba county</th>\n",
              "      <td>14552.0</td>\n",
              "      <td>24</td>\n",
              "      <td>0.164926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>san benito county</th>\n",
              "      <td>13594.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.029425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>amador county</th>\n",
              "      <td>13393.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.044800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>del norte county</th>\n",
              "      <td>6855.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.087527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inyo county</th>\n",
              "      <td>6068.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.065920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colusa county</th>\n",
              "      <td>4619.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.129898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mono county</th>\n",
              "      <td>3591.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.055695</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        elderly_population  num_beds  max_percent_elderly\n",
              "los angeles county               2152960.0      2145             0.099630\n",
              "orange county                     717395.0       614             0.085587\n",
              "san diego county                  688677.0       605             0.087850\n",
              "riverside county                  525411.0       378             0.071944\n",
              "santa clara county                417912.0       438             0.104807\n",
              "san bernardino county             392687.0       486             0.123763\n",
              "alameda county                    377472.0       291             0.077092\n",
              "sacramento county                 327733.0       376             0.114728\n",
              "contra costa county               285941.0       169             0.059103\n",
              "san francisco county              205190.0       326             0.158877\n",
              "san mateo county                  197665.0        96             0.048567\n",
              "ventura county                    197639.0       123             0.062235\n",
              "fresno county                     182237.0       149             0.081762\n",
              "kern county                       156139.0       155             0.099271\n",
              "san joaquin county                148223.0        90             0.060719\n",
              "sonoma county                     141367.0        72             0.050931\n",
              "placer county                     107029.0        58             0.054191\n",
              "stanislaus county                 104865.0        92             0.087732\n",
              "solano county                     104407.0        82             0.078539\n",
              "santa barbara county               96753.0        79             0.081651\n",
              "monterey county                    87896.0        30             0.034131\n",
              "marin county                       86143.0        30             0.034826\n",
              "tulare county                      82689.0        65             0.078608\n",
              "san luis obispo county             76867.0        48             0.062446\n",
              "santa cruz county                  67628.0        13             0.019223\n",
              "el dorado county                   61438.0        28             0.045574\n",
              "butte county                       57513.0        47             0.081721\n",
              "shasta county                      48541.0        70             0.144208\n",
              "merced county                      46804.0        24             0.051278\n",
              "yolo county                        40207.0        14             0.034820\n",
              "nevada county                      36661.0         8             0.021822\n",
              "imperial county                    36547.0        28             0.076614\n",
              "napa county                        36326.0        48             0.132137\n",
              "humboldt county                    34673.0        26             0.074986\n",
              "madera county                      32477.0        52             0.160113\n",
              "mendocino county                   26010.0        16             0.061515\n",
              "kings county                       23677.0        22             0.092917\n",
              "lake county                        19845.0         8             0.040312\n",
              "tuolumne county                    19047.0         6             0.031501\n",
              "calaveras county                   17413.0         8             0.045943\n",
              "tehama county                      17078.0         8             0.046844\n",
              "siskiyou county                    15133.0         8             0.052865\n",
              "yuba county                        14552.0        24             0.164926\n",
              "san benito county                  13594.0         4             0.029425\n",
              "amador county                      13393.0         6             0.044800\n",
              "del norte county                    6855.0         6             0.087527\n",
              "inyo county                         6068.0         4             0.065920\n",
              "colusa county                       4619.0         6             0.129898\n",
              "mono county                         3591.0         2             0.055695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oN4Mz5ySHWgN",
        "colab_type": "code",
        "outputId": "113fa4b8-a132-4c2a-ed81-f7aadc1039f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,7))\n",
        "beds_for_elderly_population[['max_percent_elderly']].iloc[0:10].plot.bar(ax=ax)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f19dc5456d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MAADQOhDqAJzS2oWGKGnCUqfLaNSyacmqdLoIAABgJU6/BAAAAACLEeoAAAAA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\nEckYizoiIiIiIiIZY1FHREREREQkY/8PAmN59UbE17gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vComHwQd6puQ",
        "colab_type": "code",
        "outputId": "d073ab37-2d40-46b0-dbea-77332802c472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "!pip install -U plotly\n",
        "!pip install plotly-geo\n",
        "!pip install geopandas==0.3.0\n",
        "!pip install pyshp==1.2.10\n",
        "!pip install shapely==1.6.3"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: plotly in /usr/local/lib/python3.6/dist-packages (4.5.4)\n",
            "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly) (1.3.3)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.12.0)\n",
            "Requirement already satisfied: plotly-geo in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "Requirement already satisfied: geopandas==0.3.0 in /usr/local/lib/python3.6/dist-packages (0.3.0)\n",
            "Requirement already satisfied: pyproj in /usr/local/lib/python3.6/dist-packages (from geopandas==0.3.0) (2.5.0)\n",
            "Requirement already satisfied: descartes in /usr/local/lib/python3.6/dist-packages (from geopandas==0.3.0) (1.1.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from geopandas==0.3.0) (0.25.3)\n",
            "Requirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas==0.3.0) (1.6.3)\n",
            "Requirement already satisfied: fiona in /usr/local/lib/python3.6/dist-packages (from geopandas==0.3.0) (1.8.13.post1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from descartes->geopandas==0.3.0) (3.1.3)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas==0.3.0) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas==0.3.0) (2018.9)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas->geopandas==0.3.0) (1.17.5)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (2.5.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (19.3.0)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (0.5.0)\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (7.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (1.12.0)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas==0.3.0) (1.1.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->descartes->geopandas==0.3.0) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->descartes->geopandas==0.3.0) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->descartes->geopandas==0.3.0) (2.4.6)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->descartes->geopandas==0.3.0) (45.2.0)\n",
            "Requirement already satisfied: pyshp==1.2.10 in /usr/local/lib/python3.6/dist-packages (1.2.10)\n",
            "Requirement already satisfied: shapely==1.6.3 in /usr/local/lib/python3.6/dist-packages (1.6.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfieJnU5-YE5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fips_df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv')\n",
        "ca_fips_df = fips_df[fips_df['STNAME'] == 'California']\n",
        "joined = pd.merge(ca_fips_df[['FIPS','CTYNAME']], grouped_by_county, left_on='CTYNAME', right_on='County')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1HxSr41BwK3",
        "colab_type": "code",
        "outputId": "e4c8af19-c6aa-405d-f7d4-a46606481059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "import numpy as np\n",
        "import plotly.figure_factory as ff\n",
        "\n",
        "values = joined['2020']\n",
        "fips = joined['FIPS']\n",
        "\n",
        "endpts = list(np.mgrid[min(values):max(values):4j])\n",
        "colorscale = [\n",
        "    'rgb(193, 193, 193)',\n",
        "    'rgb(239,239,239)',\n",
        "    'rgb(195, 196, 222)',\n",
        "    'rgb(147, 147, 58)',\n",
        "    'rgb(241, 241, 133)',\n",
        "    'rgb(62, 85, 73)',\n",
        "    'rgb(185, 161, 17)',\n",
        "    'rgb(33, 164, 221)',\n",
        "    'rgb(255, 186, 84)',\n",
        "    'rgb(238, 66, 74)'\n",
        "]\n",
        "\n",
        "fig = ff.create_choropleth(\n",
        "    fips=fips, values=values, scope=['CA'],\n",
        "    binning_endpoints=[500, 10000, 20000, 50000, 100000, 200000, 300000, 500000,1000000], colorscale=colorscale,\n",
        "    county_outline={'color': 'rgb(255,255,255)', 'width': 0.5}, round_legend_values=True,\n",
        "    legend_title='Population', title='Population >= 60 Year Old in California'\n",
        ")\n",
        "fig.layout.template = None\n",
        "fig.show()"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_n0X7_35VYeg",
        "colab_type": "text"
      },
      "source": [
        "![Population >= 60 Year Old](https://drive.google.com/uc?id=1MRvcj8qnKS6Es4ujLzywiqyYs2_q1jkU)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDJiMoHnbm1t",
        "colab_type": "code",
        "outputId": "b64bc2e5-d9ab-4bc9-bff5-53b321f0d98f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!git clone https://github.com/CSSEGISandData/COVID-19"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'COVID-19' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AubuctKtb0Xv",
        "colab_type": "code",
        "outputId": "3fc86efc-ce02-48e2-e1a2-3cea7d46c8e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "!head -5 COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Province/State,Country/Region,Lat,Long,1/22/20,1/23/20,1/24/20,1/25/20,1/26/20,1/27/20,1/28/20,1/29/20,1/30/20,1/31/20,2/1/20,2/2/20,2/3/20,2/4/20,2/5/20,2/6/20,2/7/20,2/8/20,2/9/20,2/10/20,2/11/20,2/12/20,2/13/20,2/14/20,2/15/20,2/16/20,2/17/20,2/18/20,2/19/20,2/20/20,2/21/20,2/22/20,2/23/20,2/24/20,2/25/20,2/26/20,2/27/20,2/28/20,2/29/20,3/1/20,3/2/20,3/3/20,3/4/20,3/5/20,3/6/20,3/7/20,3/8/20,3/9/20,3/10/20,3/11/20,3/12/20,3/13/20,3/14/20\n",
            ",Thailand,15.0,101.0,2,3,5,7,8,8,14,14,14,19,19,19,19,25,25,25,25,32,32,32,33,33,33,33,33,34,35,35,35,35,35,35,35,35,37,40,40,41,42,42,43,43,43,47,48,50,50,50,53,59,70,75,\n",
            ",Japan,36.0,138.0,2,1,2,2,4,4,7,7,11,15,20,20,20,22,22,45,25,25,26,26,26,28,28,29,43,59,66,74,84,94,105,122,147,159,170,189,214,228,241,256,274,293,331,360,420,461,502,511,581,639,639,701,\n",
            ",Singapore,1.2833,103.8333,0,1,3,3,4,5,7,7,10,13,16,18,18,24,28,28,30,33,40,45,47,50,58,67,72,75,77,81,84,84,85,85,89,89,91,93,93,93,102,106,108,110,110,117,130,138,150,150,160,178,178,200,\n",
            ",Nepal,28.1667,84.25,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51j02B1_b8Q-",
        "colab_type": "code",
        "outputId": "090f3773-c519-4596-eaae-2d17d09c6e27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "!head -5 COVID-19/who_covid_19_situation_reports/who_covid_19_sit_rep_time_series/who_covid_19_sit_rep_time_series.csv"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Province/States,Country/Region,WHO region,1/21/2020,1/22/2020,1/23/2020,1/24/2020,1/25/2020,1/26/2020,1/27/2020,1/28/2020,1/29/2020,1/30/2020,1/31/2020,2/1/2020,2/2/2020,2/3/2020,2/4/2020,2/5/2020,2/6/2020,2/7/2020,2/8/2020,2/9/2020,2/10/2020,2/11/2020,2/12/2020,2/13/2020,2/14/2020,2/15/2020,2/16/2020,2/17/2020,2/18/2020,2/19/2020,2/20/2020,2/21/2020,2/22/2020,2/23/2020,2/24/2020,2/25/2020,2/26/2020,2/27/2020,2/28/2020,2/29/2020,3/1/2020,3/2/2020,3/3/2020,3/4/2020,3/5/2020,,,,\r\n",
            "Confirmed,Globally,,282,314,581,846,1320,2014,2798,4593,6065,7818,9826,11953,14557,17391,20630,24554,28276,31481,34886,37558,40554,43103,45171,46997,49053,50580,51857,71429,73332,75204,75748,76769,77794,78811,79331,80239,81109,82294,83652,85403,87137,88948,90870,93091,95324,,,,\r\n",
            "Confirmed,China,Western Pacific Region,278,309,571,830,1297,1985,2741,4537,5997,7736,9720,11821,14411,17238,20471,24363,28060,31211,34598,37251,40235,42708,44730,46550,48548,50054,51174,70635,72528,74280,74675,75569,76392,77042,77262,77780,78191,78630,78961,79394,79968,80174,80304,80422,80565,,,,\r\n",
            "Confirmed,Outside of China,,4,5,10,16,23,29,57,56,68,82,106,132,146,153,159,191,216,270,288,307,319,395,441,447,505,526,683,794,804,924,1073,1200,1402,1769,2069,2459,2918,3664,4691,6009,7169,8774,10566,12669,14759,,,,\r\n",
            "Deaths,China,Western Pacific Region,,,,,,,80,106,132,170,213,259,304,361,425,491,564,637,723,812,909,1017,1114,1260,1381,1524,1666,1772,1870,2006,2121,2239,2348,2445,2595,2666,2718,2747,2791,2838,2873,2915,2946,2984,3015,,,,\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u01GjVoNcgKv",
        "colab_type": "code",
        "outputId": "83d7ba3f-bfa6-4519-d220-54dae6006e27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.25.3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGGZioLjckNy",
        "colab_type": "code",
        "outputId": "af2a3b07-fd5e-4bf5-8af5-6ed286d8570e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        }
      },
      "source": [
        "covid_across_countries_df = pd.read_csv('COVID-19/who_covid_19_situation_reports/who_covid_19_sit_rep_time_series/who_covid_19_sit_rep_time_series.csv', skiprows=0)\n",
        "covid_across_countries_df.head(10)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Province/States</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>WHO region</th>\n",
              "      <th>1/21/2020</th>\n",
              "      <th>1/22/2020</th>\n",
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              "      <th>2/5/2020</th>\n",
              "      <th>2/6/2020</th>\n",
              "      <th>2/7/2020</th>\n",
              "      <th>2/8/2020</th>\n",
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              "      <th>2/10/2020</th>\n",
              "      <th>2/11/2020</th>\n",
              "      <th>2/12/2020</th>\n",
              "      <th>2/13/2020</th>\n",
              "      <th>2/14/2020</th>\n",
              "      <th>2/15/2020</th>\n",
              "      <th>2/16/2020</th>\n",
              "      <th>2/17/2020</th>\n",
              "      <th>2/18/2020</th>\n",
              "      <th>2/19/2020</th>\n",
              "      <th>2/20/2020</th>\n",
              "      <th>2/21/2020</th>\n",
              "      <th>2/22/2020</th>\n",
              "      <th>2/23/2020</th>\n",
              "      <th>2/24/2020</th>\n",
              "      <th>2/25/2020</th>\n",
              "      <th>2/26/2020</th>\n",
              "      <th>2/27/2020</th>\n",
              "      <th>2/28/2020</th>\n",
              "      <th>2/29/2020</th>\n",
              "      <th>3/1/2020</th>\n",
              "      <th>3/2/2020</th>\n",
              "      <th>3/3/2020</th>\n",
              "      <th>3/4/2020</th>\n",
              "      <th>3/5/2020</th>\n",
              "      <th>Unnamed: 48</th>\n",
              "      <th>Unnamed: 49</th>\n",
              "      <th>Unnamed: 50</th>\n",
              "      <th>Unnamed: 51</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>Globally</td>\n",
              "      <td>NaN</td>\n",
              "      <td>282.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>581.0</td>\n",
              "      <td>846.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>2798.0</td>\n",
              "      <td>4593.0</td>\n",
              "      <td>6065.0</td>\n",
              "      <td>7818.0</td>\n",
              "      <td>9826.0</td>\n",
              "      <td>11953.0</td>\n",
              "      <td>14557.0</td>\n",
              "      <td>17391.0</td>\n",
              "      <td>20630.0</td>\n",
              "      <td>24554.0</td>\n",
              "      <td>28276.0</td>\n",
              "      <td>31481.0</td>\n",
              "      <td>34886.0</td>\n",
              "      <td>37558.0</td>\n",
              "      <td>40554.0</td>\n",
              "      <td>43103.0</td>\n",
              "      <td>45171.0</td>\n",
              "      <td>46997.0</td>\n",
              "      <td>49053.0</td>\n",
              "      <td>50580.0</td>\n",
              "      <td>51857.0</td>\n",
              "      <td>71429.0</td>\n",
              "      <td>73332.0</td>\n",
              "      <td>75204.0</td>\n",
              "      <td>75748.0</td>\n",
              "      <td>76769.0</td>\n",
              "      <td>77794.0</td>\n",
              "      <td>78811.0</td>\n",
              "      <td>79331.0</td>\n",
              "      <td>80239.0</td>\n",
              "      <td>81109.0</td>\n",
              "      <td>82294.0</td>\n",
              "      <td>83652.0</td>\n",
              "      <td>85403.0</td>\n",
              "      <td>87137.0</td>\n",
              "      <td>88948.0</td>\n",
              "      <td>90870.0</td>\n",
              "      <td>93091.0</td>\n",
              "      <td>95324.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>278.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>571.0</td>\n",
              "      <td>830.0</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>1985.0</td>\n",
              "      <td>2741.0</td>\n",
              "      <td>4537.0</td>\n",
              "      <td>5997.0</td>\n",
              "      <td>7736.0</td>\n",
              "      <td>9720.0</td>\n",
              "      <td>11821.0</td>\n",
              "      <td>14411.0</td>\n",
              "      <td>17238.0</td>\n",
              "      <td>20471.0</td>\n",
              "      <td>24363.0</td>\n",
              "      <td>28060.0</td>\n",
              "      <td>31211.0</td>\n",
              "      <td>34598.0</td>\n",
              "      <td>37251.0</td>\n",
              "      <td>40235.0</td>\n",
              "      <td>42708.0</td>\n",
              "      <td>44730.0</td>\n",
              "      <td>46550.0</td>\n",
              "      <td>48548.0</td>\n",
              "      <td>50054.0</td>\n",
              "      <td>51174.0</td>\n",
              "      <td>70635.0</td>\n",
              "      <td>72528.0</td>\n",
              "      <td>74280.0</td>\n",
              "      <td>74675.0</td>\n",
              "      <td>75569.0</td>\n",
              "      <td>76392.0</td>\n",
              "      <td>77042.0</td>\n",
              "      <td>77262.0</td>\n",
              "      <td>77780.0</td>\n",
              "      <td>78191.0</td>\n",
              "      <td>78630.0</td>\n",
              "      <td>78961.0</td>\n",
              "      <td>79394.0</td>\n",
              "      <td>79968.0</td>\n",
              "      <td>80174.0</td>\n",
              "      <td>80304.0</td>\n",
              "      <td>80422.0</td>\n",
              "      <td>80565.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>Outside of China</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>395.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>447.0</td>\n",
              "      <td>505.0</td>\n",
              "      <td>526.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>794.0</td>\n",
              "      <td>804.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>1073.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>1769.0</td>\n",
              "      <td>2069.0</td>\n",
              "      <td>2459.0</td>\n",
              "      <td>2918.0</td>\n",
              "      <td>3664.0</td>\n",
              "      <td>4691.0</td>\n",
              "      <td>6009.0</td>\n",
              "      <td>7169.0</td>\n",
              "      <td>8774.0</td>\n",
              "      <td>10566.0</td>\n",
              "      <td>12669.0</td>\n",
              "      <td>14759.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Deaths</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</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>80.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>361.0</td>\n",
              "      <td>425.0</td>\n",
              "      <td>491.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>637.0</td>\n",
              "      <td>723.0</td>\n",
              "      <td>812.0</td>\n",
              "      <td>909.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1114.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>1381.0</td>\n",
              "      <td>1524.0</td>\n",
              "      <td>1666.0</td>\n",
              "      <td>1772.0</td>\n",
              "      <td>1870.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2121.0</td>\n",
              "      <td>2239.0</td>\n",
              "      <td>2348.0</td>\n",
              "      <td>2445.0</td>\n",
              "      <td>2595.0</td>\n",
              "      <td>2666.0</td>\n",
              "      <td>2718.0</td>\n",
              "      <td>2747.0</td>\n",
              "      <td>2791.0</td>\n",
              "      <td>2838.0</td>\n",
              "      <td>2873.0</td>\n",
              "      <td>2915.0</td>\n",
              "      <td>2946.0</td>\n",
              "      <td>2984.0</td>\n",
              "      <td>3015.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>258.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>375.0</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>7153.0</td>\n",
              "      <td>9074.0</td>\n",
              "      <td>11177.0</td>\n",
              "      <td>13522.0</td>\n",
              "      <td>16678.0</td>\n",
              "      <td>19665.0</td>\n",
              "      <td>22112.0</td>\n",
              "      <td>24953.0</td>\n",
              "      <td>27100.0</td>\n",
              "      <td>29631.0</td>\n",
              "      <td>31728.0</td>\n",
              "      <td>33366.0</td>\n",
              "      <td>34874.0</td>\n",
              "      <td>51968.0</td>\n",
              "      <td>54406.0</td>\n",
              "      <td>56249.0</td>\n",
              "      <td>58182.0</td>\n",
              "      <td>59989.0</td>\n",
              "      <td>61682.0</td>\n",
              "      <td>62031.0</td>\n",
              "      <td>62662.0</td>\n",
              "      <td>63454.0</td>\n",
              "      <td>64084.0</td>\n",
              "      <td>64287.0</td>\n",
              "      <td>64786.0</td>\n",
              "      <td>65187.0</td>\n",
              "      <td>65596.0</td>\n",
              "      <td>65914.0</td>\n",
              "      <td>66337.0</td>\n",
              "      <td>66907.0</td>\n",
              "      <td>67103.0</td>\n",
              "      <td>67217.0</td>\n",
              "      <td>67332.0</td>\n",
              "      <td>67466.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Guangdong</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>32.0</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>520.0</td>\n",
              "      <td>604.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>797.0</td>\n",
              "      <td>870.0</td>\n",
              "      <td>944.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1075.0</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>1151.0</td>\n",
              "      <td>1177.0</td>\n",
              "      <td>1219.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>1295.0</td>\n",
              "      <td>1316.0</td>\n",
              "      <td>1322.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>1331.0</td>\n",
              "      <td>1332.0</td>\n",
              "      <td>1333.0</td>\n",
              "      <td>1339.0</td>\n",
              "      <td>1342.0</td>\n",
              "      <td>1345.0</td>\n",
              "      <td>1347.0</td>\n",
              "      <td>1347.0</td>\n",
              "      <td>1347.0</td>\n",
              "      <td>1348.0</td>\n",
              "      <td>1349.0</td>\n",
              "      <td>1349.0</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Henan</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>422.0</td>\n",
              "      <td>493.0</td>\n",
              "      <td>566.0</td>\n",
              "      <td>675.0</td>\n",
              "      <td>764.0</td>\n",
              "      <td>851.0</td>\n",
              "      <td>914.0</td>\n",
              "      <td>981.0</td>\n",
              "      <td>1033.0</td>\n",
              "      <td>1073.0</td>\n",
              "      <td>1105.0</td>\n",
              "      <td>1135.0</td>\n",
              "      <td>1169.0</td>\n",
              "      <td>1184.0</td>\n",
              "      <td>1212.0</td>\n",
              "      <td>1231.0</td>\n",
              "      <td>1246.0</td>\n",
              "      <td>1257.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>1267.0</td>\n",
              "      <td>1270.0</td>\n",
              "      <td>1271.0</td>\n",
              "      <td>1271.0</td>\n",
              "      <td>1271.0</td>\n",
              "      <td>1271.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>1272.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</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>599.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>724.0</td>\n",
              "      <td>829.0</td>\n",
              "      <td>895.0</td>\n",
              "      <td>954.0</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>1048.0</td>\n",
              "      <td>1075.0</td>\n",
              "      <td>1104.0</td>\n",
              "      <td>1117.0</td>\n",
              "      <td>1131.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1155.0</td>\n",
              "      <td>1162.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>1171.0</td>\n",
              "      <td>1172.0</td>\n",
              "      <td>1173.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>1213.0</td>\n",
              "      <td>1213.0</td>\n",
              "      <td>1215.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hunan</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>389.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>593.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>803.0</td>\n",
              "      <td>838.0</td>\n",
              "      <td>879.0</td>\n",
              "      <td>912.0</td>\n",
              "      <td>946.0</td>\n",
              "      <td>968.0</td>\n",
              "      <td>988.0</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>1004.0</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>1007.0</td>\n",
              "      <td>1008.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1016.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Anhui</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>297.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>408.0</td>\n",
              "      <td>480.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>591.0</td>\n",
              "      <td>665.0</td>\n",
              "      <td>733.0</td>\n",
              "      <td>779.0</td>\n",
              "      <td>830.0</td>\n",
              "      <td>860.0</td>\n",
              "      <td>889.0</td>\n",
              "      <td>910.0</td>\n",
              "      <td>934.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>962.0</td>\n",
              "      <td>973.0</td>\n",
              "      <td>982.0</td>\n",
              "      <td>986.0</td>\n",
              "      <td>987.0</td>\n",
              "      <td>988.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>989.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>990.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/States    Country/Region  ... Unnamed: 50  Unnamed: 51\n",
              "0       Confirmed          Globally  ...         NaN          NaN\n",
              "1       Confirmed             China  ...         NaN          NaN\n",
              "2       Confirmed  Outside of China  ...         NaN          NaN\n",
              "3          Deaths             China  ...         NaN          NaN\n",
              "4          Hubei              China  ...         NaN          NaN\n",
              "5       Guangdong             China  ...         NaN          NaN\n",
              "6           Henan             China  ...         NaN          NaN\n",
              "7        Zhejiang             China  ...         NaN          NaN\n",
              "8           Hunan             China  ...         NaN          NaN\n",
              "9           Anhui             China  ...         NaN          NaN\n",
              "\n",
              "[10 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wZOmmjjczQU",
        "colab_type": "code",
        "outputId": "6f93a43f-9688-4044-de27-b0df6a029913",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "global_timeseries = covid_across_countries_df.iloc[0].iloc[3:]\n",
        "global_timeseries.head(5)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1/21/2020     282\n",
              "1/22/2020     314\n",
              "1/23/2020     581\n",
              "1/24/2020     846\n",
              "1/25/2020    1320\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PinKUe8Rc8uo",
        "colab_type": "code",
        "outputId": "5ff2f8f0-fcf6-41c0-c775-0ffd5a75a7cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(rc={'figure.figsize':(11, 4)})\n",
        "ax = global_timeseries.plot(linewidth=0.5, marker='o', linestyle='-')\n",
        "ax.set_title('Global Covid-19 Confirmed Cases')\n",
        "ax.set_ylabel('Daily Confirmed Cases');"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FlcfDw8Mxbdo0REVFYdq0aVUaeTf0GBERERGZhwZJcAMDA6v0JQQAuVyO2NhY9SaM0NBQ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\nSYrbj7KhVAnwdLFHHz8PNHOUwqe5A8s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L5eLTp08sLCyQlpaG0+nkxo0bnD9/nps3b5KZ\nmQnA1atXGRoailrc6uvrcTqdrK+vc/36dYaGhgC4dOmS+TJBvPeN/Ni7d+84efIkhw4dMrcOMzIy\nsNvtlJaWcuzYMYAdx3hNTQ0QKUAePHgAQEFBAa2trdhsNo3/31ys+bvdbp48eUJWVpZ57MWLF3G5\nXADcvXsXr9cLRJ781dTUEAwGOXPmDH6/3zwmnhyDwSB5eXkcPHjQfLEpISEBj8cDRHqCGxsb+fz5\nM0lJSbjd7qgnfhKb+fl5qqqqWFlZwWazkZyczLVr13j+/HnUWgCa7/8Wlihwt3PixAl6enpwOBxx\nn2NsbIzu7m66u7v38MrkV9iL/GdnZykvL2dgYGAPr0z+a2traxiGgd/vZ9+++H/qW+P/z7RX+T97\n9ozR0VFaWlr28OrkV4t1LdB8bz2WLXBFRERE5O9k2R5cEREREfk7qcAVEREREUtRgSsiIiIilqIC\nV0REREQsRQWuiIiIiFiKClwRERERsRQVuCIiIiJiKd8ANz5JrwoChc8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXbg_fl93Sxk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "bfc970bf-6ca9-42ef-836d-ba6bb2c7ee76"
      },
      "source": [
        "covid_across_countries_df"
      ],
      "execution_count": 174,
      "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>Province/States</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>WHO region</th>\n",
              "      <th>1/21/2020</th>\n",
              "      <th>1/22/2020</th>\n",
              "      <th>1/23/2020</th>\n",
              "      <th>1/24/2020</th>\n",
              "      <th>1/25/2020</th>\n",
              "      <th>1/26/2020</th>\n",
              "      <th>1/27/2020</th>\n",
              "      <th>1/28/2020</th>\n",
              "      <th>1/29/2020</th>\n",
              "      <th>1/30/2020</th>\n",
              "      <th>1/31/2020</th>\n",
              "      <th>2/1/2020</th>\n",
              "      <th>2/2/2020</th>\n",
              "      <th>2/3/2020</th>\n",
              "      <th>2/4/2020</th>\n",
              "      <th>2/5/2020</th>\n",
              "      <th>2/6/2020</th>\n",
              "      <th>2/7/2020</th>\n",
              "      <th>2/8/2020</th>\n",
              "      <th>2/9/2020</th>\n",
              "      <th>2/10/2020</th>\n",
              "      <th>2/11/2020</th>\n",
              "      <th>2/12/2020</th>\n",
              "      <th>2/13/2020</th>\n",
              "      <th>2/14/2020</th>\n",
              "      <th>2/15/2020</th>\n",
              "      <th>2/16/2020</th>\n",
              "      <th>2/17/2020</th>\n",
              "      <th>2/18/2020</th>\n",
              "      <th>2/19/2020</th>\n",
              "      <th>2/20/2020</th>\n",
              "      <th>2/21/2020</th>\n",
              "      <th>2/22/2020</th>\n",
              "      <th>2/23/2020</th>\n",
              "      <th>2/24/2020</th>\n",
              "      <th>2/25/2020</th>\n",
              "      <th>2/26/2020</th>\n",
              "      <th>2/27/2020</th>\n",
              "      <th>2/28/2020</th>\n",
              "      <th>2/29/2020</th>\n",
              "      <th>3/1/2020</th>\n",
              "      <th>3/2/2020</th>\n",
              "      <th>3/3/2020</th>\n",
              "      <th>3/4/2020</th>\n",
              "      <th>3/5/2020</th>\n",
              "      <th>Unnamed: 48</th>\n",
              "      <th>Unnamed: 49</th>\n",
              "      <th>Unnamed: 50</th>\n",
              "      <th>Unnamed: 51</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>Globally</td>\n",
              "      <td>NaN</td>\n",
              "      <td>282.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>581.0</td>\n",
              "      <td>846.0</td>\n",
              "      <td>1320.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>2798.0</td>\n",
              "      <td>4593.0</td>\n",
              "      <td>6065.0</td>\n",
              "      <td>7818.0</td>\n",
              "      <td>9826.0</td>\n",
              "      <td>11953.0</td>\n",
              "      <td>14557.0</td>\n",
              "      <td>17391.0</td>\n",
              "      <td>20630.0</td>\n",
              "      <td>24554.0</td>\n",
              "      <td>28276.0</td>\n",
              "      <td>31481.0</td>\n",
              "      <td>34886.0</td>\n",
              "      <td>37558.0</td>\n",
              "      <td>40554.0</td>\n",
              "      <td>43103.0</td>\n",
              "      <td>45171.0</td>\n",
              "      <td>46997.0</td>\n",
              "      <td>49053.0</td>\n",
              "      <td>50580.0</td>\n",
              "      <td>51857.0</td>\n",
              "      <td>71429.0</td>\n",
              "      <td>73332.0</td>\n",
              "      <td>75204.0</td>\n",
              "      <td>75748.0</td>\n",
              "      <td>76769.0</td>\n",
              "      <td>77794.0</td>\n",
              "      <td>78811.0</td>\n",
              "      <td>79331.0</td>\n",
              "      <td>80239.0</td>\n",
              "      <td>81109.0</td>\n",
              "      <td>82294.0</td>\n",
              "      <td>83652.0</td>\n",
              "      <td>85403.0</td>\n",
              "      <td>87137.0</td>\n",
              "      <td>88948.0</td>\n",
              "      <td>90870.0</td>\n",
              "      <td>93091.0</td>\n",
              "      <td>95324.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>278.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>571.0</td>\n",
              "      <td>830.0</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>1985.0</td>\n",
              "      <td>2741.0</td>\n",
              "      <td>4537.0</td>\n",
              "      <td>5997.0</td>\n",
              "      <td>7736.0</td>\n",
              "      <td>9720.0</td>\n",
              "      <td>11821.0</td>\n",
              "      <td>14411.0</td>\n",
              "      <td>17238.0</td>\n",
              "      <td>20471.0</td>\n",
              "      <td>24363.0</td>\n",
              "      <td>28060.0</td>\n",
              "      <td>31211.0</td>\n",
              "      <td>34598.0</td>\n",
              "      <td>37251.0</td>\n",
              "      <td>40235.0</td>\n",
              "      <td>42708.0</td>\n",
              "      <td>44730.0</td>\n",
              "      <td>46550.0</td>\n",
              "      <td>48548.0</td>\n",
              "      <td>50054.0</td>\n",
              "      <td>51174.0</td>\n",
              "      <td>70635.0</td>\n",
              "      <td>72528.0</td>\n",
              "      <td>74280.0</td>\n",
              "      <td>74675.0</td>\n",
              "      <td>75569.0</td>\n",
              "      <td>76392.0</td>\n",
              "      <td>77042.0</td>\n",
              "      <td>77262.0</td>\n",
              "      <td>77780.0</td>\n",
              "      <td>78191.0</td>\n",
              "      <td>78630.0</td>\n",
              "      <td>78961.0</td>\n",
              "      <td>79394.0</td>\n",
              "      <td>79968.0</td>\n",
              "      <td>80174.0</td>\n",
              "      <td>80304.0</td>\n",
              "      <td>80422.0</td>\n",
              "      <td>80565.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Confirmed</td>\n",
              "      <td>Outside of China</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>395.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>447.0</td>\n",
              "      <td>505.0</td>\n",
              "      <td>526.0</td>\n",
              "      <td>683.0</td>\n",
              "      <td>794.0</td>\n",
              "      <td>804.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>1073.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>1769.0</td>\n",
              "      <td>2069.0</td>\n",
              "      <td>2459.0</td>\n",
              "      <td>2918.0</td>\n",
              "      <td>3664.0</td>\n",
              "      <td>4691.0</td>\n",
              "      <td>6009.0</td>\n",
              "      <td>7169.0</td>\n",
              "      <td>8774.0</td>\n",
              "      <td>10566.0</td>\n",
              "      <td>12669.0</td>\n",
              "      <td>14759.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Deaths</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</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>80.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>361.0</td>\n",
              "      <td>425.0</td>\n",
              "      <td>491.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>637.0</td>\n",
              "      <td>723.0</td>\n",
              "      <td>812.0</td>\n",
              "      <td>909.0</td>\n",
              "      <td>1017.0</td>\n",
              "      <td>1114.0</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>1381.0</td>\n",
              "      <td>1524.0</td>\n",
              "      <td>1666.0</td>\n",
              "      <td>1772.0</td>\n",
              "      <td>1870.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2121.0</td>\n",
              "      <td>2239.0</td>\n",
              "      <td>2348.0</td>\n",
              "      <td>2445.0</td>\n",
              "      <td>2595.0</td>\n",
              "      <td>2666.0</td>\n",
              "      <td>2718.0</td>\n",
              "      <td>2747.0</td>\n",
              "      <td>2791.0</td>\n",
              "      <td>2838.0</td>\n",
              "      <td>2873.0</td>\n",
              "      <td>2915.0</td>\n",
              "      <td>2946.0</td>\n",
              "      <td>2984.0</td>\n",
              "      <td>3015.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>Western Pacific Region</td>\n",
              "      <td>258.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>375.0</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>7153.0</td>\n",
              "      <td>9074.0</td>\n",
              "      <td>11177.0</td>\n",
              "      <td>13522.0</td>\n",
              "      <td>16678.0</td>\n",
              "      <td>19665.0</td>\n",
              "      <td>22112.0</td>\n",
              "      <td>24953.0</td>\n",
              "      <td>27100.0</td>\n",
              "      <td>29631.0</td>\n",
              "      <td>31728.0</td>\n",
              "      <td>33366.0</td>\n",
              "      <td>34874.0</td>\n",
              "      <td>51968.0</td>\n",
              "      <td>54406.0</td>\n",
              "      <td>56249.0</td>\n",
              "      <td>58182.0</td>\n",
              "      <td>59989.0</td>\n",
              "      <td>61682.0</td>\n",
              "      <td>62031.0</td>\n",
              "      <td>62662.0</td>\n",
              "      <td>63454.0</td>\n",
              "      <td>64084.0</td>\n",
              "      <td>64287.0</td>\n",
              "      <td>64786.0</td>\n",
              "      <td>65187.0</td>\n",
              "      <td>65596.0</td>\n",
              "      <td>65914.0</td>\n",
              "      <td>66337.0</td>\n",
              "      <td>66907.0</td>\n",
              "      <td>67103.0</td>\n",
              "      <td>67217.0</td>\n",
              "      <td>67332.0</td>\n",
              "      <td>67466.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>118</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Hungary</td>\n",
              "      <td>European Region</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>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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Saint Barthélemy</td>\n",
              "      <td>Territories</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>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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Saint Martin</td>\n",
              "      <td>Territories</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>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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Gibraltar</td>\n",
              "      <td>Territories</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>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>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>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>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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Case on an international conveyance</td>\n",
              "      <td>Other</td>\n",
              "      <td>Other</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>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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>355.0</td>\n",
              "      <td>454.0</td>\n",
              "      <td>454.0</td>\n",
              "      <td>542.0</td>\n",
              "      <td>621.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>695.0</td>\n",
              "      <td>691.0</td>\n",
              "      <td>691.0</td>\n",
              "      <td>705.0</td>\n",
              "      <td>705.0</td>\n",
              "      <td>705.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>706.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>123 rows × 52 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Province/States  ... Unnamed: 51\n",
              "0                              Confirmed  ...         NaN\n",
              "1                              Confirmed  ...         NaN\n",
              "2                              Confirmed  ...         NaN\n",
              "3                                 Deaths  ...         NaN\n",
              "4                                 Hubei   ...         NaN\n",
              "..                                   ...  ...         ...\n",
              "118                                  NaN  ...         NaN\n",
              "119                                  NaN  ...         NaN\n",
              "120                                  NaN  ...         NaN\n",
              "121                                  NaN  ...         NaN\n",
              "122  Case on an international conveyance  ...         NaN\n",
              "\n",
              "[123 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKvF6IBifaCx",
        "colab_type": "code",
        "outputId": "c4af67f6-8efd-4f0c-b30e-0b022052e52f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "global_timeseries = covid_across_countries_df.iloc[[0,2]].T.iloc[3:]\n",
        "global_timeseries.head(5)"
      ],
      "execution_count": 175,
      "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>0</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1/21/2020</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/22/2020</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/23/2020</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/24/2020</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/25/2020</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              0   2\n",
              "1/21/2020   282   4\n",
              "1/22/2020   314   5\n",
              "1/23/2020   581  10\n",
              "1/24/2020   846  16\n",
              "1/25/2020  1320  23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqhboKFLwY8w",
        "colab_type": "code",
        "outputId": "eacc4dc9-4a57-47db-faa9-7839c2b5cb42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#pd.to_datetime(global_timeseries.iloc[:,0])\n",
        "global_timeseries['global']=global_timeseries.iloc[:,0]\n",
        "global_timeseries['USA']=global_timeseries.iloc[:,1]\n",
        "global_timeseries.drop(global_timeseries.columns[0],axis=1, inplace=True)\n",
        "global_timeseries.drop(global_timeseries.columns[0],axis=1, inplace=True)\n",
        "global_timeseries.head(5)\n"
      ],
      "execution_count": 176,
      "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>global</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1/21/2020</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/22/2020</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/23/2020</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/24/2020</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/25/2020</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          global USA\n",
              "1/21/2020    282   4\n",
              "1/22/2020    314   5\n",
              "1/23/2020    581  10\n",
              "1/24/2020    846  16\n",
              "1/25/2020   1320  23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNbuesif-Gh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6853fddc-e36f-4430-ade2-e9ed804db3cb"
      },
      "source": [
        "global_timeseries = global_timeseries.filter(regex='^((?!Unnamed).)*$', axis=0)\n",
        "global_timeseries\n"
      ],
      "execution_count": 177,
      "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>global</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1/21/2020</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/22/2020</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/23/2020</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/24/2020</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/25/2020</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/26/2020</th>\n",
              "      <td>2014</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/27/2020</th>\n",
              "      <td>2798</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/28/2020</th>\n",
              "      <td>4593</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/29/2020</th>\n",
              "      <td>6065</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/30/2020</th>\n",
              "      <td>7818</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1/31/2020</th>\n",
              "      <td>9826</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/1/2020</th>\n",
              "      <td>11953</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/2/2020</th>\n",
              "      <td>14557</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/3/2020</th>\n",
              "      <td>17391</td>\n",
              "      <td>153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/4/2020</th>\n",
              "      <td>20630</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/5/2020</th>\n",
              "      <td>24554</td>\n",
              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/6/2020</th>\n",
              "      <td>28276</td>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/7/2020</th>\n",
              "      <td>31481</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/8/2020</th>\n",
              "      <td>34886</td>\n",
              "      <td>288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/9/2020</th>\n",
              "      <td>37558</td>\n",
              "      <td>307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/10/2020</th>\n",
              "      <td>40554</td>\n",
              "      <td>319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/11/2020</th>\n",
              "      <td>43103</td>\n",
              "      <td>395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/12/2020</th>\n",
              "      <td>45171</td>\n",
              "      <td>441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/13/2020</th>\n",
              "      <td>46997</td>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/14/2020</th>\n",
              "      <td>49053</td>\n",
              "      <td>505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/15/2020</th>\n",
              "      <td>50580</td>\n",
              "      <td>526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/16/2020</th>\n",
              "      <td>51857</td>\n",
              "      <td>683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/17/2020</th>\n",
              "      <td>71429</td>\n",
              "      <td>794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/18/2020</th>\n",
              "      <td>73332</td>\n",
              "      <td>804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/19/2020</th>\n",
              "      <td>75204</td>\n",
              "      <td>924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/20/2020</th>\n",
              "      <td>75748</td>\n",
              "      <td>1073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/21/2020</th>\n",
              "      <td>76769</td>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/22/2020</th>\n",
              "      <td>77794</td>\n",
              "      <td>1402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/23/2020</th>\n",
              "      <td>78811</td>\n",
              "      <td>1769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/24/2020</th>\n",
              "      <td>79331</td>\n",
              "      <td>2069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/25/2020</th>\n",
              "      <td>80239</td>\n",
              "      <td>2459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/26/2020</th>\n",
              "      <td>81109</td>\n",
              "      <td>2918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/27/2020</th>\n",
              "      <td>82294</td>\n",
              "      <td>3664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/28/2020</th>\n",
              "      <td>83652</td>\n",
              "      <td>4691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2/29/2020</th>\n",
              "      <td>85403</td>\n",
              "      <td>6009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/1/2020</th>\n",
              "      <td>87137</td>\n",
              "      <td>7169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/2/2020</th>\n",
              "      <td>88948</td>\n",
              "      <td>8774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/3/2020</th>\n",
              "      <td>90870</td>\n",
              "      <td>10566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/4/2020</th>\n",
              "      <td>93091</td>\n",
              "      <td>12669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3/5/2020</th>\n",
              "      <td>95324</td>\n",
              "      <td>14759</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          global    USA\n",
              "1/21/2020    282      4\n",
              "1/22/2020    314      5\n",
              "1/23/2020    581     10\n",
              "1/24/2020    846     16\n",
              "1/25/2020   1320     23\n",
              "1/26/2020   2014     29\n",
              "1/27/2020   2798     57\n",
              "1/28/2020   4593     56\n",
              "1/29/2020   6065     68\n",
              "1/30/2020   7818     82\n",
              "1/31/2020   9826    106\n",
              "2/1/2020   11953    132\n",
              "2/2/2020   14557    146\n",
              "2/3/2020   17391    153\n",
              "2/4/2020   20630    159\n",
              "2/5/2020   24554    191\n",
              "2/6/2020   28276    216\n",
              "2/7/2020   31481    270\n",
              "2/8/2020   34886    288\n",
              "2/9/2020   37558    307\n",
              "2/10/2020  40554    319\n",
              "2/11/2020  43103    395\n",
              "2/12/2020  45171    441\n",
              "2/13/2020  46997    447\n",
              "2/14/2020  49053    505\n",
              "2/15/2020  50580    526\n",
              "2/16/2020  51857    683\n",
              "2/17/2020  71429    794\n",
              "2/18/2020  73332    804\n",
              "2/19/2020  75204    924\n",
              "2/20/2020  75748   1073\n",
              "2/21/2020  76769   1200\n",
              "2/22/2020  77794   1402\n",
              "2/23/2020  78811   1769\n",
              "2/24/2020  79331   2069\n",
              "2/25/2020  80239   2459\n",
              "2/26/2020  81109   2918\n",
              "2/27/2020  82294   3664\n",
              "2/28/2020  83652   4691\n",
              "2/29/2020  85403   6009\n",
              "3/1/2020   87137   7169\n",
              "3/2/2020   88948   8774\n",
              "3/3/2020   90870  10566\n",
              "3/4/2020   93091  12669\n",
              "3/5/2020   95324  14759"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ILb5ediHmVE",
        "colab_type": "code",
        "outputId": "51d9ca4a-1a11-448c-ceed-16b739b69f20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "global_timeseries.index = pd.to_datetime(global_timeseries.index, errors='ignore')\n",
        "global_timeseries.head(5)"
      ],
      "execution_count": 178,
      "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>global</th>\n",
              "      <th>USA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           global USA\n",
              "2020-01-21    282   4\n",
              "2020-01-22    314   5\n",
              "2020-01-23    581  10\n",
              "2020-01-24    846  16\n",
              "2020-01-25   1320  23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1_KhbrBgvVY",
        "colab_type": "code",
        "outputId": "4886f156-df50-4311-980b-74499367cf25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates\n",
        "pd.plotting.register_matplotlib_converters()\n",
        "\n",
        "cols = ['global', 'USA']\n",
        "sns.set(rc={'figure.figsize':(11, 4)})\n",
        "ax = global_timeseries[cols].plot(linewidth=0.5, marker='o', linestyle='-')\n",
        "ax.set_title('USA vs Global Covid-19 Daily Confirmed Cases')\n",
        "ax.set_ylabel('Daily Confirmed Cases')\n",
        "ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MLl26BE9PT1hbW2Py5MnYs2cPNm/ejCNHjlT5aQ6RJWKCS9RADB48GN9++63uF/+pU6dw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\nd/TMrk2/7KcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gitH0Vu6JEPt",
        "colab_type": "code",
        "outputId": "3842416e-c095-4069-cc39-fc793838b37e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "global_timeseries['year'] = global_timeseries.index.year\n",
        "global_timeseries['month'] = global_timeseries.index.month\n",
        "global_timeseries['weekday'] = global_timeseries.index.weekday_name\n",
        "global_timeseries.head(5)"
      ],
      "execution_count": 180,
      "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>global</th>\n",
              "      <th>USA</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           global USA  year  month    weekday\n",
              "2020-01-21    282   4  2020      1    Tuesday\n",
              "2020-01-22    314   5  2020      1  Wednesday\n",
              "2020-01-23    581  10  2020      1   Thursday\n",
              "2020-01-24    846  16  2020      1     Friday\n",
              "2020-01-25   1320  23  2020      1   Saturday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siWZoF89KgB7",
        "colab_type": "code",
        "outputId": "9257be46-0349-4fb6-afba-0c4e0aa7b59b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "sns.boxplot(data=global_timeseries, x='weekday', y='global')"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f19d7f54b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cmZmZ//V5e/TooXHjxun+++9XSUmJioqKFBkZqWuuuUbvvvuuUlNT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\nARcAAACOQsAFAACAoxBwAQAA4Cj/H5FhUp0hKT2yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlOE1UVrLlCY",
        "colab_type": "code",
        "outputId": "dca82ba5-6378-4353-ab25-2fce4bb1efcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "sns.boxplot(data=global_timeseries, x='month', y='global');"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jp59+OnStvr5e6enpoZvaHnjgAb311lsxGwMAAIAdIi64Bw4cUElJyWXX7rzzztCs7aJF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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 792x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saUyHsqyRNsa",
        "colab_type": "code",
        "outputId": "9f88ab9e-38dc-4997-8e87-81b4d3a8b826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "global_timeseries.head(10)"
      ],
      "execution_count": 183,
      "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>global</th>\n",
              "      <th>USA</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2014</td>\n",
              "      <td>29</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>2798</td>\n",
              "      <td>57</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>4593</td>\n",
              "      <td>56</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>6065</td>\n",
              "      <td>68</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>7818</td>\n",
              "      <td>82</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           global USA  year  month    weekday\n",
              "2020-01-21    282   4  2020      1    Tuesday\n",
              "2020-01-22    314   5  2020      1  Wednesday\n",
              "2020-01-23    581  10  2020      1   Thursday\n",
              "2020-01-24    846  16  2020      1     Friday\n",
              "2020-01-25   1320  23  2020      1   Saturday\n",
              "2020-01-26   2014  29  2020      1     Sunday\n",
              "2020-01-27   2798  57  2020      1     Monday\n",
              "2020-01-28   4593  56  2020      1    Tuesday\n",
              "2020-01-29   6065  68  2020      1  Wednesday\n",
              "2020-01-30   7818  82  2020      1   Thursday"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "160A-FVzRQZZ",
        "colab_type": "code",
        "outputId": "d688d235-c208-461a-cb63-d3a041c932a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "global_timeseries['date'] = global_timeseries.index.to_frame(index=True)\n",
        "global_timeseries.head(10)"
      ],
      "execution_count": 184,
      "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",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>global</th>\n",
              "      <th>USA</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2020-01-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>2020-01-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>2020-01-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Friday</td>\n",
              "      <td>2020-01-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>2020-01-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2014</td>\n",
              "      <td>29</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>2020-01-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>2798</td>\n",
              "      <td>57</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Monday</td>\n",
              "      <td>2020-01-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>4593</td>\n",
              "      <td>56</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2020-01-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>6065</td>\n",
              "      <td>68</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>2020-01-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>7818</td>\n",
              "      <td>82</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>2020-01-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           global USA  year  month    weekday       date\n",
              "2020-01-21    282   4  2020      1    Tuesday 2020-01-21\n",
              "2020-01-22    314   5  2020      1  Wednesday 2020-01-22\n",
              "2020-01-23    581  10  2020      1   Thursday 2020-01-23\n",
              "2020-01-24    846  16  2020      1     Friday 2020-01-24\n",
              "2020-01-25   1320  23  2020      1   Saturday 2020-01-25\n",
              "2020-01-26   2014  29  2020      1     Sunday 2020-01-26\n",
              "2020-01-27   2798  57  2020      1     Monday 2020-01-27\n",
              "2020-01-28   4593  56  2020      1    Tuesday 2020-01-28\n",
              "2020-01-29   6065  68  2020      1  Wednesday 2020-01-29\n",
              "2020-01-30   7818  82  2020      1   Thursday 2020-01-30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ECZ_I1KM5TO",
        "colab_type": "code",
        "outputId": "0fa25b9f-c504-4c10-a382-281274fe818c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "global_data = global_timeseries.rename(columns={'date': 'ds', 'global': 'y'})\n",
        "global_data.head(5)\n"
      ],
      "execution_count": 185,
      "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>y</th>\n",
              "      <th>USA</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>weekday</th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>282</td>\n",
              "      <td>4</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>2020-01-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>314</td>\n",
              "      <td>5</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>2020-01-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>581</td>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>2020-01-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>846</td>\n",
              "      <td>16</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Friday</td>\n",
              "      <td>2020-01-24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1320</td>\n",
              "      <td>23</td>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>2020-01-25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               y USA  year  month    weekday         ds\n",
              "2020-01-21   282   4  2020      1    Tuesday 2020-01-21\n",
              "2020-01-22   314   5  2020      1  Wednesday 2020-01-22\n",
              "2020-01-23   581  10  2020      1   Thursday 2020-01-23\n",
              "2020-01-24   846  16  2020      1     Friday 2020-01-24\n",
              "2020-01-25  1320  23  2020      1   Saturday 2020-01-25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aNZNT6xSZsh",
        "colab_type": "code",
        "outputId": "fd838d8c-da77-4baf-fcf0-707f365facb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import fbprophet\n",
        "global_prophet = fbprophet.Prophet(changepoint_prior_scale=0.15)\n",
        "global_prophet.fit(global_data)\n"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f19ce379f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBVpjjQOSlig",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "global_forecast = global_prophet.make_future_dataframe(periods=365 * 2, freq='D')\n",
        "global_forecast = global_prophet.predict(global_forecast)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lknvn7c7TB47",
        "colab_type": "code",
        "outputId": "9edd20ab-3462-4a4e-b0e8-f871bf2823ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "global_prophet.plot(global_forecast, xlabel = 'Date', ylabel = 'Daily Confirmed Cases')\n",
        "plt.title('Daily Confirmed Cases Without Intervention');"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ytETTlAf91FeEhroskbwpEIuIiMg+sxyHtOXQkTDYHs2QshxCfq/6hGVYUiAW\nERGRvCVNm7ZYhs3daWxcfHioCPmoDClSyPCl714RERHZI9d1iWVs3u1M0JW28Hk8jA77taucjBgK\nxCIiIrJb0bRFR9KgJZYhbTmUB7SZhoxMCsQiIiKSk7Ec2hMm21qiRFImfq+HyqCPKrVEyAim724R\nEZES17dKxLaeFB0Jk+6eDI31DmPKA3h0g5yUAAViERGREuO4LpGUScKwMGyX7dEMluNSHvBRUx7A\nk/FRHvANdZkiB4wCsYiISAlJGDYbI0na4hkCPi8eD4wK6QY5KW0KxCIiIiUgYdhsi6bZ1pPG7/PQ\nUKmNM0T6eIe6gF194xvfYPbs2Zx33nlcdtllvPPOOwB88MEHXHzxxcyYMYOLL76YTZs25T6mmI6J\niIgUE9d16UmbbOxMsHpbN62xDLXlAarDgaEuTaSoFFUgXrx4MU888QSPPfYYc+fOZf78+QAsXLiQ\nyy67jOXLl3PZZZexYMGC3McU0zEREZFiYDkOrbE0f9rczZrtPWyNZqgOB6gpC2gXOZHdKKpAXFVV\nlXs7Ho/j8Xjo7Ozk7bffZtasWQDMmjWLt99+m0gkUlTHREREhlLasulIGmzpTvHKlh7eaktQHvBS\nXxGipiygHmGRPSi6HuJbb72Vl19+Gdd1uf/++2ltbWXs2LH4fNm7XX0+Hw0NDbS2tuK6btEcq62t\nPdBDJSIiJcx1XXoyFjvjBpGUSdywAfB5PVQEfYwOF92veJGiVXT/t/zoRz8C4LHHHuOOO+7gH//x\nH4e4osHX2dmBaVpDXQYAXV2dQ13CkCr16++jccjSOGgMhsP1Z4OwzfvdBjHTJuj1EPZ7CPqyL/q6\nQDy1f58jFu3Z/0JHAI1DVqHGIZqxqbSClBuxgpx/V4GAn7q6ygGPF10g7nPeeeexYMECxo0bR1tb\nG7Zt4/P5sG2b9vZ2GhsbcV23aI7ti7q6MTiOW6CR23cNDWOHuoQhVerX30fjkKVx0BgU+/Vv6U6x\nOZaguqaCxgKuFVxbW1ewcw8nGoesQoyDN21SVx2mobp80M/9sc+1l5ahoukhTiQStLa25t5/8cUX\nGT16NHV1dRxzzDEsW7YMgGXLlnHMMcdQW1tbVMdEREQKqStlsn5HjL92JKgrD1KmjTNEBo3Hdd2i\nmKrs6OjgG9/4BqlUCq/Xy+jRo7nlllv4u7/7OzZu3Mi8efOIRqOMGjWKxYsXc/jhhwMU1bF8dXbG\ni2aGuL29rehnQwqp1K+/j8YhS+OgMSjG649nLN7vStEWz1AW8FEZ9BV8O+VIpFMzo2gc+hRqHLrT\nJodUhzn0AM0Q76llomgCcb3/yYYAACAASURBVClRIC4epX79fTQOWRoHjUExXb9hO2ztTvFBd5qQ\nz8uoA3iTnIJglsYhqxQCcdH2EIuIiJQa13XZmTDYEcvQkTLB9VBXrrWDRQpNgVhERKQIxDIWGzoS\ndKUtygM+baIhcgApEIuIiAwhy3HYFk3zXmeKMr+X+orgUJckUnIUiEVERIaA5Ths7kqxtSeD5brU\najc5kSGjQCwiInIAua5Le8LgrzsT2K5LdVhBWGSoKRCLiIgcAI7r0hbPsKkrTcKwqC4LEPAVzXYA\nIiVNgVhERKSAXNelK23xfiRJd8piVNhPfWVoqMsSkV0oEIuIiBSA7bhEMxZbe9K0xzOUB/3UV+qG\nOZFipEAsIiIySDKWQ3fKZEc8QyRl4bguQb9XM8IiRU6BWEREZD+4rktH0mBH3KA9buAC5QEf1WV+\nrSMsMkx8okC8atUqvF4vJ5xwwmDXIyIiMiy4rkskZfJuZ5K4YVPm91KrXeVEhqW8bm+9/PLLee21\n1wD4+c9/zo033si3v/1tli5dWtDiREREio1hO7RE06zZ3sO61hgA9RVBKkOaERYZrvKaIX733XeZ\nOHEiAL/97W958MEHqaio4NJLL+W6664raIEiIiLFIG3ZbO1Js7UnjeNCZdCnXeVERoi8ArHjOHg8\nHrZs2YLrunz6058GoKenp6DFiYiIDLVYxqI1lmFrTxqfx0NNmdoiREaavALxpEmTWLRoETt37uT0\n008HYMuWLdTU1BS0OBERkaESz1hsj2XY2pMi4FV/sMhIllcP8e23386oUaM46qijuP766wF4//33\nueKKKwpanIiIyIGWtmz+1pFg1bYedsQz1JUHqdassMiIltcMcU1NDTfeeGO/x0455ZRC1CMiIjIk\nDNthW8wgnuzBdaFOM8IiJSOvGWLDMLjrrruYPn06kyZNAuCll17i3//93wtanIiISCHZjktbPMP6\nHTFe2tzF5h6DsoBPM8IiJSavQHzbbbexYcMGlixZgqf3B8SRRx7Jww8/XNDiREREBpPrusQyFtuj\naV7bHuUPm7r4y444ccOmtixAddhH0JfXr0YRGUHyapl4/vnnefbZZykvL8frzf6gGDt2LG1tbQUt\nTkREZDB0p03a4wat8QyW4+IFygI+asv8uYkeESldeQXiQCCAbdv9HotEIlRXVxekKBERkf1l2g7R\njEUkZbKpK0XQ76Uq6MfnVQAWkf7yel1o5syZ3HLLLWzduhWA9vZ2Fi1axNlnn13Q4kRERPZVPGPx\n151x/rS5iz/viNES610pIhxQGBaR3corEH/rW9/ioIMOYvbs2USjUWbMmEFDQwPf/OY3C12fiIjI\nXjmuSzRtsa4lyqpt3bQlDGrKAgrCIpKXvFomgsEg8+fPZ/78+UQiEWpqatRzJSIiRSGatvhLW4y0\n5RD2e6mvCA11SSIyzOQViN977z2qq6sZM2YMoVCIe+65B6/Xy9VXX01ZWVmhaxQREenHdV0Shs2m\n7hQ74gaVQR9jKoJDXZaIDFN5tUzceOONRKNRABYvXsyaNWt44403WLBgQUGLExER2ZXtuLTG0qza\n2sPqbT1EUhZ15QHKAr6hLk1EhrG8Zoi3b9/O4Ycfjuu6PPfcczz11FOEw2GmT59e6PpEREQAiKRM\n3u1MEM/YVIX8mhEWkUGTVyAOhULE43E2btxIY2MjtbW1WJZFJpMpdH0iIlLCMpZDV8qgNWbQkTSo\nVBAWkQLIKxDPmjWLK6+8kkQiweWXXw7A22+/zUEHHVTQ4kREpPQ4rktP2mJbNE173MAFKnp7hLWd\nsogUQl6BeP78+bz00kv4/X6+8IUvAODxePjOd75T0OJERKR0WI5DJGny144kpu0Q9HmpKw9oVSMR\nKbi8AjHAiSee2O/9z372s4NejIiIlB7XdYmkTN5qT2DYDqNDfkaH8/71JCKy3/L6iWNZFr/5zW9Y\ns2YNXV1duK6bO/brX/+6YMWJiMjIZTsuHQmDdyNJ0pbDKAVhERkieS27dvvtt/P//t//o7m5mbfe\neoszzjiDzs7OXPuEiIhIvtKWzabuJC9t7uLN9jghn5f6iiAhf16/kkREBl1eP32effZZfvGLX3Dl\nlVfi8/m48soruffee1m9evWgFdLV1cXXvvY1ZsyYwTnnnMP1119PJBIB4I033mD27NnMmDGDuXPn\n0tnZmfu4YjomIiIDM2yHzd0pXtnSzQeRdG7FiKCCsIgMsbx+CqXTaRobGwEIh8OkUimOOOII3n77\n7UErxOPxcM0117B8+XKefPJJDj74YJYsWYLjONx8880sWLCA5cuX09zczJIlSwCK6piIiOxeNG3x\nZluMlzZ3sTGSpDocoLY8gN+rm+VEpDjkFYiPOOII/vKXvwBw3HHHcc899/Czn/2MsWPHDloh1dXV\nTJkyJff+xIkTaWlp4c033yQUCtHc3AzAJZdcwjPPPANQVMdERKS/hGHzVluM/97WTVfaoqYsQF15\nEJ+CsIgUmbwC8fz58/H5sttizps3j7fffpsVK1bwT//0TwUpynEcHn74YaZNm0Zrayvjx4/PHaut\nrcVxHLq7u4vqmIiIZKUtm3c7E7y6tZuOlMmYiiCjQn6tISwiRSuv23k/97nP5d4+9NBD+dWvflWo\negD4p3/6J8rLy7n88st57rnnCvq5hkJnZwemaQ11GQB0dZV2D3SpX38fjUOWxmH/xsCwHVoTFluj\nBl6gKuTF8XjoSg5efYUWi/YMdQlFQeOQpXHIKtQ4RDM2lVaQciNWkPPvKhDwU1dXOeDxPQbi1157\njRdffJGbb775Y8eWLFnCaaedxsSJE/e/yl0sXryYzZs3s3TpUrxeL42NjbS0tOSORyIRvF4v1dXV\nRXVsX9TVjcFx3L0/8QBpaBi81pfhqNSvv4/GIUvjsO9jEDcsWqIZtsfT4A1y6LjRw7otora2bqhL\nKAoahyyNQ1YhxsGbNqmrDtNQXT7o5/7Y59rLz6Q9tkzcd999TJ48ebfHJk+ezNKlSz95ZbvxL//y\nL7z55pvce++9BIPZveqPO+440uk0a9euBeCRRx5h5syZRXdMRKSUOK5LZ9LgrbYYq7b20BrPMDoc\noFY9wiIyDO1xhvidd97hpJNO2u2xL33pS9x6662DVsi7777Lfffdx6GHHsoll1wCwEEHHcS9997L\nHXfcwcKFC8lkMkyYMIE777wTAK/XWzTHRERKQdywiGcsPuhKkzAtQn4fY7S9sogMcx53123nPqKp\nqYlXX32VcDj8sWPpdJqpU6eybt26ghY4EnV2xoumZaK9va2kXyIu9evvo3HI0jjsfgz6dpTbGk3T\nnTbx4KEi6KMs4BuiKgsnEunUS+RoHPpoHLIKNQ7daZNDqsMceoBaJvbUQ7zHlonDDz+cl156abfH\nXnrpJQ4//PD9q05ERIpWxnJoi2VYva2bN9vjWI5LfUWIMRXBERmGRaR07bFl4qqrrmLhwoU4jsNp\np52G1+vFcRyef/55Fi1axLx58w5UnSIicgBYjsuOWJr2hMHOhAlAVcjPmIq8FiUSERmW9vgT7pxz\nzqGjo4NbbrkF0zSprq6mu7ubQCDADTfcwKxZsw5UnSIiUkAZy6ErZfBGW5JgZYDygI869QaLSInY\n65/8c+bM4e///u9Zt24d3d3dVFdX09TURGXlwH0YIiIyPPSkTbZHM+yIZXABjwfqK4JDXZaIyAGV\n12tglZWVA642ISIiw4vruvRkLDZ1pehImoT8XmrKA3g9HiKZvDYwFREZUdQUJiJSImzHpSNp8H4k\nRdK0Cfm9mg0WEUGBWERkxDNsh7a4wQddKUzboTLkZ4yCsIhIjgKxiMgIlTRtWqJptvSkcV0YHfYT\nCOvHvojIRw34k9FxnLxO4PWq30xEpFi4rkvcsNkRz7ClO43f66E6HNB2yiIiezBgID722GPzWm7n\nnXfeGdSCRERk31mOQ0/aYnN3mq6Uic/robb3RjkREdmzAQPxCy+8kHt75cqVLF++nGuvvZbx48fT\n0tLCL37xC84444wDUqSIiOxePGOxPZZhezSN40J5wKf+YBGRfTRgIJ4wYULu7V/96lf87ne/Y9So\nUQAcdthhHHfccVx44YVcdtllha9SRERyLMchmsn2B++IGwR8HmrKNBssIvJJ5XV3RSwWI5VK5QIx\nQDqdJhaLFawwERHpz+xdLWJjJInluIT8XsZoNzkRkf2WVyA+//zzmTNnDldeeSXjxo1jx44dPPTQ\nQ5x//vmFrk9EpOSlTJuOpMkHXUlM282uFuHTDc0iIoMlr0B8880386lPfYqnn36a9vZ26uvr+cpX\nvsJFF11U6PpEREqS47rsTBhsj2boSpl4gKqwn9FhBWERkcGWVyD2er1ceumlXHrppYWuR0SkZLmu\nS8pyiGUsNnWniKVtyoO6SU5EpNDyCsSu6/Lb3/6Wp556ikgkwpNPPsmaNWvYuXMnZ511VqFrFBEZ\n0WzHpSWWYVtPmqRpA1Ae9FFfqSAsInIg5PXa249//GP+8z//k4suuojW1lYAxo0bx/3331/Q4kRE\nRrKkadMWz/Df23r4W0cCv9fDmIogYyqClAd8Q12eiEjJyGuG+NFHH+XRRx+ltraW73//+wAcdNBB\nbN26tZC1iYiMOBnLIZI02NyTJmHa4EJlyE+92iJERIZMXoHYtm0qKioAcsv7JBIJysvLC1eZiMgI\n4bgu8YzNlp4U7QkD14WqkJ8x5QrBIiLFIK+WiZNPPpnbb78dwzCAbE/xj3/8Y0499dSCFiciMpxl\nLIdNXUn+uKmLNS09dKUtasoCjKkIEvJrtQgRkWKR10/k73znO+zcuZNJkyYRi8VoamqipaWFm266\nqdD1iYgMK67r0pUyWb8jykubu3g/kmJU72zwqJBfu8mJiBShvFomKisruffee+no6KClpYXGxkbq\n6+sLXZuIyLBh2g5be9Jsi2YwbIcyv5c67SInIjIs5BWI+4TDYcaOHYvjOLS1tQEwduzYghQmIlLs\nTNshkjLpSJp0JAxsx2V0WYDR3n360SoiIkMsr5/ar7zyCt/73vdoaWnBdd3c4x6Ph3feeadgxYmI\nFBvLcWiPG2yLpokbNq4LYb+XypAfv1ezwSIiw1FegfjWW2/lG9/4BmeddRbhcLjQNYmIFBXXdYll\nbLZF07TFDRzXpTLkp7ZMLREiIiNBXoE4k8lwwQUX4PNpoXgRKQ2u65IwbVqiGdoTBmnLJujzUV2m\nG+NEREaavFaZuOqqq7j//vv7tUuIiIxEsYzFe50J/rS5m9Vbe2iNZSgL+KivCDE6rDAsIjIS5TVD\nfMYZZ3D11Vdz3333UVNT0+/YCy+8UJDCREQOBMtxiGZsohmTv7Ul8cf8+LweqkJ+/Lo5TkSkJOT1\n0/6GG26gubmZmTNnqodYRIY113VJWQ7RtElb3CCSsnBcF5/Xg+O6jNEWyiIiJSevQLxt2zYee+wx\nvF7trCQiw4/ruiQMm0jKZFN3GtNxwIXyYP+eYDeln3EiIqUor0A8ffp0Vq1axRe/+MVC1yMiMmgc\n16UzabKpK0k0Y+P1eKgK+Qj41AohIiIfyuu3gmEY/MM//APNzc3U1dX1O3bHHXcUpDARkU/CchyS\nhkMkbbCtJ0PasqkM+tUKISJygLiuS9J0iBsWsYxN3LCJZSy60iYtsQzRtE3StLEchwWnHs6h1eVD\nXXJ+gfjII4/kyCOPLHQtLF68mOXLl7N9+3aefPJJPvOZzwDwwQcfMG/ePLq7u6murmbx4sUceuih\nRXdMRIZG0rSJpS06UiadSQPTdvF5PFSEfFSFNBssIvJJOb1LUGZiGWIZKxtuewNuPGMTMyy6Uibb\nYxl60h8+5gywMFnI52F0OEB5wEtVyD/g8w40j7uXtdRs2+bRRx9l9uzZBIOFnWFZu3YtEyZM4Ctf\n+QpLly7NBeIrrriCCy+8kHPPPZfHH3+c3/3udzz44INFdyxfnZ1xnCL5Dmhvb6OhoXS33y716+8z\nHMfBcV260xY7YhlaYhk8ZHeMCwd8n3jHuEikk9raur0/cQQr9TEo9evvo3HIGqnjkDBstkfT9KQt\nYobdbyb3w7ctIkmT1riBtYfMEvB6qA77GT8qRE04QGXvRERV0Edl0J99P+inMuijOuyntjyQu2+j\nO21ySHX4gMwQe70e6uoqBzy+10AM0NzczNq1awe1sD2ZNm1aLhB3dnYyY8YMVq9ejc/nw7ZtpkyZ\nwrPPPovrukVzrLa2Nu/rUyAuHqV+/X2Gyzi4rks0Y9Gdttjcnca0HYJ+L1VB36DsGDdSf/nti1If\ng1K//j4ah6xiHwfXdUlbTm+QtelMmrTGMnSnzd42hexsbbw34PbN7Br27jNIyOehsjfMVoX8jA77\nGV8VIuwaNFSPygbdUDboVgV9VIb8hHyeT/zzt5gCcV6vJZ566qm8+OKLTJs2bdAKy1draytjx47N\n7ZLn8/loaGigtbUV13WL5ti+BeIOTNMatDHaH11dnUNdwpAq9evvU+zjYDkubQmT7TGTjOPgBSqD\n2ZlgKwNdicH5PLFoz+CcaBgr9TEo9evvo3HIOpDjkF0S0iVhOiRMm7jpkDAcEqZDV9qmLWkSMxzi\nhpM9ZtokTAfL2f35yvweKgLe3v981Ie9HDbKT0WgjFFBH+Mq/IwO+agMeqnsfU7At/tgG4saVI3y\nAHb2P9eADCQzkNyPa45mbCqtIOVGbD/Okp9AwL//gTiTyXDDDTfQ1NTEuHHj+v0loJvq9l1d3Zii\nmSEGhsXMYCGV+vX3KbZxSFs2PWmLtniGSNLCJsDYej9BX2GXRivm2aADpdTHoNSvv4/GIeuTjkPC\nsIlmLGKZ3raE3L/ZWdtYJrsUZEs0TU9vb+6eosGY8gA1ZQEqw0HGjvIxKpRtQ9h1Rre6d0a3uizw\niVvHBlKI7wdv2qSuOkzDAZoh3pO8AvFnPvOZXD/vgdbY2EhbWxu2befaFNrb22lsbMR13aI5JiL7\nz7QdejIW27rTdKZMXKA84GNUOLt7nIjIUDEsh85UtiUh23v78b7bvpvOOpMmPZmBXwkOeD1UBn3U\nlAU4pKaM6rCfyqCfUaH+fbdVvf+ODvsJ+bVOeiHlFYivv/76QtcxoLq6Oo455hiWLVvGueeey7Jl\nyzjmmGNyLQrFdExE9p3ruvRkLHbEM7REM7gulAV8WiZNRArio3230YyV673tSVu5Xtt4xqY7lSFl\nbyeescgM0Hcb9nuzM7W9s7TjKoMcXV+RnakN+we971YKY8Cb6tasWcPkyZMBePXVVwc8wdSpUwet\nmB/+8Ic8++yzdHR0UFNTQ3V1NU899RQbN25k3rx5RKNRRo0axeLFizn88MMBiupYvnRTXfEo9evv\nMxTj4LguOxMG70dSJEyboM9LVciXu/t4KBT7DTQHQqmPQalff5/hMg4DrXfbt3JCRyIbdKOZD28o\nixv2gKsmlPm9/WZngzjUVZVlV0kI+agJBxg/qjfo9j5W6DauYlCo74diuqluwEA8a9Ysli1bBjDg\nzXQej4cXXnhhEMosLQrExaPUr7/PgRqHvlUiWmMZdsQNbMelMuQnXCQvBQ6XEFBIpT4GpX79fYZq\nHGzHpT1hEO0Nrh/tu+0LvB3JD4Punn6dhv1exleFGB3O9tt+tO+279+asgDjq0Ifa0vQ90NWKQTi\nAVsm7rzzztzbL7744uBWJSIlxXIcOpMmW7pT9GQsgj4vo0LqCxYZ6QzLIWbYdCSND2dq+y0F9uGs\nbSxjEUmZDNCZQMDrybUe1JYF+OKnqhkd/nC9275+211neMN+r1oTJC8DBuKvfOUrvP766wCcccYZ\nPPvsswesKBEZ/vpmg7f2pGlPmLiuS0XQR31FaKhLE5F99NG+274Q254w2BHLEN3H9W4/2nc7tjLI\nEbXl1JUHaOztve07pr5bORAGDMSjRo1ixYoVfPrTn2bnzp1s3bp1t887+OCDC1aciAw/GcuhO23y\nQVeKuGER8vuoKfMPaW+wiHzoo323fUF21x3LtkViRMydxHI7mQ3cdxv2e3OtCFUhHweNDmff3mW2\ntrYs23s7OlQ6fbcyvAwYiG+99VZuu+02WlpacByH008//WPP8Xg8vPPOOwUtUESKn+u62V+i0TQ7\n4tmVIjQbLHLgxDIWO+JG9oay3ax32zdru7N3RnegtgQArwdqwj4OGl3GoTVl/WZpqz6y7m1tWYDa\nMr9mbmXYGzAQn3766bkQ3NTUxLp16w5YUSIyPNhOdsm09yNJetIWAZ+X2rKAfjmK7Ie+vttYxsq2\nJMQzA/fd9j6WGmC7sl37bitDPg6vKePE3t7b3fXdVoZ8lPm9dHVFdDOZlJS81iFevXp1oesQkWHC\ndlzihsXOhMH2aAbTcSnXusEiH7O79W77Zm3b40Z2RtewPjaTO1DfbcjnoSrk79d3++lQOVVBX673\nNhd0e5+jzRxE8pNXIE4mk/z0pz/lnXfeIZnsv2v1r3/964IUJiJDz3IcEoZN2nJyOy+lTAfXdfF5\nPVopQkpGX9/tR2dmd+27jRsWbXGDtr7WhT303XrIbsXbt2nDwbvpu60M+hhTkQ26o0KF37ZcpJTl\nFYi//e1vYxgGZ555JmVlZYWuSUSGUNqySRg2XSmTbT0ZHFxwIej3EvZ7KVO/oIwQpu3QljBo3yXA\ntnVHcXypfn23sUx2Rjdm2AOey+eBqpCfMeUBDqkO91vvNrcdb++sbWXQR11ZgKBmb0WKRl6BeN26\ndaxatYpgUC+Jiow0aSv7cm4ikmRHwiDZ+0vf7/MwKqwZYBke+vpu+2Zr2+IZ2hJGtvd2l3Vud93J\nLJ1n3+2Y8iDH1FcwrjI7U1sZ8jFqN323+kNRZPjKKxAfddRR7Nixg0996lOFrkdEDoBYxqIzadAa\nM0haNj3dSWqrw1QE1QssQ6uv7za2m13K+gfdvnC7577bj653O64yyKfrynOrJdSXBxjX25JQFfJh\nJXoYV19/gK9aRIZaXoH4C1/4Atdccw0XXHABY8aM6Xfsf/yP/1GQwkRkcGUsh86kQUssQ0/awuf1\nUBHIzn55035qygNDXaKMQJbj0pk0BlzvNteqEM/k2hIG6rsFKA94GVcZyq53Oyq8293JKoN+6isC\nNFaG9rktIZJWG4NIKcorEK9du5axY8fy8ssv93vc4/EoEIsUKdd1SZg2PensjT7daTO3PrBmgeWT\nMm2HWMZmRzzDzoRBtHeGdtfZ2tzsbsZmZ9JgoHzb13dbGfTRUBlk6qeqczO3u+u7rQr6qAj61Jog\nIoMur0D80EMPFboOERkEaSsbgDuSJp1JE9Nx8LhQFvRRV64QLP3tut5t37Jg77dHiW9M5ta7jfYe\ni3+CvttDa3w0VAQZWxli1K4zub0bO4TVdysiRSKvQAzQ09PDihUraGtrY+zYsZx66qmMHj26kLWJ\nyF44rks8kw0tfbPAAKHevkmfN+//xWUY21Pfbd9NZK2xDDsTJnHDys3q5rvebWNlkMpd+m4rgz7G\nVgazrQu9j4V8HoVbERm28l5l4tprr+Xwww9n/PjxrFixgttuu4377ruPpqamQtcoIrsw7OxC/zti\n2ZesLcfF44HyQHZxfoWS4c91szsAdiTM3Wzc8GHfbV/Q7Uiae+y79XronakNctAu6932zeju+m/I\nTHDIuHp9H4lISckrEN92220sXLiQs88+O/fY008/zQ9/+EN+97vfFaw4EcmGo7hh05EwaEsYJHqX\nRQv6vVRpY4xhwXJcYhmLaMZiR8xgZ9L42O5kudaF3seT5u5bE3btu60M+jl6TAUNlUGqBui7HRXy\nUxbw4s0z4EYiaYVhESk5eQXiTZs2ceaZZ/Z7bMaMGSxcuLAgRYmUMtN2SJp27yxwdmtX28nuDKct\nkodexnJys7Z9PbbZoJthZ9L8sGVhH/pu+8JrfXmQw2qys7fjKoPUVwYZ1TeDq75bEZGCySsQH3LI\nITz11FOcc845uceeeeYZDj744IIVJlIqLMchaTgkDIsdcYOutAku0NsGMTrsz3t2T/LX13e7awvC\njs4Ebqebm7WNZixaYhk6k+Ze17v1eqCmLEDVAH23Vbusg9tQkZ3RDWmnMhGRopBXIJ4/fz7XXXcd\nDz30EOPHj2f79u1s3ryZpUuXFro+kRHHchzSpkMsY7EzadCRzN4IhwvlQR81ZQEF4E/ItB1aYwYd\nSSM7Q/uRvtu48WGbQks0Q8IceCterwcqgz7GVYY4eC99t1W9694GfAq4IiLDUV6B+Pjjj+e5555j\n5cqVtLe3c+qpp3LyySdTXV1d6PpEhj3TdkiZDpG0QUfCJJqxshPALoQDPmrLdCPc7pi2k1vb9sOZ\nWiP32K43m+1L321VyE9NmZ9j6iuorwj267t1M3EmjKmjah/7bkVEZHjbYyBOp9Ns2bKFz3zmM4we\nPZpzzz03d2zDhg2UlZURCoUKXqTIcGLaDtHe2d/OhEmmt3/U6/VQVqIB+KN9t31v92QsWqIZIikz\n17aQX99t3wzth323lcHsTWXjqkI0lAc/0Xq3kYhJbZV+pomIlJo9BuL777+faDTK/PnzP3bsv/7r\nv6iqquKb3/xmwYoTGQ7ihkXKdOhKmXSlTBKmjetC0OelLOClMjSy1gI2bIfutJULrrlWhMyHLQo9\nvcuB9W3Za+5hSbDKoI8x5YH+fbd9mzgEPwy1jaNC1JcH1XcrIiKDbo+/qZ9++mkeeOCB3R6bM2cO\nc+bMUSCWkpK2bBJG9r9IbwB2AHoDcNjvHXYzwLbj0p22aWmL05UyPxJyrX43nUXT1h634vV6sltD\nVwX9jK8KcURt2R77bvtmeYfTeImIyMizx0Dctyvd7owdO5a2traCFCVSDDKWQ8K0SRrZGc9oxiLZ\nuwawz+MhFPBSXWQ3wO3adxszLHrSFtujmd6g27cF77713fatklAd9vOp0WHGV4UYUxHoF2z7ZnLV\ndysiIsPRHgNxWVkZNks53wAAIABJREFUra2tNDY2fuxYS0sLZWVlBStM5EByXZeEaZMyHRKmRVfS\nyi5/BnjwEPR5CPq9B3QN4N313cYy2R7b7l16b/dnvdu+vlufneGIsbXUlwe03q2IiJScPQbik08+\nmX/5/+3deZDdVZ3//+dnu0v37T3pJJ0QEhLCIkZWWRRBcIkKCYhT4EgxipSK/jEoSDEUBaOOS0C/\n6riMCzg4Iz+CA0Y0CgKCozWOAxQiqIzDTkh3utPr7b7bZzu/Pz7dl0SS0B16ud339aiiSPfndt9z\n3qE/eXHy/pzz//4fN9xww8uuffWrX+W0006bsYGJzJTYGEpBTBDH7Bj16WeM/mJAEMdgwLGtaW99\nMMYwXA6TADuxQrt73+14qB0uJ0F3qBzsc79bSFoTlubSdDR4LMulaOpoSB4i28spZcua0rRk9t/H\nPDg4QHt7y7TMVUREZL7Z75+Sl112Geeffz4bN27kbW97G4sXL2bXrl3ce++9jI2NsWXLltkap8gB\nmdjyrBiEFIKYQiVksBwSGYMFjIxU6HQDcikHx578w29RnBynPFwO2JGvMFwO9tjvdvfdEkYrSetC\naR+rt7v33TanXV6zJMeiBq96NO/uK7tNKZdc2qHRc3Rks4iIyDTZbwJYvHgxW7du5Xvf+x6/+c1v\nGB4eprW1lTe/+c184AMfoKVFK0pSO/woOfCiFEaMlMPqkboAGHAdG8+xaE671TBplZPTwvKVcI/V\n2uFSQPdohaFSOKX9bl3bGg+uL+13u7IlQ1M6ecisYzzoNqfVdysiIlIrXnFJrKWlhY9//OOzMRaR\nSTHGUPAjimGy8psvhYyN9/9OPFRWiQyVKKYSRIwFMWOVkPweQTeoruZWouf2+j62Ba0Zb4/9bg9p\n23O3hJZM0pLQ0eCp71ZERGSeWlgbpMqCEsWGfCXkL/0FekYr7BzzGSj61QfJCn5UfRBupJzse7uv\ntgQAC2hKO3Q1pelqSpNLu3hxwOKW3B59t01pl5a0S2cuhau2BBERkQVPgVimxR/+8HtuvvlGnn/+\nWVw3RRj6HHzwat7//kt43euOASCMk+A6XA7ZVfB5dqhEX8FnqBQmh1qUAwaLAcPj7Q6jlYhiELG3\nR8tsiz16bBc1eqxf2kR71t1jq7Dd2xMa9tJ3mzxM1jELFRIREZFapUAsk7J74G1tbSfGon+shEk3\nMepH9OWLxKkG4swawqbFxOkcD9LAD/+/P9L6QJ5iZFMIor1+b2f8obIGL/knl3JY3JilOe3SkvFY\nkkvRlnFfevAs45JyLCzLwrEsLCtZ/Z0IzrZlYZHsFhEbU/1Yi70iIiKyN5MKxENDQ7S1tc30WOaN\nZ599lquuuqr6kOHmzZtZtWrVXA9rWtxxx21s3Xo7vh8Qhj6tre0MVAxPFFzKy19H8JoziFONGC+z\nz+9hV8awy3lsv4BdHKTNb+SkQ9eRS7k0pGxynktzxmF1a5alTWkaPBuwiA2kHQt7PLmGcUwUg2OD\nTRKAwzgmiA0pJ3lNEEGMIYwMrg0YCA1ExlAOI1zLohLHGEOyrdruDAyXI0zRTwK1IUnWu13HsrCt\nZB9fz0mODLas5GAO7fIgIiKyMEwqEL/5zW/m5JNPZtOmTZxxxhmkUrN3OEEtuu666/jbv/1bNm3a\nxJ133sm1117Lv/3bv831sKbsjjtu44c/3IIxhjD0KcUuz4+GBC3LCFu6CFuWETR3ETe0AmAXh0j3\nPflS2PWLWEERu5L82g6K2OUx7KBYfQ/Xdfnni/+NY485aq6muYfYmD1+vWOnT0dHsluKgWpwDsfP\nJo5jQyWOKfpxdYU7jA3FKCKKzUvL0rtlY9e2scdXrV3HxrMVnkVERGrZpALx/fffz7Zt2/jud7/L\ntddey9vf/nY2bdrE8ccfP9PjqzkDAwP8+c9/5l//9V8BOOuss/jMZz7D4OAg7e3tczy6PU2s9qZS\naQCGhwexvQxlY1O0G9kxWiFsXY2/aA1By/Jq8AWwwgpufifp3v/FG3qB1K6n8Ia2Y+21o/clXV3L\nyeVW7rWHuBbsvr2ZbVlkXZtc+sA6h4wx1cMzgjjGjwyVINn9IohiYgPFINmbOIyS1Wkz0d5hkn9b\n4yvNadfGsaiuQouIiMjsmVQSaG9v56KLLuKiiy7imWee4c477+TKK6/Esiw2btzIe97zHpYvXz7T\nY60JPT09LFmyBMdxAHAch87OTnp6emoqEP/H7bdx+f3bCVdswrgZjJchXpMGx9vzhSbGHekm3fsE\n7kgPXr4Hd6QbpzC41/C7aNEiVq5cBSQBe18P0NUDy7JIu0nATjMeZLPeXl8bxYbYGCJj8MNkS7jY\nGILx7eGGSgF+FJOvhNXWDQvAQMq1kx5o28K1LFzH0r7FIiIi02jKS2P9/f309/dTKBQ48sgj6e3t\n5dxzz+WSSy7hQx/60EyMccEZGOgnCMIZfY+77tqGba/DHd2FFVawwjJ2UMYKK9hBCbs4hFsYxCn0\nYwell319V9dyXNetHr7i+wEbNryTd71r437ft6+vd0bmM1OGhgbm7L0tIDX+T5MHeBCMt2qEcRKW\nS2HEaDnpgS7HyYp0MUw+xoCxDDYWWdfCexVBeTQ/Mk2zmt9UB9Wg3uc/QXVIqA6JmapDvhKRC1M0\n+KMz8v1353kuHR25fV6fVCB+8skn+clPfsK2bdvIZrOcc8453HnnnSxduhSAj370o2zcuLEuAvGy\nZcvo7e0liiIcxyGKIvr6+li2bNmkv0dHxyLieP+tB6/Wu955Fo985rpJvXblyoNxXYfW1nYOOWQN\nZ599Tl2t9HZ2LpnrIUxJbAxhbJKT+cKYoh/RV/Ap+ON9zdZLrc2ubWGTrCo71v5Ds7afS6gOqkG9\nz3+C6pBQHRIzUQe7HNDRmqGztWHav/fL3usVnuWZVCC+8MILede73sVXv/pV1q9f/7LrK1as4O/+\n7u8ObITzTEdHB0cccQTbtm1j06ZNbNu2jSOOOKKm2iUAzjvvfICX9RBPtDhMhN83vvFNnH76mXM5\nVJki27JIORYpxyaXAhpgZWuW2BiMSQJzJYwphckJfWFsKAURldgwUg6JjMH6qx01RsoRbiXEtS1c\nO/neIiIi9cIyxrziUmUQBHje3nsj69HTTz/NVVddRT6fp7m5mc2bN3PIIYdM+usHBsZmfIV4svr6\neufdCul0qrf5G2MoBkn/ctLTDH4Y09PXR66lnVIQUQhiiuM7ajjjO2Q0eE5dnNqng1pUg3qf/wTV\nIaE6JGaqDsPlgINbM6yapRXiA2qZuP322yf1Bu95z3umPqp5bs2aNfzHf/zHXA9DZMosy6Ix5bzs\n83YpReeixurH5TCiGCTtGGNBSH8hwA/jpB3DgDu+D3TKscm6NpYe8hMRkXlsn4H4zjvvfMUvtiyr\nLgOxyEKXcR0yrkP7xK4Zi5KDUop+TCVKWjEA8pWQ/mJQ7b6wxvuUG1OOdsIQEZF5Y5+B+N///d9n\ncxwiUuNc26Y5k/QWL2586XCechjhh4YYQ8GPGC6H9I35mPFH+2wsMp5NRivJIiJSo/YZiI0x1T+8\n4r8+8nY3tq2Hb0TqWbKanPy6NeOxvBmOWJzshFEJYwaKPgPFkMFSkGwVR/I8n+fYeE7So+za2ltZ\nRETmzj4D8XHHHccjjzwCwJFHHvmylZ2JwPzEE0/M7AhFZN7ZfSeMprTLqjaqD/LlKxFBFDNaCclX\nIvwoTraMMwYTJ/3JGdcm5dg68lpERGbFPgPxz372s+qvf/nLX87KYERk4bKtZBW4PZv8rdKSXLp6\nLTamGoxH/ZDBYsiYnwRnx7bIpRwday0iIjNmn4F494Mm6uVYZhGZG7ZlVR/k62hIsaqV8dXkkOFS\nwAsjFYJKiEWyy4VlWTR4dnW/ZK0ki4jIqzHpo5t/+ctf8tBDDzE0NMTuWxdff/31MzIwEalvtmXR\nmvFozXgc3JqlEEQYA6UwJoxiukcrjPrJbhfR+L7eE2HZsy2ynq1VZRERmZRJBeKvf/3rbNmyhXe+\n853cfffdnH/++Wzbto13vvOdMz0+EREsyyKXSm5XTeOdFl3Nmer1MI6phIZyGFEJk9aL3oJPvhJi\nYdGQckg7lna5EBGRvZpUIL7jjjv43ve+x7p16/jRj37E1VdfzVlnncU3v/nNmR6fiMgrcm0bN8Ue\nh46s7Wig4EcMlgL6Cj4DxYCJPJxxHdKuXRen74mIyCubVCDO5/OsW7cOAM/zCIKA9evX89BDD83o\n4EREDpRlWeTSLrm0y8rWLH4UUw5iSmHEztEKo36yw4UxSQ9y2rWJX/kkexERWYAmFYhXrlzJk08+\nyaGHHsqhhx7KrbfeSnNzMy0tLTM9PhGRaZFykofwmnGrO1xEcXKYyGDZZ6QU0luJoehjGWhIOTpM\nRESkTkwqEF922WUMDw8DcPnll3PFFVdQLBa57rrrZnRwIiIzybEtmjMuzRkXWmGpXaS5vZXBUsDO\n0ZfaLCYWji0LMMmhIik3OX1PRETmv0kF4tNOO63669e97nXce++9MzYgEZG5YlkWWc9hueewvDlT\nbbMI4pjYgOdYVMKYfDlkqBzQX/TBQMa1aUw5Wk0WEZmnJhWIn3rqKR5++GFGRkZoaWnh+OOPZ+3a\ntTM9NhGROTXRZvHXJlouSkHEUCmgvxjQX/AxJNvFTbRbiIjI/LDfQGyM4eqrr+bHP/4xS5cupbOz\nk97eXvr6+ti0aROf+9zntCIiInUr6zlkPYeu5gxRnBwkUvAjukfL9Bd8LCA1fgx1Stu+iYjUrP0G\n4ttuu40HH3yQ2267jfXr11c//9hjj3H55ZezZcsW3vve9874IEVEap1jW7RlPdqyHitaMskx1JWA\n4XJyDPVgKQTM+OEh4NlJH7L2RxYRmXv7DcR33nkn11xzzR5hGGD9+vVcffXVfPvb31YgFhHZi8aU\nQ2PKYWlT8nEUG0pBRGgMUWzYVfQZqyT7JE+E5Izr7LGXsoiIzI79BuKnn36aE044Ya/XTjjhBK68\n8soZGZSIyELj2Mm+yBM6GlIABFFMMYgYrUT0F/xqq0XGS/qQHR0eIiIy4/YbiKMoIpfL7fVaLpcj\njuMZGZSISL3wHJsWx6Yl81KrRb4c0F8KGCgERMZgAU1pl5Qe1BMRmRH7DcRhGPK73/0Os4/Tm6Io\nmpFBiYjUq4lWi2XjD+oV/IgxP+SZoRL5SojBkHYdPNsi5WgFWURkOuw3EHd0dHD11Vfv83p7e/u0\nD0hERBK7HxyytClNOYwpBRG7Cj4FPyZfCYliU22xaPB0sp6IyIHYbyC+//77Z2scIiKyH7Zl0eA5\nNHhOtf84NoaxSkQxCOktJCfrTTyg1+Dp6GkRkcma1MEcIiJSe2xr9xXkDLFJ9kIu+hE940dPA7Rm\nPVy1VoiI7JMCsYjIAmFbFq0Zj9aMR9f40dN9Yz5PDxYJjQEDjmWR9mzS6j8WEalSIBYRWaBSjs2K\nlgxdzWn8KCaIDAMln3wpZLgSEcQx1vgz065j0+BpFwsRqU8KxCIiC5xtWWRch4ybbN9GKxhjKIUx\nUWzG90EO2TnmM1QOoRjQknG1giwidUOBWESkDlnjD+lBEpKX5NIc0t7As04JpzHD88NlwtjgWFZy\nxLRrqw9ZRBYsBWIREQGSleSmlENnWwMHtWQpBhGDJZ/RSsxgKcCPYprTLmkdECIiC4wCsYiIvIxj\nWzSl3aTFgmSLt52jFV4YKbNrrELaTbZ18xxLW7uJyLynQCwiIq/Itiy6mjN0NWcYrYTsGC2TL4cM\nlkKMgYxrk/Uc9R2LyLykQCwiIlPSlHY5PJ0DIIiSE/N25MsMlUPCONneLeXaNCogi8g8oUAsIiIH\nzHNsOhpSdDSkMMZQCCKKQcxgyWfnqE80vv9xyrHJpR1stVeISA1SIBYRkWlhWRa5lEsuBZ2NKdZ1\nGMphzGg5ZKgc0DNaIR4/Wtq2LFKOTcbVASEiMvdq4lHhO++8k7PPPpsjjzySH/zgB3tcK5VKXHbZ\nZbz1rW9lw4YNPPDAAzV5TURE9mSPb+22pCnN4YtznL66nZNXtnL0smZWtWVoSjuM+RH9BZ+Bgk9/\nwacYRHM9bBGpQzWxQnzEEUfw5S9/me985zsvu3bTTTeRy+W49957ee6553jf+97HPffcQ2NjY01d\nExGR/ZvY+7jBc2jPetXP+1HMmB8RxoYXhkv0F3wgabPIeDYppybWbkRkAauJu8y6detYu3Yttv3y\n4dx1112cf/75AKxatYqjjjqKX//61zV3TUREDkzKsWnPenQ2pjh+eQsnr2zlmK5mFjV4hLGprh7n\nJx7aExGZZjWxQrw/3d3dLF++vPrxsmXL2LlzZ81dExGR6fHXq8jlMGK0khwS0lcI8MMYgMaUQ3b8\ntD0RkVdjVgLxueeeS3d3916v/fa3v8Vx6uuGNjDQTxCEcz0MAIaGBuZ6CHOq3uc/QXVIqA61XYM2\noC0LY35EOTS8MOTTHcaAwbMtMtNwvPRofmRaxjrfqQ4J1SExU3XIVyJyYYoGf3RGvv/uPM+loyO3\nz+uzEoi3bt16wF/b1dXFjh07aG9vB6Cnp4cTTzyx5q5NRUfHIuIa+mu/zs4lcz2EOVXv85+gOiRU\nh9qvQef4v48Y3+ZtpBwy5ofsHPUJYlM9JCSOzQGdpNfe3jH9g56HVIeE6pCYiTrY5YCO1gydrQ3T\n/r1f9l6v8D/LNdFDvD8bNmzgtttuA+C5557j8ccf59RTT625ayIiMrsmtnlb3pzhsEU5TlnZyvql\nTbRlPQp+SGwMg6WAgaJPVEOLECJSeyxjzJzfJbZt28b1119PPp/H8zyy2Szf+973WLt2LcVikauu\nuoonnngC27b55Cc/yVve8haAmro2FQMDYzWzQtzX11vzq0Ezqd7nP0F1SKgOC68GQRSzI1/mmcFS\nsvexa5P17H0eEDI4OKAVQVSHCapDYqbqMFwOOLg1w6pZWiHeX8tETQTieqNAXDvqff4TVIeE6rBw\na1AKInaOVsj7IbsKARaQ9ZyXhWMFoITqkFAdEvUQiGt+lwkREZFXK+s5rG5P/tAtBhGlIOLFkTKD\npRCDwcIil6qvB7xF5CUKxCIiUlcmtnXraEgRxYaCHzFcDugerTBQCrFKQfXBPBGpDwrEIiJStxzb\nojnj0pxxWdma5UmKpJuy9BV8BooBE12FGdfGsiw8x9LJeSILkAKxiIjIuJa0Q2drlpWtWWKTrB4X\ng4i+go+FRb4SkK+EWIDn2DR4zqve+1hE5p4CsYiIyF7YlkVT2qUp7bIkl65+fqIHuXfMZ1fBxxho\nzbpT3u9YRGqHArGIiMgU7N6D7EcxzwyWeDFfJuXY5FIOjlaMReYdBWIREZEDlHJsDl/cyIrmNDtG\nK+wcrRDGhpRj06hwLDJvKBCLiIi8Srm0y2Fpl7XtDYz6ITvHKvTkK1hYNKYdPYgnUuMUiEVERKaJ\nY1u0ZjxaMx6rWxvozpfpGaswUgpIuw5NaUe9xiI1SIFYRERkBqRdm9XtDaxub2C0ErJ9pEzPaAWA\nprRL2tWqsUitUCAWERGZYU1plyM7c6xpb2Cg6PP8cJn+gp/sg5x21WssMscUiEVERGZJ2rXpas6w\nrClNwY/oHq2wI18hNoZc2iWjVWOROaFALCIiMsssyyKXdlmXdlndlmWwFPDMYImBgo8BXMci4zoK\nyCKzRIFYRERkDnmOzZJcmiW5NEEUM+ZHDBR9BksB/QUfSAJyLuXqVDyRGaJALCIiUiM8x6Yta9OW\n9TDjR0dHxtA75tMzvsexY1ukHJusZ2NrxwqRaaFALCIiUoMm2ioAWjIeh7RnGfMjhksB+UpIfzHA\nGMi4dvV1InJg9BMkIiIyD7i2TWvGpjXjARDGMQU/4smBIn1jFVoynrZyEzlA+skRERGZh1zbpiXj\ncWxXM+uXNuFHMQNFH2PMXA9NZN7RCrGIiMg8ZlsWS3JpOho8nhoosWO0jAUYA5YFDV5ydLT2OhbZ\nNwViERGRBcC1bQ5f3MjK1gx+FONHhiCK2Tnqk6+EROMrxynHpimlI6RFdqdALCIisoA0eA4NnlP9\neHlzBmMM5TCmEET0jFboHfPxnOSUPO1UIaJALCIisuBZlkXWc8h6DosaUqxuC3lxpEz3aAXPtmlM\nOWqpkLqmQCwiIlJncimXwxfnOLg1y//uKpCvhNU9jpu0hZvUIf1XLyIiUqeynsMxXc2EcUy+EjFS\nDtg+UmGwFBEXfCwLGlMuacdSz7EsaArEIiIidc61bdqzNu1Zj1WtWZ73yrR1NDPmR+zIlxkshRgA\nA42ppPVCZCFRIBYREZEqy7Jo8JI9jlsyHsubM8TG4EcxpSDmf3cV2FXwSTs2TWntViELgwKxiIiI\n7JdtWWRch4zrcNJBLeQryUN5PaM+rpM8sKe2CpnPFIhFRERk0izLemn1uCUgXw7pK/gMlgIA2rOe\ngrHMOwrEIiIickBaMx6tGY+VrVmCKObpwRLbR0o4tk3GtXFti5RWjmUeUCAWERGRV81zkpPyljal\nKPoR/UWfSmSSB/LGT8mD5DjpjOvgju97bFvJ14rMJQViERERmTYTq8ZdzRmA6gN5BT8CoBTEDJdD\nSmGEBVTCmJFKCAYswHVsUo5F2rV1ip7MGgViERERmTG7P5A3YUXLS9eNMZTCmDg2jFZCymHMYClk\npBwSxQbLgrRjk/FeWlUWmW4KxCIiIjJnkm3ekrCcGz8lbzXJyvJYJWLMDxkoBQyXQ4IoxoyvJGc8\nh6ynVWSZHjXRtPOpT32KDRs2sHHjRi644AIef/zx6rX+/n4uvvhi3v72t7Nx40b+8Ic/1OQ1ERER\nmT62ZdGccelqzvDaJU2cenAbbzy4jeOXN3PE4kZyns1wKWSg4OOH8VwPV+a5mgjEb3rTm/jpT3/K\nT37yEz784Q/z8Y9/vHrtS1/6Escffzy/+MUvuPbaa/nkJz9Zbc6vpWsiIiIys1JOcmDIsuYM65c1\n88aDW1ndliWMDbvGKgwUfSoKx3IAaiIQv/nNb8bzPACOPvpodu7cSRwn/0HffffdXHDBBQAcf/zx\npFKp6gpyLV0TERGR2eU5NqvbGzjpoBaOW97C4YsaCeOY/oLPcDnQyrFMWs31EN9yyy2cfvrp2LbN\n0NAQxhja29ur15ctW8bOnTs56KCDauba+vXrpzTHgYF+giCc0tfMlKGhgbkewpyq9/lPUB0SqoNq\nUO/znzBf6+ACh6QNI0TkKxF9IyGV0GBItntr9CxSU9jibTQ/MmNjnU9mqg75SkQuTNHgj87I99+d\n57l0dOT2eX1WAvG5555Ld3f3Xq/99re/xXGSZvqf/exn/PSnP+WWW26ZjWHNmY6ORcRx7bRadHYu\nmeshzKl6n/8E1SGhOqgG9T7/CfO5DkvH/22MoRzGlMOYYhDx/HCJUhjjWBbNaRdnErtWtLd3zOxg\n54mZqINdDuhozdDZ2jDt3/tl7/UKv9ezEoi3bt36iq+59957+fKXv8zNN9/MokWLAGhrawNgcHCw\nujLb09PD0qVLa+qaiIiI1B7Lssh6DlnPoS3r0dWUZsyP6B2rsCNfIYgNuVRyXepbTfQQP/DAA3z+\n85/npptuYsWKFXtc27BhA1u2bAHg4Ycfplwuc9RRR9XcNREREaltlmXRlHZZ29HIqavaOHpZE7YF\n/QWffu1WUdcsUwPbJJx00kl4nrdHf+7NN99MW1sbu3bt4pOf/CTd3d2k02k+9alPceyxxwLU1LWp\nGBgYq5mWib6+3nn912KvVr3Pf4LqkFAdVIN6n/+EeqvDWCUkXwl5brhEKYixrWRP5LGRIbVMAIOD\nAzNSh+FywMGtGVbNUsvE/nqIayIQ1xsF4tpR7/OfoDokVAfVoN7nP6Fe62CMoRBEDBYDXsyX6dk1\nyKKONprTbl0fAFIPgbjmdpkQERERmQuWZZFLueRSLitbszzrlPDTaXaMlDFAc9rFcyysOg7HC5UC\nsYiIiMheNHoOqxc1sqo1y0DR5/nhMqOVMDk7Gkg7dvW4aZnf9LsoIiIish9p16arOUNXc4Ywjhnz\nI0p+RO/4w3iOPflt3KQ2KRCLiIiITJJr27RmbFrHj5DOl0P6iz7bR8qEsaEl4+JN4fAPqQ0KxCIi\nIiIHqDnj0pxxWdmaoWfU59mhImE5pDXracV4HlEgFhEREXmVXNvmoJYMS3MpduTLPD+crBi3Zj1c\nBeOap0AsIiIiMk08x2ZVWwMrWjLsyJd5bqgMQEvG1e4UNUyBWERERGSaubbNwa0NLM1leGaoSHe+\ngmtbCsY1SoFYREREZIakXZsjFuc4uDXLC8NlXsyXcMaPkNbDd7VDgVhERERkhjV4DocvbmRFS5rB\nYsBzw2X8ckjGtWnwHD2AN8cUiEVERERmycRJeF3NafrGfEbKIT1jFVzbJpdSMJ4rCsQiIiIis8y1\nJw77gINaM7yYL9OT94mNoTHlkHFt9RrPIgViERERkTmUS7kcvijHmraYwVLAC8MlBooBnmPTnHYU\njGeBArGIiIhIDfAcmyW5NEtyaQp+xDNDRfrGfFw7eQhP7RQzR4FYREREpMY0phxeu6SJsbaQ7nyF\nntEKYWxIuzZNacW36aaKioiIiNSoXMpl3SKXQ9qzjJRDduQr9BUqZF2HrHanmDYKxCIiIiI1zrVt\nOhpSdDSkGCwlp+ANFgPC2JBxbXJaNX5VVD0RERGReaQ969Ge9Yhiw1A54MWRMrsKPmnHpkkP4R0Q\nBWIRERGRecixLRY1pOjIeoxWIl4YKdFX8DEG2hs8bAXjSVMgFhEREZnHLMuiOeNyVKaJIIrZPlLm\nmaESzWmXtKvjoSdDVRIRERFZIDzH5pD2Bo7taqYSxgwVg7ke0rygQCwiIiKywLRnPU46qIWORo9d\nhQq7Cj4FPyKKzVwPrSapZUJERERkAfIcm9cuaWJ1W5ZiENOTLzNSiQjiGAw0pBwaPGeuh1kTFIhF\nREREFrBcyiUK9kf9AAAO/klEQVSXgs7GFADFIKIcxvxlV4H+gk9zxiXl1HfTgAKxiIiISB1p8JKV\n4devaKF3rMLTgyXy5ZCMa5PxHNw6POxDgVhERESkDjm2RVdzhiW5NP0Fn4FSwK6CT2wg7drYFnWz\ncqxALCIiIlLHHNtiSVOaJU1pKmHMjnyZsSAiigz5SshgOaQlNgv6mGgFYhEREREBkpXhQ9obqh9H\nseHRqMBoJSSIzYLd21iBWERERET2yrEtDmpO0b6olf6Cz1ODJfKVkFzKIbuAdqhQIBYRERGR/XJt\nm6VNSb/xSCXk8Z1jFHyftqy3IFopFt6at4iIiIjMCMuyaM0kh36sac8yWPQpBRFhbDBm/h76oRVi\nEREREZkSz7FZ1dZAc8bjmcEipSDCj2LMPD3woyZWiP/lX/6Fs88+m3POOYdNmzbx85//vHqtVCpx\n2WWX8da3vpUNGzbwwAMP1OQ1ERERkXrTnvU4fnkLbzy4jVMPbuPoZU3YFuwqVBguB/PmqOiaWCG+\n8MILufTSSwHo7e3lHe94B294wxtoaWnhpptuIpfLce+99/Lcc8/xvve9j3vuuYfGxsaauiYiIiJS\nzzzHpqMhRWvGY7gcMFQKeDFfIYxjWtIeqRrenaImRtbU1FT9dbFYxLIs4jgG4K677uL8888HYNWq\nVRx11FH8+te/rrlrIiIiIpLsTNHRkGJtRyNvWtXG+iVNlMOY/oJPwY/menh7VRMrxAC33nor3//+\n99m5cyef+9znaGtrA6C7u5vly5dXX7ds2TJ27txZc9dEREREZE+2ZdGZS9Pe4DFUCtkxUmZXoULG\ndailZ/BmJRCfe+65dHd37/Xab3/7WxzH4b3vfS/vfe97+ctf/sIVV1zBySefXA3FC83AQD9BEM71\nMAAYGhqY6yHMqXqf/wTVIaE6qAb1Pv8JqkNCdUhMZx26XGhORzw7UmFnIaQxTNPgp6ft+++L57l0\ndOT2eX1WAvHWrVsn/drDDjuMzs5OHnzwQd7+9rfT1dXFjh07aG9vB6Cnp4cTTzwRoKauTUVHxyLi\nGmoy7+xcMtdDmFP1Pv8JqkNCdVAN6n3+E1SHhOqQmM46dAKrlyfHQnuOPSs7UtivsFdyTfQQP/XU\nU9Vfb9++nSeeeIK1a9cCsGHDBm677TYAnnvuOR5//HFOPfXUmrsmIiIiIpNjWRYtGa9mtmeriR7i\nr33tazz11FO4rovjOFxzzTWsWbMGgA9+8INcddVVvPWtb8W2bT796U+Ty+Vq7pqIiIiIzE+Wmc/H\nisxTAwNjNdMy0dfXW9d/HVTv85+gOiRUB9Wg3uc/QXVIqA6JhVAH27b220NcEy0TIiIiIiJzRYFY\nREREROqaArGIiIiI1DUFYhERERGpawrEIiIiIlLXFIhFREREpK4pEIuIiIhIXVMgFhEREZG6pkAs\nIiIiInVNgVhERERE6poCsYiIiIjUNXeuB1CPbNua6yFUeZ5bU+OZbfU+/wmqQ0J1UA3qff4TVIeE\n6pBYCHV4pfFbxhgzS2MREREREak5apkQERERkbqmQCwiIiIidU2BWERERETqmgKxiIiIiNQ1BWIR\nERERqWsKxCIiIiJS1xSIRURERKSuKRCLiIiISF1TIBYRERGRuqajm+eJoaEhrrzySl544QVSqRQH\nH3wwn/70p2lvb+fRRx/l2muvpVKpsHz5cm644QY6Ojp49tlnufbaa9m1axeu6/La176W6667jkwm\nA8D999/P9ddfTxRFvOY1r+Hzn/882Wx2r++/r9c+8sgjXH/99eTzeQBOO+00rrzySixreo94rNX5\nv/jii7ztbW/j0EMPrb725ptvpq2tbVrnX+t1APjWt77FT37yE1zXpbGxkU9/+tN71GWh1MH3fS69\n9FL++Mc/AvA///M/k7pW6zWY6tgX2j1huuY/3+8J01UHmN/3hKnUoVbuCTD9dZjqz/MPf/hDvvvd\n72KM4U1vehPXXHMNtm1z33338c1vfhPf9zHGcN5553HxxRfPaC2mzMi8MDQ0ZH73u99VP/7CF75g\n/uEf/sFEUWTe8pa3mIceesgYY8w3vvENc9VVVxljjNm+fbv505/+ZIwxJooi8/d///fm61//ujHG\nmLGxMXPKKaeYZ5991hhjzNVXX22+9rWv7fW99/fav/zlL9XPVyoVc8EFF5itW7dO69yNqd35b9++\n3bz+9a+f9vnuS63W4c9//rM5/fTTTaFQMMYY8/3vf99ccskl0zv53cxlHYIgMP/1X/9l/vznP7/s\n935/16bbdNdgKmNfiPeE6Zr/fL8nTFcd5vs9YSp1qJV7gjHTX4ep/Dy/8MIL5tRTTzUDAwMmiiJz\n8cUXV1/76KOPmp07dxpjjMnn83uMpVaoZWKeaG1t5cQTT6x+fPTRR9Pd3c0f//hH0uk0xx9/PAAX\nXHABd999NwArVqzgyCOPBMC2bdavX093dzcAv/71rznqqKNYtWpV9evuuuuuvb73/l67bt266udT\nqRRHHnlk9T2mU63Of7bVah0syyIIAsrlMgCjo6MsXbp0eie/m7msg+u6nHLKKTQ1NU3p2nSb7hpM\nZewL8Z4wXfOfbbVah/l+T5hKHWrlngDTX4ep/Dz/4he/4C1veQvt7e3Yts3f/M3f8POf/xyA173u\ndSxZsgSApqYm1qxZw44dO6a/AK+CAvE8FMcxt956K2eccQY9PT10dXVVr7W3txPHMcPDw3t8Tblc\n5o477uCMM84AeNnXdXV10dPTs9f3m+xrBwYG+MUvfsHpp5/+aqb3impt/oVCgXe/+928+93v5sYb\nb8QYMy3zfCW1VIfDDz+cD3zgA5xxxhmceuqp/PznP+cTn/jEtM11f2a7DrVoOmowFQvxnjAVC/me\nMBUL+Z6wEEx3HV7p53my94Wnn36aRx99lJNOOukAZzYzFIjnoc985jM0NDRw4YUXTur1YRjy8Y9/\nnJNOOokzzzxzRsY0NjbGpZdeysUXX1z9P82ZUkvz7+zs5D//8z/50Y9+xHe/+13uuecebr/99ml9\nj32ppTrs2LGDX/7yl9xzzz385je/4dxzz+Wqq66a1vfYl1qqw1ypxRronqB7gu4Jc2s66zBdP899\nfX189KMf5brrrquuGNcKBeJ5ZvPmzTz//PN85StfwbZtli1btsdfXwwODmLbNq2trQBEUcQVV1xB\nS0sL11xzTfV1f/113d3dLFu2DIBPfepTbNq0iU2bNvHMM8/s97UApVKJj3zkI7zhDW+Y8Sb5Wpt/\nKpWio6MDgI6ODs4++2weeeSRmSvAuFqrw9133826devo7OwE4Jxzzpnxh0dgbupQa6arBvtTD/eE\n/amne8L+1NM9YX9q/Z4A01uHff08f+xjH6vWYWxs7BXvCwMDA3zgAx/gkksu4R3veMdMTf3AzXUT\ns0zel770JXPhhReaYrFY/VwURebMM8/ca6N8FEXmiiuuMJ/4xCdMGIZ7fK/R0VFz8sknT+ohov29\ntlwum4suushcf/310znVvarF+ff39xvf940xxhSLRfP+97/f3HzzzdM2572pxTrcfffd5qyzzqo+\nQHP77beb97znPdM2572ZqzpM2N/DU7P1YNV01mAqY1+I94QJr3b+8/2eMOHV1mG+3xMmTOVnuRbu\nCcZMbx2m8vO8t4fqfvSjHxljjBkcHDRnn322ueWWW6ZrmtPOMmaWmpvkVXnyySc566yzWLVqVXWb\nqBUrVvCNb3yDRx55hOuuu26PrVQWLVrEr371Kz784Q+zbt06bDv5y4Bjjz2W6667DoD77ruPG264\ngTiOOeKII/jCF75AQ0PDXt9/X6+95ZZb+Kd/+ifWrVtXfe2GDRu49NJL62L+99xzD//8z/+MbduE\nYcjpp5/O5ZdfjuM40zr/Wq+DMYYbbriB+++/n1QqRXNzM//4j//I2rVrF2QdzjvvPHp7exkYGGDx\n4sWceuqpfPazn33Fa7Veg6mMfSHeE6Zj/gvhnjAddVgI94Sp1KEW7gkzUYep/jxv2bKFG2+8EYA3\nvOENXHvttTiOw+bNm7nllltYvXp19bUXXXQR55133ozU4UAoEIuIiIhIXVMPsYiIiIjUNQViERER\nEalrCsQiIiIiUtcUiEVERESkrikQi4iIiEhdUyAWERERkbrmzvUARERk9p1xxhn09/fjOA6O47B2\n7Vo2bdrE+eefX92LdF9efPFFzjzzTP70pz/huvpjRETmP93JRETq1Le+9S1OOeUURkdHefDBB/ns\nZz/LY489xuc///m5HpqIyKxSy4SISJ1ramrizDPP5Ctf+Qpbt27l//7v//jVr37FOeecw7HHHstp\np53G1772terrL7zwQgBOOOEEjjnmGH7/+98DcPvtt/OOd7yDE044gQ9+8IPs2LFjTuYjIjJVCsQi\nIgLA+vXrWbp0KQ8//DDZbJbNmzfz8MMP8+1vf5tbb72V++67D4Af/OAHADz00EP8/ve/55hjjuG+\n++7j29/+Nl//+tf57//+b4477jguv/zyuZyOiMikKRCLiEhVZ2cnIyMjnHjiiRx22GHYts3hhx/O\nu971Lh588MF9ft2WLVv40Ic+xJo1a3Bdl4985CM88cQTWiUWkXlBPcQiIlLV29tLS0sLf/jDH/ji\nF7/Ik08+SRAE+L7Phg0b9vl13d3dfO5zn2Pz5s3Vzxlj6O3tZfny5bMxdBGRA6ZALCIiADz22GP0\n9vZy3HHH8bGPfYwLL7yQG2+8kXQ6zWc/+1mGhoYAsCzrZV+7bNkyPvKRj7Bx48bZHraIyKumlgkR\nkTo3NjbGAw88wCc+8Qk2btzIYYcdRqFQoKWlhXQ6zWOPPca2bduqr29vb8e2bbZv31793AUXXMB3\nvvMdnnzySQBGR0e56667Zn0uIiIHwjLGmLkehIiIzK7d9yG2bZu1a9eyceNGLrjgAhzH4e6772bz\n5s0MDw/z+te/nuXLl5PP5/niF78IwFe/+lVuvfVWwjDkxhtv5Oijj+bHP/4xN910Ezt27KCpqYlT\nTjlFW7iJyLygQCwiIiIidU0tEyIiIiJS1xSIRURERKSuKRCLiIiISF1TIBYRERGRuqZALCIiIiJ1\nTYFYREREROqaArGIiIiI1DUFYhERERGpawrEIiIiIlLX/n/iX3xAWaPa9gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcri0GlcTvgT",
        "colab_type": "code",
        "outputId": "97bc1aa2-72df-42e9-8845-5ea07d0fc627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "global_prophet.changepoints[:10]"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2020-01-22\n",
              "3    2020-01-24\n",
              "4    2020-01-25\n",
              "6    2020-01-27\n",
              "7    2020-01-28\n",
              "8    2020-01-29\n",
              "10   2020-01-31\n",
              "11   2020-02-01\n",
              "13   2020-02-03\n",
              "14   2020-02-04\n",
              "Name: ds, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    }
  ]
}