Covid_19_Data_Analysis_using_python.ipynb

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  "metadata": {
    "colab": {
      "name": "Covid-19_Data_Analysis_using_python.ipynb",
      "provenance": [],
      "mount_file_id": "1MJC2Doe2S8SA1x0ZFacxsAq_8ikY2x5c",
      "authorship_tag": "ABX9TyPOPENGRsQPX1IdtiBBX0AC",
      "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/luthra2059/COVID-19-Data-Amalysis-using-Python/blob/master/Covid_19_Data_Analysis_using_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EuwuQJUsrzx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "2900df00-613c-41c6-b1b0-1c78192c2ebd"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxX4T7PDtDK5",
        "colab_type": "code",
        "outputId": "a678d5f4-547c-4f74-a456-9618f557efd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "corona_dataset_csv = pd.read_csv(\"drive/My Drive/datasets/covid/covid19_Confirmed_dataset.csv\")\n",
        "corona_dataset_csv.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>17.0608</td>\n",
              "      <td>-61.7964</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>-38.4161</td>\n",
              "      <td>-63.6167</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>266</td>\n",
              "      <td>301</td>\n",
              "      <td>387</td>\n",
              "      <td>387</td>\n",
              "      <td>502</td>\n",
              "      <td>589</td>\n",
              "      <td>690</td>\n",
              "      <td>745</td>\n",
              "      <td>820</td>\n",
              "      <td>1054</td>\n",
              "      <td>1054</td>\n",
              "      <td>1133</td>\n",
              "      <td>1265</td>\n",
              "      <td>1451</td>\n",
              "      <td>1451</td>\n",
              "      <td>1554</td>\n",
              "      <td>1628</td>\n",
              "      <td>1715</td>\n",
              "      <td>1795</td>\n",
              "      <td>1975</td>\n",
              "      <td>1975</td>\n",
              "      <td>2142</td>\n",
              "      <td>2208</td>\n",
              "      <td>2277</td>\n",
              "      <td>2443</td>\n",
              "      <td>2571</td>\n",
              "      <td>2669</td>\n",
              "      <td>2758</td>\n",
              "      <td>2839</td>\n",
              "      <td>2941</td>\n",
              "      <td>3031</td>\n",
              "      <td>3144</td>\n",
              "      <td>3435</td>\n",
              "      <td>3607</td>\n",
              "      <td>3780</td>\n",
              "      <td>3892</td>\n",
              "      <td>4003</td>\n",
              "      <td>4127</td>\n",
              "      <td>4285</td>\n",
              "      <td>4428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>40.0691</td>\n",
              "      <td>45.0382</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>194</td>\n",
              "      <td>235</td>\n",
              "      <td>249</td>\n",
              "      <td>265</td>\n",
              "      <td>290</td>\n",
              "      <td>329</td>\n",
              "      <td>407</td>\n",
              "      <td>424</td>\n",
              "      <td>482</td>\n",
              "      <td>532</td>\n",
              "      <td>571</td>\n",
              "      <td>663</td>\n",
              "      <td>736</td>\n",
              "      <td>770</td>\n",
              "      <td>822</td>\n",
              "      <td>833</td>\n",
              "      <td>853</td>\n",
              "      <td>881</td>\n",
              "      <td>921</td>\n",
              "      <td>937</td>\n",
              "      <td>967</td>\n",
              "      <td>1013</td>\n",
              "      <td>1039</td>\n",
              "      <td>1067</td>\n",
              "      <td>1111</td>\n",
              "      <td>1159</td>\n",
              "      <td>1201</td>\n",
              "      <td>1248</td>\n",
              "      <td>1291</td>\n",
              "      <td>1339</td>\n",
              "      <td>1401</td>\n",
              "      <td>1473</td>\n",
              "      <td>1523</td>\n",
              "      <td>1596</td>\n",
              "      <td>1677</td>\n",
              "      <td>1746</td>\n",
              "      <td>1808</td>\n",
              "      <td>1867</td>\n",
              "      <td>1932</td>\n",
              "      <td>2066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-35.4735</td>\n",
              "      <td>149.0124</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>19</td>\n",
              "      <td>32</td>\n",
              "      <td>39</td>\n",
              "      <td>39</td>\n",
              "      <td>53</td>\n",
              "      <td>62</td>\n",
              "      <td>71</td>\n",
              "      <td>77</td>\n",
              "      <td>78</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>99</td>\n",
              "      <td>100</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>105</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-33.8688</td>\n",
              "      <td>151.2093</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>669</td>\n",
              "      <td>669</td>\n",
              "      <td>818</td>\n",
              "      <td>1029</td>\n",
              "      <td>1219</td>\n",
              "      <td>1405</td>\n",
              "      <td>1617</td>\n",
              "      <td>1791</td>\n",
              "      <td>2032</td>\n",
              "      <td>2032</td>\n",
              "      <td>2182</td>\n",
              "      <td>2298</td>\n",
              "      <td>2389</td>\n",
              "      <td>2493</td>\n",
              "      <td>2580</td>\n",
              "      <td>2637</td>\n",
              "      <td>2686</td>\n",
              "      <td>2734</td>\n",
              "      <td>2773</td>\n",
              "      <td>2822</td>\n",
              "      <td>2857</td>\n",
              "      <td>2857</td>\n",
              "      <td>2863</td>\n",
              "      <td>2870</td>\n",
              "      <td>2886</td>\n",
              "      <td>2897</td>\n",
              "      <td>2926</td>\n",
              "      <td>2936</td>\n",
              "      <td>2957</td>\n",
              "      <td>2963</td>\n",
              "      <td>2969</td>\n",
              "      <td>2971</td>\n",
              "      <td>2976</td>\n",
              "      <td>2982</td>\n",
              "      <td>2994</td>\n",
              "      <td>3002</td>\n",
              "      <td>3004</td>\n",
              "      <td>3016</td>\n",
              "      <td>3016</td>\n",
              "      <td>3025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 104 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Province/State       Country/Region  ...  4/29/20  4/30/20\n",
              "0                           NaN          Afghanistan  ...     1939     2171\n",
              "1                           NaN              Albania  ...      766      773\n",
              "2                           NaN              Algeria  ...     3848     4006\n",
              "3                           NaN              Andorra  ...      743      745\n",
              "4                           NaN               Angola  ...       27       27\n",
              "5                           NaN  Antigua and Barbuda  ...       24       24\n",
              "6                           NaN            Argentina  ...     4285     4428\n",
              "7                           NaN              Armenia  ...     1932     2066\n",
              "8  Australian Capital Territory            Australia  ...      106      106\n",
              "9               New South Wales            Australia  ...     3016     3025\n",
              "\n",
              "[10 rows x 104 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncI1nCSZxMrI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8cedc421-f12e-4609-bbea-c0bcb7c2f63c"
      },
      "source": [
        "corona_dataset_csv.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(266, 104)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVcSk8NBxU8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_csv.drop([\"Lat\", \"Long\"], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQNSd0AXxU-8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "2277996d-1d33-4f37-8b5c-01407ee04971"
      },
      "source": [
        "corona_dataset_csv.head(10)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
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              "      <th>2/6/20</th>\n",
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              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>...</th>\n",
              "      <th>3/22/20</th>\n",
              "      <th>3/23/20</th>\n",
              "      <th>3/24/20</th>\n",
              "      <th>3/25/20</th>\n",
              "      <th>3/26/20</th>\n",
              "      <th>3/27/20</th>\n",
              "      <th>3/28/20</th>\n",
              "      <th>3/29/20</th>\n",
              "      <th>3/30/20</th>\n",
              "      <th>3/31/20</th>\n",
              "      <th>4/1/20</th>\n",
              "      <th>4/2/20</th>\n",
              "      <th>4/3/20</th>\n",
              "      <th>4/4/20</th>\n",
              "      <th>4/5/20</th>\n",
              "      <th>4/6/20</th>\n",
              "      <th>4/7/20</th>\n",
              "      <th>4/8/20</th>\n",
              "      <th>4/9/20</th>\n",
              "      <th>4/10/20</th>\n",
              "      <th>4/11/20</th>\n",
              "      <th>4/12/20</th>\n",
              "      <th>4/13/20</th>\n",
              "      <th>4/14/20</th>\n",
              "      <th>4/15/20</th>\n",
              "      <th>4/16/20</th>\n",
              "      <th>4/17/20</th>\n",
              "      <th>4/18/20</th>\n",
              "      <th>4/19/20</th>\n",
              "      <th>4/20/20</th>\n",
              "      <th>4/21/20</th>\n",
              "      <th>4/22/20</th>\n",
              "      <th>4/23/20</th>\n",
              "      <th>4/24/20</th>\n",
              "      <th>4/25/20</th>\n",
              "      <th>4/26/20</th>\n",
              "      <th>4/27/20</th>\n",
              "      <th>4/28/20</th>\n",
              "      <th>4/29/20</th>\n",
              "      <th>4/30/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>74</td>\n",
              "      <td>84</td>\n",
              "      <td>94</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>120</td>\n",
              "      <td>170</td>\n",
              "      <td>174</td>\n",
              "      <td>237</td>\n",
              "      <td>273</td>\n",
              "      <td>281</td>\n",
              "      <td>299</td>\n",
              "      <td>349</td>\n",
              "      <td>367</td>\n",
              "      <td>423</td>\n",
              "      <td>444</td>\n",
              "      <td>484</td>\n",
              "      <td>521</td>\n",
              "      <td>555</td>\n",
              "      <td>607</td>\n",
              "      <td>665</td>\n",
              "      <td>714</td>\n",
              "      <td>784</td>\n",
              "      <td>840</td>\n",
              "      <td>906</td>\n",
              "      <td>933</td>\n",
              "      <td>996</td>\n",
              "      <td>1026</td>\n",
              "      <td>1092</td>\n",
              "      <td>1176</td>\n",
              "      <td>1279</td>\n",
              "      <td>1351</td>\n",
              "      <td>1463</td>\n",
              "      <td>1531</td>\n",
              "      <td>1703</td>\n",
              "      <td>1828</td>\n",
              "      <td>1939</td>\n",
              "      <td>2171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>89</td>\n",
              "      <td>104</td>\n",
              "      <td>123</td>\n",
              "      <td>146</td>\n",
              "      <td>174</td>\n",
              "      <td>186</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>223</td>\n",
              "      <td>243</td>\n",
              "      <td>259</td>\n",
              "      <td>277</td>\n",
              "      <td>304</td>\n",
              "      <td>333</td>\n",
              "      <td>361</td>\n",
              "      <td>377</td>\n",
              "      <td>383</td>\n",
              "      <td>400</td>\n",
              "      <td>409</td>\n",
              "      <td>416</td>\n",
              "      <td>433</td>\n",
              "      <td>446</td>\n",
              "      <td>467</td>\n",
              "      <td>475</td>\n",
              "      <td>494</td>\n",
              "      <td>518</td>\n",
              "      <td>539</td>\n",
              "      <td>548</td>\n",
              "      <td>562</td>\n",
              "      <td>584</td>\n",
              "      <td>609</td>\n",
              "      <td>634</td>\n",
              "      <td>663</td>\n",
              "      <td>678</td>\n",
              "      <td>712</td>\n",
              "      <td>726</td>\n",
              "      <td>736</td>\n",
              "      <td>750</td>\n",
              "      <td>766</td>\n",
              "      <td>773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>201</td>\n",
              "      <td>230</td>\n",
              "      <td>264</td>\n",
              "      <td>302</td>\n",
              "      <td>367</td>\n",
              "      <td>409</td>\n",
              "      <td>454</td>\n",
              "      <td>511</td>\n",
              "      <td>584</td>\n",
              "      <td>716</td>\n",
              "      <td>847</td>\n",
              "      <td>986</td>\n",
              "      <td>1171</td>\n",
              "      <td>1251</td>\n",
              "      <td>1320</td>\n",
              "      <td>1423</td>\n",
              "      <td>1468</td>\n",
              "      <td>1572</td>\n",
              "      <td>1666</td>\n",
              "      <td>1761</td>\n",
              "      <td>1825</td>\n",
              "      <td>1914</td>\n",
              "      <td>1983</td>\n",
              "      <td>2070</td>\n",
              "      <td>2160</td>\n",
              "      <td>2268</td>\n",
              "      <td>2418</td>\n",
              "      <td>2534</td>\n",
              "      <td>2629</td>\n",
              "      <td>2718</td>\n",
              "      <td>2811</td>\n",
              "      <td>2910</td>\n",
              "      <td>3007</td>\n",
              "      <td>3127</td>\n",
              "      <td>3256</td>\n",
              "      <td>3382</td>\n",
              "      <td>3517</td>\n",
              "      <td>3649</td>\n",
              "      <td>3848</td>\n",
              "      <td>4006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>113</td>\n",
              "      <td>133</td>\n",
              "      <td>164</td>\n",
              "      <td>188</td>\n",
              "      <td>224</td>\n",
              "      <td>267</td>\n",
              "      <td>308</td>\n",
              "      <td>334</td>\n",
              "      <td>370</td>\n",
              "      <td>376</td>\n",
              "      <td>390</td>\n",
              "      <td>428</td>\n",
              "      <td>439</td>\n",
              "      <td>466</td>\n",
              "      <td>501</td>\n",
              "      <td>525</td>\n",
              "      <td>545</td>\n",
              "      <td>564</td>\n",
              "      <td>583</td>\n",
              "      <td>601</td>\n",
              "      <td>601</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>659</td>\n",
              "      <td>673</td>\n",
              "      <td>673</td>\n",
              "      <td>696</td>\n",
              "      <td>704</td>\n",
              "      <td>713</td>\n",
              "      <td>717</td>\n",
              "      <td>717</td>\n",
              "      <td>723</td>\n",
              "      <td>723</td>\n",
              "      <td>731</td>\n",
              "      <td>738</td>\n",
              "      <td>738</td>\n",
              "      <td>743</td>\n",
              "      <td>743</td>\n",
              "      <td>743</td>\n",
              "      <td>745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>266</td>\n",
              "      <td>301</td>\n",
              "      <td>387</td>\n",
              "      <td>387</td>\n",
              "      <td>502</td>\n",
              "      <td>589</td>\n",
              "      <td>690</td>\n",
              "      <td>745</td>\n",
              "      <td>820</td>\n",
              "      <td>1054</td>\n",
              "      <td>1054</td>\n",
              "      <td>1133</td>\n",
              "      <td>1265</td>\n",
              "      <td>1451</td>\n",
              "      <td>1451</td>\n",
              "      <td>1554</td>\n",
              "      <td>1628</td>\n",
              "      <td>1715</td>\n",
              "      <td>1795</td>\n",
              "      <td>1975</td>\n",
              "      <td>1975</td>\n",
              "      <td>2142</td>\n",
              "      <td>2208</td>\n",
              "      <td>2277</td>\n",
              "      <td>2443</td>\n",
              "      <td>2571</td>\n",
              "      <td>2669</td>\n",
              "      <td>2758</td>\n",
              "      <td>2839</td>\n",
              "      <td>2941</td>\n",
              "      <td>3031</td>\n",
              "      <td>3144</td>\n",
              "      <td>3435</td>\n",
              "      <td>3607</td>\n",
              "      <td>3780</td>\n",
              "      <td>3892</td>\n",
              "      <td>4003</td>\n",
              "      <td>4127</td>\n",
              "      <td>4285</td>\n",
              "      <td>4428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>194</td>\n",
              "      <td>235</td>\n",
              "      <td>249</td>\n",
              "      <td>265</td>\n",
              "      <td>290</td>\n",
              "      <td>329</td>\n",
              "      <td>407</td>\n",
              "      <td>424</td>\n",
              "      <td>482</td>\n",
              "      <td>532</td>\n",
              "      <td>571</td>\n",
              "      <td>663</td>\n",
              "      <td>736</td>\n",
              "      <td>770</td>\n",
              "      <td>822</td>\n",
              "      <td>833</td>\n",
              "      <td>853</td>\n",
              "      <td>881</td>\n",
              "      <td>921</td>\n",
              "      <td>937</td>\n",
              "      <td>967</td>\n",
              "      <td>1013</td>\n",
              "      <td>1039</td>\n",
              "      <td>1067</td>\n",
              "      <td>1111</td>\n",
              "      <td>1159</td>\n",
              "      <td>1201</td>\n",
              "      <td>1248</td>\n",
              "      <td>1291</td>\n",
              "      <td>1339</td>\n",
              "      <td>1401</td>\n",
              "      <td>1473</td>\n",
              "      <td>1523</td>\n",
              "      <td>1596</td>\n",
              "      <td>1677</td>\n",
              "      <td>1746</td>\n",
              "      <td>1808</td>\n",
              "      <td>1867</td>\n",
              "      <td>1932</td>\n",
              "      <td>2066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>19</td>\n",
              "      <td>32</td>\n",
              "      <td>39</td>\n",
              "      <td>39</td>\n",
              "      <td>53</td>\n",
              "      <td>62</td>\n",
              "      <td>71</td>\n",
              "      <td>77</td>\n",
              "      <td>78</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>99</td>\n",
              "      <td>100</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>102</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>103</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>105</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>669</td>\n",
              "      <td>669</td>\n",
              "      <td>818</td>\n",
              "      <td>1029</td>\n",
              "      <td>1219</td>\n",
              "      <td>1405</td>\n",
              "      <td>1617</td>\n",
              "      <td>1791</td>\n",
              "      <td>2032</td>\n",
              "      <td>2032</td>\n",
              "      <td>2182</td>\n",
              "      <td>2298</td>\n",
              "      <td>2389</td>\n",
              "      <td>2493</td>\n",
              "      <td>2580</td>\n",
              "      <td>2637</td>\n",
              "      <td>2686</td>\n",
              "      <td>2734</td>\n",
              "      <td>2773</td>\n",
              "      <td>2822</td>\n",
              "      <td>2857</td>\n",
              "      <td>2857</td>\n",
              "      <td>2863</td>\n",
              "      <td>2870</td>\n",
              "      <td>2886</td>\n",
              "      <td>2897</td>\n",
              "      <td>2926</td>\n",
              "      <td>2936</td>\n",
              "      <td>2957</td>\n",
              "      <td>2963</td>\n",
              "      <td>2969</td>\n",
              "      <td>2971</td>\n",
              "      <td>2976</td>\n",
              "      <td>2982</td>\n",
              "      <td>2994</td>\n",
              "      <td>3002</td>\n",
              "      <td>3004</td>\n",
              "      <td>3016</td>\n",
              "      <td>3016</td>\n",
              "      <td>3025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 102 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Province/State       Country/Region  ...  4/29/20  4/30/20\n",
              "0                           NaN          Afghanistan  ...     1939     2171\n",
              "1                           NaN              Albania  ...      766      773\n",
              "2                           NaN              Algeria  ...     3848     4006\n",
              "3                           NaN              Andorra  ...      743      745\n",
              "4                           NaN               Angola  ...       27       27\n",
              "5                           NaN  Antigua and Barbuda  ...       24       24\n",
              "6                           NaN            Argentina  ...     4285     4428\n",
              "7                           NaN              Armenia  ...     1932     2066\n",
              "8  Australian Capital Territory            Australia  ...      106      106\n",
              "9               New South Wales            Australia  ...     3016     3025\n",
              "\n",
              "[10 rows x 102 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PudyWP64xVFG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated = corona_dataset_csv.groupby(\"Country/Region\").sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkD7UEeFxVJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "d72d337e-dcb9-4e6c-cb03-8b0c4a6d26d6"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>4/28/20</th>\n",
              "      <th>4/29/20</th>\n",
              "      <th>4/30/20</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>74</td>\n",
              "      <td>84</td>\n",
              "      <td>94</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>120</td>\n",
              "      <td>170</td>\n",
              "      <td>174</td>\n",
              "      <td>237</td>\n",
              "      <td>273</td>\n",
              "      <td>281</td>\n",
              "      <td>299</td>\n",
              "      <td>349</td>\n",
              "      <td>367</td>\n",
              "      <td>423</td>\n",
              "      <td>444</td>\n",
              "      <td>484</td>\n",
              "      <td>521</td>\n",
              "      <td>555</td>\n",
              "      <td>607</td>\n",
              "      <td>665</td>\n",
              "      <td>714</td>\n",
              "      <td>784</td>\n",
              "      <td>840</td>\n",
              "      <td>906</td>\n",
              "      <td>933</td>\n",
              "      <td>996</td>\n",
              "      <td>1026</td>\n",
              "      <td>1092</td>\n",
              "      <td>1176</td>\n",
              "      <td>1279</td>\n",
              "      <td>1351</td>\n",
              "      <td>1463</td>\n",
              "      <td>1531</td>\n",
              "      <td>1703</td>\n",
              "      <td>1828</td>\n",
              "      <td>1939</td>\n",
              "      <td>2171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>89</td>\n",
              "      <td>104</td>\n",
              "      <td>123</td>\n",
              "      <td>146</td>\n",
              "      <td>174</td>\n",
              "      <td>186</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>223</td>\n",
              "      <td>243</td>\n",
              "      <td>259</td>\n",
              "      <td>277</td>\n",
              "      <td>304</td>\n",
              "      <td>333</td>\n",
              "      <td>361</td>\n",
              "      <td>377</td>\n",
              "      <td>383</td>\n",
              "      <td>400</td>\n",
              "      <td>409</td>\n",
              "      <td>416</td>\n",
              "      <td>433</td>\n",
              "      <td>446</td>\n",
              "      <td>467</td>\n",
              "      <td>475</td>\n",
              "      <td>494</td>\n",
              "      <td>518</td>\n",
              "      <td>539</td>\n",
              "      <td>548</td>\n",
              "      <td>562</td>\n",
              "      <td>584</td>\n",
              "      <td>609</td>\n",
              "      <td>634</td>\n",
              "      <td>663</td>\n",
              "      <td>678</td>\n",
              "      <td>712</td>\n",
              "      <td>726</td>\n",
              "      <td>736</td>\n",
              "      <td>750</td>\n",
              "      <td>766</td>\n",
              "      <td>773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>201</td>\n",
              "      <td>230</td>\n",
              "      <td>264</td>\n",
              "      <td>302</td>\n",
              "      <td>367</td>\n",
              "      <td>409</td>\n",
              "      <td>454</td>\n",
              "      <td>511</td>\n",
              "      <td>584</td>\n",
              "      <td>716</td>\n",
              "      <td>847</td>\n",
              "      <td>986</td>\n",
              "      <td>1171</td>\n",
              "      <td>1251</td>\n",
              "      <td>1320</td>\n",
              "      <td>1423</td>\n",
              "      <td>1468</td>\n",
              "      <td>1572</td>\n",
              "      <td>1666</td>\n",
              "      <td>1761</td>\n",
              "      <td>1825</td>\n",
              "      <td>1914</td>\n",
              "      <td>1983</td>\n",
              "      <td>2070</td>\n",
              "      <td>2160</td>\n",
              "      <td>2268</td>\n",
              "      <td>2418</td>\n",
              "      <td>2534</td>\n",
              "      <td>2629</td>\n",
              "      <td>2718</td>\n",
              "      <td>2811</td>\n",
              "      <td>2910</td>\n",
              "      <td>3007</td>\n",
              "      <td>3127</td>\n",
              "      <td>3256</td>\n",
              "      <td>3382</td>\n",
              "      <td>3517</td>\n",
              "      <td>3649</td>\n",
              "      <td>3848</td>\n",
              "      <td>4006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>113</td>\n",
              "      <td>133</td>\n",
              "      <td>164</td>\n",
              "      <td>188</td>\n",
              "      <td>224</td>\n",
              "      <td>267</td>\n",
              "      <td>308</td>\n",
              "      <td>334</td>\n",
              "      <td>370</td>\n",
              "      <td>376</td>\n",
              "      <td>390</td>\n",
              "      <td>428</td>\n",
              "      <td>439</td>\n",
              "      <td>466</td>\n",
              "      <td>501</td>\n",
              "      <td>525</td>\n",
              "      <td>545</td>\n",
              "      <td>564</td>\n",
              "      <td>583</td>\n",
              "      <td>601</td>\n",
              "      <td>601</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>659</td>\n",
              "      <td>673</td>\n",
              "      <td>673</td>\n",
              "      <td>696</td>\n",
              "      <td>704</td>\n",
              "      <td>713</td>\n",
              "      <td>717</td>\n",
              "      <td>717</td>\n",
              "      <td>723</td>\n",
              "      <td>723</td>\n",
              "      <td>731</td>\n",
              "      <td>738</td>\n",
              "      <td>738</td>\n",
              "      <td>743</td>\n",
              "      <td>743</td>\n",
              "      <td>743</td>\n",
              "      <td>745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                1/22/20  1/23/20  1/24/20  ...  4/28/20  4/29/20  4/30/20\n",
              "Country/Region                             ...                           \n",
              "Afghanistan           0        0        0  ...     1828     1939     2171\n",
              "Albania               0        0        0  ...      750      766      773\n",
              "Algeria               0        0        0  ...     3649     3848     4006\n",
              "Andorra               0        0        0  ...      743      743      745\n",
              "Angola                0        0        0  ...       27       27       27\n",
              "\n",
              "[5 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ2WCo_zxVOt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7371dcc6-945b-44cd-c29a-c9ea60ffb702"
      },
      "source": [
        "corona_dataset_aggregated.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(187, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lk6HDvvAxVRt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "5a17f9d3-8899-4890-d9ca-6f2e047a1672"
      },
      "source": [
        "#Visulaizating data related to China\n",
        "corona_dataset_aggregated.loc[\"China\"].plot()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25f0a1bf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BB68gwTxVW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "4477614a-31d0-41fb-e026-779d7f9c50b7"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"][:3].plot()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f261c6b2e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xp2TzI01xVat",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3d36c590-27da-4cf4-960c-69f81113e597"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().plot()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25f0491be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7RpJi2gxVUl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4d9f973d-ea93-4b99-efe2-fa5525e2e150"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().max()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15136.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRd_xZjNxVM6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3fc98d06-bca0-4bb6-ae3e-e0d7adbb20b5"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6557.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNHnoOeHxVDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7c89c40e-888b-4cfd-f8d3-da0186afc586"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9630.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvuClWij1NZ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countries = list(corona_dataset_aggregated.index)\n",
        "max_infection_rates = []\n",
        "for country in countries :\n",
        "    max_infection_rates.append(corona_dataset_aggregated.loc[country].diff().max())\n",
        "corona_dataset_aggregated['max infection rate'] = max_infection_rates"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRnSrtWV1NgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "f31d8897-f54f-4950-b072-e62b7f4581d1"
      },
      "source": [
        "corona_data = pd.DataFrame(corona_dataset_aggregated['max infection rate'])\n",
        "corona_data.head()"
      ],
      "execution_count": 24,
      "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>max infection rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>232.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max infection rate\n",
              "Country/Region                    \n",
              "Afghanistan                  232.0\n",
              "Albania                       34.0\n",
              "Algeria                      199.0\n",
              "Andorra                       43.0\n",
              "Angola                         5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4T6RZqdF0yB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "714e53be-2c83-45ce-cd36-95ac31cfa03a"
      },
      "source": [
        "print(countries)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzvYY6G41NkL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "12a22a53-dbde-4235-c8b5-c7e4374b7879"
      },
      "source": [
        "happiness_report_csv = pd.read_csv(\"drive/My Drive/datasets/covid/worldwide_happiness_report.csv\")\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 28,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
              "      <th>Score</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.769</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Norway</td>\n",
              "      <td>7.554</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.494</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.488</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.322</td>\n",
              "      <td>0.298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Overall rank Country or region  ...  Generosity  Perceptions of corruption\n",
              "0             1           Finland  ...       0.153                      0.393\n",
              "1             2           Denmark  ...       0.252                      0.410\n",
              "2             3            Norway  ...       0.271                      0.341\n",
              "3             4           Iceland  ...       0.354                      0.118\n",
              "4             5       Netherlands  ...       0.322                      0.298\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKE2ip3M1Nnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "useless_cols = [\"Overall rank\", \"Score\", \"Generosity\", \"Perceptions of corruption\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pybFMdt1NvK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1473ef27-6d08-4e3f-d5c4-0f51aac9f30e"
      },
      "source": [
        "happiness_report_csv.drop(useless_cols,axis=1 , inplace=True)\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 33,
      "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",
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              "        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>Country or region</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Country or region  ...  Freedom to make life choices\n",
              "0           Finland  ...                         0.596\n",
              "1           Denmark  ...                         0.592\n",
              "2            Norway  ...                         0.603\n",
              "3           Iceland  ...                         0.591\n",
              "4       Netherlands  ...                         0.557\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHWetnXn1N12",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "57a9b0ed-7ad4-491d-c513-3ab4d575595d"
      },
      "source": [
        "happiness_report_csv.set_index(['Country or region'],inplace=True)\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "        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>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country or region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   GDP per capita  ...  Freedom to make life choices\n",
              "Country or region                  ...                              \n",
              "Finland                     1.340  ...                         0.596\n",
              "Denmark                     1.383  ...                         0.592\n",
              "Norway                      1.488  ...                         0.603\n",
              "Iceland                     1.380  ...                         0.591\n",
              "Netherlands                 1.396  ...                         0.557\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSxQsxAc1N5v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "94598f10-1c42-480c-936f-9d8f8f65b186"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 36,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max infection rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>232.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>34.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max infection rate\n",
              "Country/Region                    \n",
              "Afghanistan                  232.0\n",
              "Albania                       34.0\n",
              "Algeria                      199.0\n",
              "Andorra                       43.0\n",
              "Angola                         5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgnb2Syj1N86",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "c28c26bb-f2c9-4302-8cfd-8193bf56b247"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country or region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   GDP per capita  ...  Freedom to make life choices\n",
              "Country or region                  ...                              \n",
              "Finland                     1.340  ...                         0.596\n",
              "Denmark                     1.383  ...                         0.592\n",
              "Norway                      1.488  ...                         0.603\n",
              "Iceland                     1.380  ...                         0.591\n",
              "Netherlands                 1.396  ...                         0.557\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uf_Y2i4q1OAA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "32a324c2-a519-40cb-928c-0e11aa05bbc2"
      },
      "source": [
        "data = corona_data.join(happiness_report_csv,how=\"inner\")\n",
        "data.head()"
      ],
      "execution_count": 38,
      "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>max infection rate</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>232.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>34.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>199.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>291.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>134.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max infection rate  ...  Freedom to make life choices\n",
              "Afghanistan               232.0  ...                         0.000\n",
              "Albania                    34.0  ...                         0.383\n",
              "Algeria                   199.0  ...                         0.086\n",
              "Argentina                 291.0  ...                         0.471\n",
              "Armenia                   134.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvHFx4Lb1NyO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "652d9106-aa06-4c9f-e948-c3655082f80a"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>max infection rate</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>max infection rate</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.250118</td>\n",
              "      <td>0.191958</td>\n",
              "      <td>0.289263</td>\n",
              "      <td>0.078196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP per capita</th>\n",
              "      <td>0.250118</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.759468</td>\n",
              "      <td>0.863062</td>\n",
              "      <td>0.394603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social support</th>\n",
              "      <td>0.191958</td>\n",
              "      <td>0.759468</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.765286</td>\n",
              "      <td>0.456246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <td>0.289263</td>\n",
              "      <td>0.863062</td>\n",
              "      <td>0.765286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.427892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <td>0.078196</td>\n",
              "      <td>0.394603</td>\n",
              "      <td>0.456246</td>\n",
              "      <td>0.427892</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              max infection rate  ...  Freedom to make life choices\n",
              "max infection rate                      1.000000  ...                      0.078196\n",
              "GDP per capita                          0.250118  ...                      0.394603\n",
              "Social support                          0.191958  ...                      0.456246\n",
              "Healthy life expectancy                 0.289263  ...                      0.427892\n",
              "Freedom to make life choices            0.078196  ...                      1.000000\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lOyB4F81Ns0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "07075904-0ae1-48c3-e665-ac59eb306e2a"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 40,
      "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>max infection rate</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>232.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>34.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>199.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>291.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>134.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max infection rate  ...  Freedom to make life choices\n",
              "Afghanistan               232.0  ...                         0.000\n",
              "Albania                    34.0  ...                         0.383\n",
              "Algeria                   199.0  ...                         0.086\n",
              "Argentina                 291.0  ...                         0.471\n",
              "Armenia                   134.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g0EgJ1y1Nqr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b559f476-a3ea-4f47-a827-4c90d7837512"
      },
      "source": [
        "x = data['GDP per capita']\n",
        "y = data['max infection rate']\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25f00b5a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqUkOMzq1Ndy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "2bb766eb-787f-400b-ec38-abab6136fa1c"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25f0034438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_N1nIQo5AHF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "15c42129-d56d-4b12-c908-80c961ca2b0a"
      },
      "source": [
        "x = data['Social support']\n",
        "y = data['max infection rate']\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed78ea90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGhGilhO5AKx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1ca62ef4-390e-4a68-be9c-4c9c459b1048"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed75a940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uy-Hn-Mt5APw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b9b9a484-bdfc-4f03-cc20-0b3ad7c9486d"
      },
      "source": [
        "x = data['Healthy life expectancy']\n",
        "y = data['max infection rate']\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed6dd588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5WZ6i6Z5AYM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "da4bf068-9ea7-4d64-b4e0-4f04de4a07a0"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed779cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6jVwx-z5Aei",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b7b6d575-c462-4d8c-e1d3-9b0499ee0452"
      },
      "source": [
        "x = data['Freedom to make life choices']\n",
        "y = data['max infection rate']\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed6157f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkSMgxxi5AbG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e66c6864-508a-499e-cff4-524ee6d3940a"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed5edcf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}