covid19_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/chan-98/data_science/blob/main/covid19_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Covid19 Data Analysis"
      ],
      "metadata": {
        "id": "LBO9TTzqam0T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA:\n",
        "\n",
        "> 1. \"/content/covid19_Confirmed_dataset.csv\"\n",
        "> 2. \"/content/covid19_deaths_dataset.csv\"\n",
        "> 3. \"/content/worldwide_happiness_report.csv\""
      ],
      "metadata": {
        "id": "px_Agulx87ut"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Studying the relation between countrywise Covid19 infection and World Happiness Metrics.**"
      ],
      "metadata": {
        "id": "k1LN_IKC9LnX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "quFrdFOQWeUz"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Dataset"
      ],
      "metadata": {
        "id": "OoNl_K2-amFT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/covid19_Confirmed_dataset.csv\")\n",
        "# confirmed covid cases on different dates from Jan to April\n",
        "dataset.head(10)"
      ],
      "metadata": {
        "id": "Xv6oV-H6a_HI",
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          "height": 465
        },
        "outputId": "2c33326b-5470-4fe5-b59f-20ffa29d53cf"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                 Province/State       Country/Region      Lat      Long  \\\n",
              "0                           NaN          Afghanistan  33.0000   65.0000   \n",
              "1                           NaN              Albania  41.1533   20.1683   \n",
              "2                           NaN              Algeria  28.0339    1.6596   \n",
              "3                           NaN              Andorra  42.5063    1.5218   \n",
              "4                           NaN               Angola -11.2027   17.8739   \n",
              "5                           NaN  Antigua and Barbuda  17.0608  -61.7964   \n",
              "6                           NaN            Argentina -38.4161  -63.6167   \n",
              "7                           NaN              Armenia  40.0691   45.0382   \n",
              "8  Australian Capital Territory            Australia -35.4735  149.0124   \n",
              "9               New South Wales            Australia -33.8688  151.2093   \n",
              "\n",
              "   1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  4/21/20  \\\n",
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              "   4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  \\\n",
              "0     1176     1279     1351     1463     1531     1703     1828     1939   \n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a9bfd13d-0803-4120-8294-b81c8c8b24ff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "id": "bPy2vzEva-7U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f977dc3-2969-4e2f-c09d-eea1ce8ad679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(266, 104)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean data\n",
        "\n",
        "    # delete cols latitude and longitude\n",
        "\n",
        "df1 = dataset.drop([\"Lat\", \"Long\"], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "_B1ujBhXa-g5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "ngb-dOi2f_0C",
        "outputId": "83507456-cd9f-469f-e162-aa27dd8a6241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Province/State Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
              "0            NaN    Afghanistan        0        0        0        0        0   \n",
              "1            NaN        Albania        0        0        0        0        0   \n",
              "2            NaN        Algeria        0        0        0        0        0   \n",
              "3            NaN        Andorra        0        0        0        0        0   \n",
              "4            NaN         Angola        0        0        0        0        0   \n",
              "\n",
              "   1/27/20  1/28/20  1/29/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
              "0        0        0        0  ...     1092     1176     1279     1351   \n",
              "1        0        0        0  ...      609      634      663      678   \n",
              "2        0        0        0  ...     2811     2910     3007     3127   \n",
              "3        0        0        0  ...      717      723      723      731   \n",
              "4        0        0        0  ...       24       25       25       25   \n",
              "\n",
              "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
              "0     1463     1531     1703     1828     1939     2171  \n",
              "1      712      726      736      750      766      773  \n",
              "2     3256     3382     3517     3649     3848     4006  \n",
              "3      738      738      743      743      743      745  \n",
              "4       25       26       27       27       27       27  \n",
              "\n",
              "[5 rows x 102 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-195f5a3d-3b60-40e7-a811-8170305e3317\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>...</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>...</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>...</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>...</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>...</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>...</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 × 102 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-195f5a3d-3b60-40e7-a811-8170305e3317')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-195f5a3d-3b60-40e7-a811-8170305e3317 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-195f5a3d-3b60-40e7-a811-8170305e3317');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-67e86143-b079-4a72-834a-eaa7a22f13df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67e86143-b079-4a72-834a-eaa7a22f13df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-67e86143-b079-4a72-834a-eaa7a22f13df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    # aggregate rows by country\n",
        "covid_dataset_aggregated = dataset.groupby(\"Country/Region\").sum()\n",
        "covid_dataset_aggregated.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "c2mV3VaGgRRx",
        "outputId": "3661777f-5285-4f44-9523-31e24641d731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-8d900064ea90>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  covid_dataset_aggregated = dataset.groupby(\"Country/Region\").sum()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
              "Country/Region                                                                  \n",
              "Afghanistan           0        0        0        0        0        0        0   \n",
              "Albania               0        0        0        0        0        0        0   \n",
              "Algeria               0        0        0        0        0        0        0   \n",
              "Andorra               0        0        0        0        0        0        0   \n",
              "Angola                0        0        0        0        0        0        0   \n",
              "\n",
              "                1/29/20  1/30/20  1/31/20  ...  4/21/20  4/22/20  4/23/20  \\\n",
              "Country/Region                             ...                              \n",
              "Afghanistan           0        0        0  ...     1092     1176     1279   \n",
              "Albania               0        0        0  ...      609      634      663   \n",
              "Algeria               0        0        0  ...     2811     2910     3007   \n",
              "Andorra               0        0        0  ...      717      723      723   \n",
              "Angola                0        0        0  ...       24       25       25   \n",
              "\n",
              "                4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
              "Country/Region                                                                 \n",
              "Afghanistan        1351     1463     1531     1703     1828     1939     2171  \n",
              "Albania             678      712      726      736      750      766      773  \n",
              "Algeria            3127     3256     3382     3517     3649     3848     4006  \n",
              "Andorra             731      738      738      743      743      743      745  \n",
              "Angola               25       25       26       27       27       27       27  \n",
              "\n",
              "[5 rows x 100 columns]"
            ],
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              "      <th>Afghanistan</th>\n",
              "      <td>0</td>\n",
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              "      <th>Albania</th>\n",
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              "      <td>773</td>\n",
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              "      <th>Algeria</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>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",
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              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>25</td>\n",
              "      <td>26</td>\n",
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              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae3a6545-ad92-48d4-af93-8d7e00d31b55 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.shape\n",
        "\n",
        "# 100 days (cols), 187 countries (rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQcbbHi3giXp",
        "outputId": "dbe62bab-33e7-4f49-e5d6-a46813a77b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(187, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize data related to countries\n",
        "covid_dataset_aggregated.loc[\"China\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhhLWuEJgwyT",
        "outputId": "3be0e951-c9ef-475c-cd43-a224975dc7ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1/22/20      548\n",
              "1/23/20      643\n",
              "1/24/20      920\n",
              "1/25/20     1406\n",
              "1/26/20     2075\n",
              "           ...  \n",
              "4/26/20    83912\n",
              "4/27/20    83918\n",
              "4/28/20    83940\n",
              "4/29/20    83944\n",
              "4/30/20    83956\n",
              "Name: China, Length: 100, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.loc[\"China\"].plot()\n",
        "covid_dataset_aggregated.loc[\"India\"].plot()\n",
        "covid_dataset_aggregated.loc[\"Singapore\"].plot()\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Gw2j4KeCgwtA",
        "outputId": "8f132a8f-9f8b-40a2-ddbd-db5253656ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e48cb0589d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate a good measure (number) that can represent the spread of covid in a country\n",
        "covid_dataset_aggregated.loc[\"China\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "XUaalqRlgwqZ",
        "outputId": "13377313-d489-4ff1-be41-69ff5d3d2ed2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.loc[\"China\"][:3].plot()  # spead in first 3 days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "t2m-6EDbhigK",
        "outputId": "b6b16845-378e-41a0-973c-de338e8da9ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the first derivative of the curve\n",
        "\n",
        "covid_dataset_aggregated.loc[\"China\"].diff()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXmnOFgshtTq",
        "outputId": "59a766e4-ea0f-4b42-b934-b843b5349228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1/22/20      NaN\n",
              "1/23/20     95.0\n",
              "1/24/20    277.0\n",
              "1/25/20    486.0\n",
              "1/26/20    669.0\n",
              "           ...  \n",
              "4/26/20      3.0\n",
              "4/27/20      6.0\n",
              "4/28/20     22.0\n",
              "4/29/20      4.0\n",
              "4/30/20     12.0\n",
              "Name: China, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.loc[\"China\"].diff().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "-c6GosAMiG86",
        "outputId": "5a2d5ac7-ee7b-4f8e-bc12-213660d6dffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first derivative shows the rate of infection spread between Jan and April in China"
      ],
      "metadata": {
        "id": "VkHCgtUbiUD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# max infection rate\n",
        "covid_dataset_aggregated.loc[\"China\"].diff().max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJSoryE7iLVq",
        "outputId": "83576205-34c8-43c5-becf-2d1479508c57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15136.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.loc[\"India\"].diff().max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l608lLE5ijiS",
        "outputId": "e9b3bc14-791f-4700-f757-9d2d09ba3907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1893.0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated.loc[\"Vietnam\"].diff().max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6ha0wvaiorq",
        "outputId": "294d09f4-e101-49f3-81c9-f96327f25732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19.0"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# max infec rate for all countries\n",
        "\n",
        "countries = list(covid_dataset_aggregated.index)    # countries are stored in index\n",
        "max_infec_rates = [covid_dataset_aggregated.loc[country].diff().max() for country in countries]\n",
        "covid_dataset_aggregated[\"Maximum Infection Rates\"] = max_infec_rates"
      ],
      "metadata": {
        "id": "Nw_1p8umiq2K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "covid_dataset_aggregated"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "VeO8T5j6jLKa",
        "outputId": "da2327e0-77fd-4e64-992e-fb614e9cb35a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
              "Country/Region                                                             \n",
              "Afghanistan               0        0        0        0        0        0   \n",
              "Albania                   0        0        0        0        0        0   \n",
              "Algeria                   0        0        0        0        0        0   \n",
              "Andorra                   0        0        0        0        0        0   \n",
              "Angola                    0        0        0        0        0        0   \n",
              "...                     ...      ...      ...      ...      ...      ...   \n",
              "West Bank and Gaza        0        0        0        0        0        0   \n",
              "Western Sahara            0        0        0        0        0        0   \n",
              "Yemen                     0        0        0        0        0        0   \n",
              "Zambia                    0        0        0        0        0        0   \n",
              "Zimbabwe                  0        0        0        0        0        0   \n",
              "\n",
              "                    1/28/20  1/29/20  1/30/20  1/31/20  ...  4/22/20  4/23/20  \\\n",
              "Country/Region                                          ...                     \n",
              "Afghanistan               0        0        0        0  ...     1176     1279   \n",
              "Albania                   0        0        0        0  ...      634      663   \n",
              "Algeria                   0        0        0        0  ...     2910     3007   \n",
              "Andorra                   0        0        0        0  ...      723      723   \n",
              "Angola                    0        0        0        0  ...       25       25   \n",
              "...                     ...      ...      ...      ...  ...      ...      ...   \n",
              "West Bank and Gaza        0        0        0        0  ...      474      480   \n",
              "Western Sahara            0        0        0        0  ...        6        6   \n",
              "Yemen                     0        0        0        0  ...        1        1   \n",
              "Zambia                    0        0        0        0  ...       74       76   \n",
              "Zimbabwe                  0        0        0        0  ...       28       28   \n",
              "\n",
              "                    4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  \\\n",
              "Country/Region                                                             \n",
              "Afghanistan            1351     1463     1531     1703     1828     1939   \n",
              "Albania                 678      712      726      736      750      766   \n",
              "Algeria                3127     3256     3382     3517     3649     3848   \n",
              "Andorra                 731      738      738      743      743      743   \n",
              "Angola                   25       25       26       27       27       27   \n",
              "...                     ...      ...      ...      ...      ...      ...   \n",
              "West Bank and Gaza      484      342      342      342      343      344   \n",
              "Western Sahara            6        6        6        6        6        6   \n",
              "Yemen                     1        1        1        1        1        6   \n",
              "Zambia                   84       84       88       88       95       97   \n",
              "Zimbabwe                 29       31       31       32       32       32   \n",
              "\n",
              "                    4/30/20  Maximum Infection Rates  \n",
              "Country/Region                                        \n",
              "Afghanistan            2171                    232.0  \n",
              "Albania                 773                     34.0  \n",
              "Algeria                4006                    199.0  \n",
              "Andorra                 745                     43.0  \n",
              "Angola                   27                      5.0  \n",
              "...                     ...                      ...  \n",
              "West Bank and Gaza      344                     66.0  \n",
              "Western Sahara            6                      4.0  \n",
              "Yemen                     6                      5.0  \n",
              "Zambia                  106                      9.0  \n",
              "Zimbabwe                 40                      8.0  \n",
              "\n",
              "[187 rows x 101 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e615d35-86c2-47fa-bd27-3641e9ec34aa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>...</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",
              "      <th>Maximum Infection Rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>...</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",
              "      <td>232.0</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>...</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",
              "      <td>34.0</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>...</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",
              "      <td>199.0</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>...</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",
              "      <td>43.0</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>...</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",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bank and Gaza</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>...</td>\n",
              "      <td>474</td>\n",
              "      <td>480</td>\n",
              "      <td>484</td>\n",
              "      <td>342</td>\n",
              "      <td>342</td>\n",
              "      <td>342</td>\n",
              "      <td>343</td>\n",
              "      <td>344</td>\n",
              "      <td>344</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Western Sahara</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>...</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yemen</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>...</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>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zambia</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>...</td>\n",
              "      <td>74</td>\n",
              "      <td>76</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>88</td>\n",
              "      <td>88</td>\n",
              "      <td>95</td>\n",
              "      <td>97</td>\n",
              "      <td>106</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</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>...</td>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>187 rows × 101 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e615d35-86c2-47fa-bd27-3641e9ec34aa')\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-2e615d35-86c2-47fa-bd27-3641e9ec34aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e615d35-86c2-47fa-bd27-3641e9ec34aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f36630b5-7395-48eb-8dce-15d905aa99ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_2e65bf35-0325-421e-8cba-1310bf869598\">\n",
              "    <style>\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid_dataset_aggregated')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2e65bf35-0325-421e-8cba-1310bf869598 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('covid_dataset_aggregated');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we no longer need all the individual data\n",
        "\n",
        "covid_data = pd.DataFrame(covid_dataset_aggregated[\"Maximum Infection Rates\"])\n",
        "covid_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "2S6bmowgjc6a",
        "outputId": "021ecb3a-a54e-4a51-a7f1-6aa4af9318cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Maximum Infection Rates\n",
              "Country/Region                             \n",
              "Afghanistan                           232.0\n",
              "Albania                                34.0\n",
              "Algeria                               199.0\n",
              "Andorra                                43.0\n",
              "Angola                                  5.0\n",
              "...                                     ...\n",
              "West Bank and Gaza                     66.0\n",
              "Western Sahara                          4.0\n",
              "Yemen                                   5.0\n",
              "Zambia                                  9.0\n",
              "Zimbabwe                                8.0\n",
              "\n",
              "[187 rows x 1 columns]"
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              "      <td>4.0</td>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import World happiness data\n",
        "\n",
        "happiness_report = pd.read_csv(\"/content/worldwide_happiness_report.csv\")\n",
        "happiness_report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "Dg87UYFgjvMb",
        "outputId": "ed6c8df6-5d07-4a09-a9fd-35f5d19af891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Overall rank         Country or region  Score  GDP per capita  \\\n",
              "0               1                   Finland  7.769           1.340   \n",
              "1               2                   Denmark  7.600           1.383   \n",
              "2               3                    Norway  7.554           1.488   \n",
              "3               4                   Iceland  7.494           1.380   \n",
              "4               5               Netherlands  7.488           1.396   \n",
              "..            ...                       ...    ...             ...   \n",
              "151           152                    Rwanda  3.334           0.359   \n",
              "152           153                  Tanzania  3.231           0.476   \n",
              "153           154               Afghanistan  3.203           0.350   \n",
              "154           155  Central African Republic  3.083           0.026   \n",
              "155           156               South Sudan  2.853           0.306   \n",
              "\n",
              "     Social support  Healthy life expectancy  Freedom to make life choices  \\\n",
              "0             1.587                    0.986                         0.596   \n",
              "1             1.573                    0.996                         0.592   \n",
              "2             1.582                    1.028                         0.603   \n",
              "3             1.624                    1.026                         0.591   \n",
              "4             1.522                    0.999                         0.557   \n",
              "..              ...                      ...                           ...   \n",
              "151           0.711                    0.614                         0.555   \n",
              "152           0.885                    0.499                         0.417   \n",
              "153           0.517                    0.361                         0.000   \n",
              "154           0.000                    0.105                         0.225   \n",
              "155           0.575                    0.295                         0.010   \n",
              "\n",
              "     Generosity  Perceptions of corruption  \n",
              "0         0.153                      0.393  \n",
              "1         0.252                      0.410  \n",
              "2         0.271                      0.341  \n",
              "3         0.354                      0.118  \n",
              "4         0.322                      0.298  \n",
              "..          ...                        ...  \n",
              "151       0.217                      0.411  \n",
              "152       0.276                      0.147  \n",
              "153       0.158                      0.025  \n",
              "154       0.235                      0.035  \n",
              "155       0.202                      0.091  \n",
              "\n",
              "[156 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
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              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0.252</td>\n",
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              "    <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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>152</td>\n",
              "      <td>Rwanda</td>\n",
              "      <td>3.334</td>\n",
              "      <td>0.359</td>\n",
              "      <td>0.711</td>\n",
              "      <td>0.614</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.217</td>\n",
              "      <td>0.411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>153</td>\n",
              "      <td>Tanzania</td>\n",
              "      <td>3.231</td>\n",
              "      <td>0.476</td>\n",
              "      <td>0.885</td>\n",
              "      <td>0.499</td>\n",
              "      <td>0.417</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>154</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>3.203</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>155</td>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>3.083</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.105</td>\n",
              "      <td>0.225</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>156</td>\n",
              "      <td>South Sudan</td>\n",
              "      <td>2.853</td>\n",
              "      <td>0.306</td>\n",
              "      <td>0.575</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.202</td>\n",
              "      <td>0.091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>156 rows × 9 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean Data\n",
        "\n",
        "drop_columns = [\"Overall rank\", \"Generosity\", \"Score\", \"Perceptions of corruption\"]\n",
        "happiness_report.drop(drop_columns, axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "cprgQAZXj7lK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "happiness_report.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "owukC-k5kX27",
        "outputId": "e3f6f473-756f-43dc-d7e0-b87e39f5d22b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Country or region  GDP per capita  Social support  Healthy life expectancy  \\\n",
              "0           Finland           1.340           1.587                    0.986   \n",
              "1           Denmark           1.383           1.573                    0.996   \n",
              "2            Norway           1.488           1.582                    1.028   \n",
              "3           Iceland           1.380           1.624                    1.026   \n",
              "4       Netherlands           1.396           1.522                    0.999   \n",
              "\n",
              "   Freedom to make life choices  \n",
              "0                         0.596  \n",
              "1                         0.592  \n",
              "2                         0.603  \n",
              "3                         0.591  \n",
              "4                         0.557  "
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              "      <th>Country or region</th>\n",
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              "      <td>Denmark</td>\n",
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              "      <th>2</th>\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-94cd0c3e-dccf-4926-9fff-b68d5112e3fa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-94cd0c3e-dccf-4926-9fff-b68d5112e3fa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-00d0731c-8c42-47e5-9321-51a0e094a5da\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-00d0731c-8c42-47e5-9321-51a0e094a5da')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-00d0731c-8c42-47e5-9321-51a0e094a5da button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make country/region as index\n",
        "happiness_report.set_index(\"Country or region\", inplace=True)\n",
        "happiness_report.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "WgEePzf0oN90",
        "outputId": "e165988a-542d-490d-b8c5-a7a766763de6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   GDP per capita  Social support  Healthy life expectancy  \\\n",
              "Country or region                                                            \n",
              "Finland                     1.340           1.587                    0.986   \n",
              "Denmark                     1.383           1.573                    0.996   \n",
              "Norway                      1.488           1.582                    1.028   \n",
              "Iceland                     1.380           1.624                    1.026   \n",
              "Netherlands                 1.396           1.522                    0.999   \n",
              "\n",
              "                   Freedom to make life choices  \n",
              "Country or region                                \n",
              "Finland                                   0.596  \n",
              "Denmark                                   0.592  \n",
              "Norway                                    0.603  \n",
              "Iceland                                   0.591  \n",
              "Netherlands                               0.557  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29f135ee-11cf-4618-96b7-f487676dfd7c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</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>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29f135ee-11cf-4618-96b7-f487676dfd7c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29f135ee-11cf-4618-96b7-f487676dfd7c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-29f135ee-11cf-4618-96b7-f487676dfd7c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7ae47a3f-685e-40e5-8255-8af339a95c79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7ae47a3f-685e-40e5-8255-8af339a95c79')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7ae47a3f-685e-40e5-8255-8af339a95c79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Join `covid_data` and `happiness_report` using INNER JOIN"
      ],
      "metadata": {
        "id": "wmiURxo2pXST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "covid_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ompm_XOzo3dM",
        "outputId": "1bf006db-a0a2-44c2-9471-8b3bfa2e0e6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(187, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
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        "happiness_report.shape"
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      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "(156, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = covid_data.join(happiness_report, how = \"inner\")"
      ],
      "metadata": {
        "id": "nS5C1duppiXV"
      },
      "execution_count": null,
      "outputs": []
    },
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        "id": "FB9lsHy9pypZ",
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      "execution_count": null,
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            "text/plain": [
              "             Maximum Infection Rates  GDP per capita  Social support  \\\n",
              "Afghanistan                    232.0           0.350           0.517   \n",
              "Albania                         34.0           0.947           0.848   \n",
              "Algeria                        199.0           1.002           1.160   \n",
              "Argentina                      291.0           1.092           1.432   \n",
              "Armenia                        134.0           0.850           1.055   \n",
              "...                              ...             ...             ...   \n",
              "Venezuela                       29.0           0.960           1.427   \n",
              "Vietnam                         19.0           0.741           1.346   \n",
              "Yemen                            5.0           0.287           1.163   \n",
              "Zambia                           9.0           0.578           1.058   \n",
              "Zimbabwe                         8.0           0.366           1.114   \n",
              "\n",
              "             Healthy life expectancy  Freedom to make life choices  \n",
              "Afghanistan                    0.361                         0.000  \n",
              "Albania                        0.874                         0.383  \n",
              "Algeria                        0.785                         0.086  \n",
              "Argentina                      0.881                         0.471  \n",
              "Armenia                        0.815                         0.283  \n",
              "...                              ...                           ...  \n",
              "Venezuela                      0.805                         0.154  \n",
              "Vietnam                        0.851                         0.543  \n",
              "Yemen                          0.463                         0.143  \n",
              "Zambia                         0.426                         0.431  \n",
              "Zimbabwe                       0.433                         0.361  \n",
              "\n",
              "[143 rows x 5 columns]"
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              "      <th>Maximum Infection Rates</th>\n",
              "      <th>GDP per capita</th>\n",
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              "      <th>Afghanistan</th>\n",
              "      <td>232.0</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>34.0</td>\n",
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              "      <td>0.383</td>\n",
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              "      <th>Algeria</th>\n",
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              "      <td>1.160</td>\n",
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              "      <th>Argentina</th>\n",
              "      <td>291.0</td>\n",
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              "      <td>0.543</td>\n",
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              "      <td>1.163</td>\n",
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              "      <td>0.426</td>\n",
              "      <td>0.431</td>\n",
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              "      <td>0.366</td>\n",
              "      <td>1.114</td>\n",
              "      <td>0.433</td>\n",
              "      <td>0.361</td>\n",
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          "metadata": {},
          "execution_count": 54
        }
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    {
      "cell_type": "markdown",
      "source": [
        "Create a Correlation Matrix"
      ],
      "metadata": {
        "id": "YOIx049Yp300"
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    {
      "cell_type": "code",
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        "# Pearson Correlation Coefficient\n",
        "\n",
        "data.corr()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 276
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      "execution_count": null,
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            "text/plain": [
              "                              Maximum Infection Rates  GDP per capita  \\\n",
              "Maximum Infection Rates                      1.000000        0.250118   \n",
              "GDP per capita                               0.250118        1.000000   \n",
              "Social support                               0.191958        0.759468   \n",
              "Healthy life expectancy                      0.289263        0.863062   \n",
              "Freedom to make life choices                 0.078196        0.394603   \n",
              "\n",
              "                              Social support  Healthy life expectancy  \\\n",
              "Maximum Infection Rates             0.191958                 0.289263   \n",
              "GDP per capita                      0.759468                 0.863062   \n",
              "Social support                      1.000000                 0.765286   \n",
              "Healthy life expectancy             0.765286                 1.000000   \n",
              "Freedom to make life choices        0.456246                 0.427892   \n",
              "\n",
              "                              Freedom to make life choices  \n",
              "Maximum Infection Rates                           0.078196  \n",
              "GDP per capita                                    0.394603  \n",
              "Social support                                    0.456246  \n",
              "Healthy life expectancy                           0.427892  \n",
              "Freedom to make life choices                      1.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Maximum Infection Rates</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>Maximum Infection Rates</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",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From above matrix, we can say all of these show POSITIVE CORRELATION.\n",
        "\n",
        "Max Infec Rates has very little correlation with Freedom to make life choices. Healthy life expectancy has very high correlation with per-capita GDP."
      ],
      "metadata": {
        "id": "3JgPefoBqMGR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "\n",
        "# data.corr().plot()      # messy plot, do it individually"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "gD7Ovm_5qA-M",
        "outputId": "1e5e7e7a-c90c-451e-d3e7-349e0658a20f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "\n",
        "x = data[\"GDP per capita\"]\n",
        "y = data[\"Maximum Infection Rates\"]\n",
        "\n",
        "sns.scatterplot(x=x, y=y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "8VAFK7OCqnNE",
        "outputId": "91f9be1c-65c7-4eff-c0e1-6465fcbae169"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GDP per capita', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=x, y=np.log(y))     # log scaling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Lz31-avxrB-c",
        "outputId": "fe5998ed-066d-4f1e-ca0d-560a6fb93fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GDP per capita', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5ZXhq+laCTIqR5mW6NDMSGxmOxff/CXmnyvDFnvxagWVdAYTSTIuaxyqUMmzBorimp3Xr1jh9+nSt7cXFxWjdurUqjSKi4MovPovx2Tb0m78Jt775Hfq9sgmPZtuQX3xW76aZnj9zGumdSTHKvEyeMiOje6fj9Q0HkJwQVSvgcXIGEP48nnN/b/U5ah4ro2XYZKA46Pntt99QXV1da3tlZSWOHTumSqOIKHhYhCo3a2wkmjf2XZuhdybFKPMyecqMZKU1wtbc06g87/B5X08BhD+ZlkupeayMlGGThd/dW59//rnr/9etWwer1er6u7q6Gjk5OWjVqpWqjSMi7TFFLr+mCdHok5mEzR7eJ1kyKamNYvD68Kygz8tU33oaZ7AT1cB3DsBTABFopkWtY+XMGsl8XsjG76BnyJAhAACLxVJrna2IiAi0atUKr7zyiqqNIyLtMUUuP2d2YOqKn9wucGpnUupb11WfeZkCeW416mmcwY7taDF6ZSR67OLyFkDUJ9OixhxWwTovQonfQY/DcSEaTk9Px86dO5GU5LlPkoiMhSlyY9A6k6Ln0OdAnjuQkUueMiPOYGfxljwsHH5hrrmtl4ze8hZAyJBp0SvDZlSKl6EwGi5DQeRbSXkVHs22ef3i5rDX0FdSXoXx2TaP3ZxanwOBPvfBk3b0m7/J6+PmTO6LNslxtbbnF591y4w4R2+98U0ufjhchNG905GV1ggA0LxxDJomRPt87Zc+nrPdc2/vhGacJ6depFiGAgDKysqwadMmHDlyBFVV7oVaEyZMUKVhRBQcTJGTnnVdgT632vU0iwLMljDTYiyKgx6bzYYbb7wR5eXlKCsrQ5MmTVBYWIjY2FgkJycz6CEyIH5xm5uedV2BPrcW9TSBnu9cY844FA9Zf+yxxzBo0CAUFRUhJiYG27dvx+HDh3HVVVdh3rx5WrSRiILAGhuJNslx6NyiMdokx/FL3ET0rOsK9LmNMjcQyUVx0LNr1y48/vjjCAsLQ3h4OCorK5GWloaXXnoJTz31lBZtJCIiDekZQAT63EaZGyiUGXGhU8XdWxEREQgLuxArJScn48iRI7jiiitgtVpx9OhR1RtIRETa0rOuqz7PzW5Z/Rh1oVPFQU9WVhZ27tyJzMxM9O3bF9OnT0dhYSH++c9/okOHDlq0kYiINKZnAFGf52Y9TfAZeaFTxUHPiy++iDNnzgAAZs2ahfvuuw9jx45FZmYm3n//fdUbSEREwaFnAMHgxTiMPIu74qCna9eurv9PTk7G2rVrVW0QERERycvIs7grLmT25scff8TNN9+s1sMRERFpzojFuHoz8izuijI969atw/r16xEZGYkHH3wQrVu3xi+//IKpU6di9erVGDhwoFbtJCIiUpVRi3H1JsPyG4HyO9Pz/vvv44YbbsDSpUsxd+5c/PnPf8aHH36IHj16oGnTpti7dy/+/e9/a9lWIiIiVdRVjMuMj3dGni7A70zPa6+9hrlz5+Jvf/sbVqxYgaFDh+LNN9/Enj170Lx5cy3bSEREpCojF+PKwKjTBfgd9Bw8eBBDhw4FANx2221o0KABXn75ZQY8RERkOEYuxpWFEUfc+d29dfbsWcTGxgIALBYLoqKi0KxZM80aRkREpBUjF+NS4BQVMr/33nuIi4sDAJw/fx5Lly5FUpJ7nx4XHCUiItkZuRiXAmcRQgh/dmzVqhUsFovvB7NYcOjQIVUappbS0lJYrVaUlJQgISFB7+YQEZEk8ovPel3+ohlHb+lOi+u330GPUTHoISMpKa9Cob0KpRXnkBATgaSGxuszJzIS52fOSMW4ZqHF9VvxjMxEpA3OGUIUfEYsxqXAqTYjMxEFjnOGEBFpj5keIglwzhDv2OVHRGph0EMkAc4Z4hm7/IhITezeIpIA5wypjV1+8uNinfXD4xd8AWV6HA4HcnNzcfLkSTgcDrfb+vTpo0rDiMyEc4a4KymvwvGSCnb5SYxZuPrh8dOH4qBn+/btuPvuu3H48GFcOtrdYrGgurpatcYRmYVzAT9vc4aY6eLuvBgM79bC535m7fLTmj81VHVl4V4fnmWqc1YpHj/9KA56Hn74YXTt2hVffvklmjVrVueEhUTkH6Mu4KemmheD+3u28rmvGbv8tOZv9sHIhfcyFMYb+fgZneKg58CBA/jXv/6FjIwMLdpDZGpmnzOk5sXAdrQYvTISsTX3dK39zNjlpzUl2QejFt7L0qVk1OMXChQXMnfv3h25ublatIWITK7mxWDxljyM6pWOXhmJbvuYscsvGPzJPjgZsfBepsJ4Ix6/UKE40/Poo4/i8ccfR0FBATp27IiICPc3p1OnTqo1jojMpebFoLyqGhOybRjdOx2je6Wj8rwDrZMaopk1mgGPBpRkH4xYeC9Tl5IRj1+oUBz03H777QCA0aNHu7ZZLBYIIVjITET1cunFoLyqGos2XMgs98lMYoGnhpRkH4xYeC9Tl5IRj1+oUBz05OXladEOIiKpLgYyFLwGk9Lsg9EK72XrUjLa8QsVioOeli1batEOIiIAclwMZCl4DaZAAk4jFd7L2KVkpOMXKizi0sl2/HDw4EEsWLAA+/btAwC0a9cOEydORJs2bVRvYH1psTQ9kUzMlpHQWkl5FcZn2zzWf5ihi815PoVi9iG/+KzXoK5ZiAazRqbF9VtxpmfdunW45ZZb0LlzZ/Tq1QsAsHXrVrRv3x6rV6/GgAEDVGmY07FjxzBlyhSsWbMG5eXlyMjIwJIlS9C1a1dVn4fIiMyYkdCaTAWvegjl7EPNLGJZ5TlYYyJRVe1AQWkFys9V8weDCSgOeqZOnYrHHnsMc+bMqbV9ypQpqgY9RUVF6NWrF6699lqsWbMGl112GQ4cOIDGjRur9hxERsVZXbUhU8Erqc8Z1Onxg4FZWf0pDnr27duH5cuX19o+evRoLFiwQI02ucydOxdpaWlYsmSJa1t6errP+1RWVqKystL1d2lpqaptIpKF2TMSWtG64JUXPv3p8YOBWVk5KJ6c8LLLLsOuXbtqbd+1axeSk5PVaJPL559/jq5du2Lo0KFITk5GVlYW3n33XZ/3mT17NqxWq+tfWlqaqm0ikgUzEtpwFrx6Ut+C1/zisxifbUO/+Ztw65vfod8rm/Botg35xWcDfsz6MuNK30omYlSDTBMjmp3iTM9DDz2EMWPG4NChQ+jZsyeACzU9c+fOxeTJk1Vt3KFDh/DWW29h8uTJeOqpp7Bz505MmDABkZGRGDlypMf7TJs2za0dpaWlDHwoJMk2BDdU+DOKyVe2xtttMnZHmjX7EOwfDMzKykNx0PPMM88gPj4er7zyCqZNmwYASE1NxbPPPosJEyao2jiHw4GuXbvixRdfBABkZWVh7969ePvtt70GPVFRUYiKilK1HUQyknEIbqjwNWzeV6BgAfCkl9vOVlVLdeGTMQgLlmD/YGBWVh6Ku7csFgsee+wx/P777ygpKUFJSQl+//13TJw4UfUV15s1a4Z27dq5bbviiitw5MgRVZ+HyIicGYlLu2I4q6s6rLGRaJMch84tGqNNcpxf2ZqNv57y3oVx1ncXRrAvfMHu4pGJll2YnjArKw/FmZ6a4uPj1WqHR7169cL+/fvdtv3666+cIJHo/yfDRH5mUlegMLJnK6+3PXXjFT4fO9gXPjNnH4I98zezsvLwK+jp0qULcnJy0LhxY2RlZfnM6Pz444+qNe6xxx5Dz5498eKLL2LYsGHYsWMH3nnnHbzzzjuqPQeR0YXyvCqyqStQqDzv8HpbeJhFqguf2bMPwfzBINPyKmbnV9AzePBgV53M4MGDVe/G8uZPf/oTPv30U0ybNg3PPfcc0tPTsWDBAowYMSIoz09EVFNdgUJUA+8VA+FhFqkufGbKPngrLg/mD4ZLg6yGUQ0QGR6Gk2c4MWIwBbQMhZFwGQoiUmtunJLyKjyabfMaKNzQsRmmrdzj8TZnYbAWyzwE+vrMsCyDjCPUZGyTjLS4fisOelq3bo2dO3ciMTHRbXtxcTG6dOmCQ4cOqdIwtTDoITIOLSbuU/sC4ytQAIApQQ4i6vv6QnmtLRnXUZOxTbKSIugJCwtDQUFBrYkIT5w4gbS0NFRVyVXxz6CHyBi0+PWr1QXGV6AQzCCCF1DfDp60o9/8TV5vz5ncF22S44LYIjnbJCtdFxz9/PPPXf+/bt06WK1W19/V1dXIycmpc4kIIiJPtJozxt9J4ZRmmHzVggSzTkTrSe+MtmTGpe2tFgKxkeEor6r2uL8eI9TMPGpOBn4HPUOGDAFwYZ6eSycGjIiIQKtWrfDKK6+o2jgiMgetLt7+XGCMXF+h5QXUaMfFU3uvzkzCwuFZmJBt8xj46DFCzeyj5vTm9+SEDocDDocDLVq0wMmTJ11/OxwOVFZWYv/+/bj55pu1bCsRhSitLt51XWAaRjUw9JpIWl1AjbZWlLf2fnugEEu35mF079q9EHqNUAv2xIjkTvGMzHl5eUhK8vyGEREFQquLd10XmMjwMEPPSqzVBdRoszX7au+W3NPo2dp94I2e8+NwJnV9KZ6RecKECcjIyKi1ztaiRYuQm5uLBQsWqNU2IjdGqy8g/2k1Z0xdk8KdPFPh8/6y11doNeldoJk3vT6jdbU3OiIcOZP7SjNCTa+Z1PkdGkDQs2LFCreiZqeePXtizpw5DHpIE0arLyBltJyx1tcFxluBq5MR6iu0uIAGknnT8zNaV3utMRHSjYgK9kzq/A69QHHQc/r0abeRW04JCQkoLPScXiSqDzOvBq0HvX4Navnr19sFJlRmJVb7Aqr0uOj9GQ2V91Erer8/MlFc05ORkYG1a9fW2r5mzRq0bt1alUYR1WS0+gIjyy8+i/HZNvSbvwm3vvkd+r2yCY9m25BffDYoz+9pZXOtn4/1FbUpPS56f0b5Pvqm9/sjE8WZnsmTJ2P8+PE4deoU/vKXvwAAcnJy8Morr7BrizTBeS2C49Jfg7GR4RjdOx1ZaY2w73gpyirPIzk+KuQuIFyp3rO6jkvNjOB5h+85boPxGeX76B2/Qy9SHPSMHj0alZWVmDVrFp5//nkAQKtWrfDWW2/hvvvuU72BRKE8r4VMhYU1fw3GRoZj4fAsLNmah0Ubcl37hGoNAFeq98zbcbm0PuT9kV19Pk6wPqN8Hz0L5e9QpRQHPQAwduxYjB07FqdOnUJMTAzi4uQqEKPQEqr99bIVFtb8NTi6dzqWbM3D1tzTbvuYsQZADTIFt/XlqT7EdrQYvTISa50vgLE/o6EiVL9DA6G4pgcAzp8/j6+//horV66Ec+mu/Px82O12VRtHBIRmf72Mk7/V/DWYldbI4wUMMF8NQH0FWidVUl6FgyftsB0pwsFTdmkmBPRUH7J4Sx5G9UpHrwx55sOhi0LxOzRQijM9hw8fxvXXX48jR46gsrISAwYMQHx8PObOnYvKykq8/fbbWrSTTC7U+uu1XjMpEDV/DVaed/jc10w1APUR6KgZ2bKANXmqDymvqsaEbBtG907H/914BarOOwz/GQ01ofYdGijFmZ6JEyeia9euKCoqQkzMxQ/frbfeipycHFUbR1RTsEf2eKPGL3AZCwtr/hqMauD7q8FMNQD1EcioGRmzgDV5qw8pr6rGog25iGoQrvtnlDyT5TtUT4ozPd9++y2+++47REa6H6xWrVrh2LFjqjWMSEZq/QKXtbDQ+WuwuPwcrs5M8njBNlsNQH0EEtzKmAWsifUhZGSKMz0OhwPV1bVnMf39998RHx+vSqOIAqF1DYSav8BlXnTQGhuJlkkNMTfAGgBZa1H0EEhwK2MWsCbWh5CRKc70XHfddViwYAHeeecdAIDFYoHdbseMGTNw4403qt5AIn8EowZCzV/gWi67oJZAagBkrkXRQyBZEVmzgDWxPoSMyiKcw6/89Pvvv2PgwIEQQuDAgQPo2rUrDhw4gKSkJGzevBnJyclatTUgpaWlsFqtKCkpQUJCgt7NIQ2UlFdhfLbNa1eMWsOrbUeKcOub33m9fdUjPdG5RWNFj+kcyhwKF45gvQ9Gk1981mtw28xDIFhSXoVHs21eAyWzHkcyHy2u34ozPc2bN8fu3buxbNky7N69G3a7HQ888ABGjBjhVthMFCzBqoHQ4hd4KE2mJnstil6UZkVkzAKG0jxDZG5+BT1dunRBTk4OGjdujOeeew5PPPEERowYgREjRmjdPqI6BasGggWcvslei6InpcGtTN1H7LKkUOJXIfO+fftQVlYGAJg5cyYnISSpBKsGggWcvhmhFsVIZBheLPvweSKl/Mr0dO7cGaNGjULv3r0hhMC8efO8Lj0xffp0VRtIVJdgZmBk+gUuG2bCQg+7LCnU+BX0LF26FDNmzMAXX3wBi8WCNWvWoEGD2ne1WCwMeijogl0DEUp1OGqSsRbFLLSquWGXJYUaxaO3wsLCUFBQIN0oLW84ess8QmkklJEF+30we5GtljU3B0/a0W/+Jq+350zuizbJvhecNvv7Q4GTYvSWw+F7TR4ivTADI4dgvg9mL7INdG0vf9W3y9Ls7w/JR3GmBwAOHDiAb775BidPnqwVBMnWvcVMD1Fo4rxA6mRi6qJ0niEnvj9UX1Jket59912MHTsWSUlJaNq0KSwWi+s21vQQUbCwyDY4NTeBFu/z/SEZKQ56XnjhBcyaNQtTpkzRoj1ERH5hkW1wp2tQGqDw/SEZKQ56ioqKMHToUC3aQkTkN84LJPc0ATXfn9jIcIzunY6stEaoPO9AdEQ4GjPLQzpQvMr60KFD8dVXX2nRFiIiv8m8Un2wyDxhpvP9iY0Mx8LhWbAdKcIDH/wHj3z0I0Yv3YlnPtuL/OKzurWPzElxIfPs2bMxf/583HTTTejYsSMiItx/TU2YMEHVBtYXC5mJQlegRbZa0mOItqzTNeQXn8WmX0/hi5/ysTX3dK3bWdBMvmhx/VYc9KSnp3t/MIsFhw4dqnej1MSgh0guagcFMl3w1RiiHWrz2hw4cQYDXt3s9XY1RphRaJJi9FZeXp4qT0xE5qPFvC2yzM+kxpw5oTivjb3yvM/bWdBMwaS4poeIKBChvnilP0O0fQnV4yNrwXlJeRUOnrTDdqQIB0/ZDXt8SRm/Mz2TJ0/2a7/58+cH3BgiCl2hPm9LfYdo6318tOpWk3GEWShm1Mg/fgc9Nputzn1qTlRIRFSTkedt8ScgqG9Go7TinMeh3T8eKcLiLXmaHh8tgwDZFqLVeukOkpvfQc8333yjZTuIKMTJ2s1RF38DgvpmNKwxEVg4PAtLtuZh0YZc1/ZeGYlYODwLCTHaHB+tg4CS8ipUnKvG0ze3g0MIlFdWwxqjrOBczSyU3hk10pfiQmYiMwi1ETQykLGboy5KAoL6ZjQaRjXAkq15tYZ2b809DQuAV4Z1VuU1XUrLIMBXwOjvY6qdhTJyxpHqj0EP0SXY368N2bo5/KE0IAh0nSoAsFec9ziXDQBsyT0Ne8V5pGgw64ZWQYAaGSQtslBGzTiSOhj0SIzZhuBjf7+26hMUBMOln7lqIRAbGY7yqmqP+3sKCAIdQq9XBkKrIECNDJIWWSgjZhxJPQx6JMVsgz7Y3689WebVuZSnz9zVmUlYODwLE7JtHgMfNbMCemUgtAoC1AjitAgEjZhxJPUw6JEQsw36YX+/OXn7zH17oBBCCIzune5WXAyonxXQKwOhVRCgRhCnVSAoe8aRtBNQ0FNRUYGffvoJJ0+ehMPhcLvtlltuUaVhZsZsg37Y329Ovj5zW3JP45FrMtyCHi2yAnpmILQIAtQI4rQMBGXNOJK2FAc9a9euxX333YfCwtonocViQXW1577vUKJ1rQ2zDfoJxf5+1obVra7PXHREOHIm99U8K6BnBkLtIECNII5dUaQ2xUHPo48+iqFDh2L69OlISUnRok1SC0atTSDZBl7Y1KHHl6yW7x1rw/xT12fOGhMRtEUxQykD4S2IA4CDJ+1+nfPsiiI1KV5lPSEhATabDW3atNGqTapSc5XWkvIqjM+2eUyD98lMUq3WpqS8Co9m27xmGy59Hl7Y1Beslbu1fO+Cdb6qSa/gXelnjgLH7yvylxarrCtecPSOO+7Axo0bVXlyo6nvgoL+cmYb+mQmuW33lG0I1UUK9WaNjUSb5Dh0btEYbZLjNMvwaPneBet8VUt+8VmMz7ah3/xNuPXN79DvlU14NNuG/OKzmj+3v585LlJZP/y+Ir0p7t5atGgRhg4dim+//RYdO3ZERIR7WnjChAmqNU42way18Tely6Jn49L6vTNSbZgMIxbr+swxQ1F//L4ivSkOerKzs/HVV18hOjoaGzdudFtk1GKxhHTQE+yRPf707RvpwkbutH7vjDQSTZaLobfPnNKgjDV2nvH7ivSmOOj5v//7P8ycORNTp05FWJji3jFDk3Fkj5EubORO6/dOxvPVG9kvhkqCMmaEvOP3FelNcdRSVVWFO++803QBD6Cs1iZYnBc2T2S7sAWLUeoutH7vZDxfvZH9YuhvUMaaFd98nfNXZyYhLprz5ZK2FJ9hI0eOxLJly/DUU09p0R7pyTZ8kvNYuDPSr+xgvHf+nq96d8fInpXyNyiTpZtOVt7O+V4ZiRjZsxWe/nQPZg7uIN1nlUKH4qCnuroaL730EtatW4dOnTrVKmSeP3++ao2TlWzzaMgWiOlFhmJYpYLx3tV1vsoQKMoevPsblMneTSeD1EYxeHnolTh40o7is+cQ1SAMtqPFrvXNKs/L+Vml0KA46NmzZw+ysrIAAHv37nW7rWZRsxbmzJmDadOmYeLEiViwYIGmz2U0sgViejDqr2w93zuZAkWZg3d/gzLZu+lkYa84j7vf+97jbTJ/Vsn4FAc933zzjRbtqNPOnTvx97//HZ06ddLl+Ul+/JWtnGyBYl0BoJ7dcP4EZbJ308lC6WdV7+5XCh2GqBqz2+0YMWIE3n33Xbzwwgs+962srERlZaXr79LSUq2bR5Lgr2zljBQoytIN5+tiK3s3nSyUfFZleN8pdCgOeq699lqf3VgbNmyoV4M8GTduHG666Sb079+/zqBn9uzZmDlzpuptIPnxV7ZyRgkUZeqGq4uM3XRqZErUzLb4+1k10vtOxqA46OncubPb3+fOncOuXbuwd+9ejBw5Uq12uXzyySf48ccfsXPnTr/2nzZtGiZPnuz6u7S0FGlpaaq3i+TDX9nKGSVQlK0bri4y1dipkSlRO9vi72fVaO87yU9x0PPqq6963P7ss8/CbrfXu0E1HT16FBMnTsT69esRHR3t132ioqIQFRWlajvIOGT8lS0zowSKRuqGk4kamRKtsi3+fFb5vpPaVKvpueeee9CtWzfMmzdPrYfEDz/8gJMnT6JLly6ubdXV1di8eTMWLVqEyspKhIeHq/Z8FBpk+pVtBEYIFI3SDScbNTIlWmZb6vqs8n0ntakW9Gzbts3vbIy/+vXrhz179rhtGzVqFC6//HJMmTKFAQ+RSmQPFI3SDScbNTIlemZb+L6T2hQHPbfddpvb30IIHD9+HP/5z3/wzDPPqNYwAIiPj0eHDh3ctjVs2BCJiYm1thNR6FKjG86Mw57VyJTomW0xSvcrGYfioMdqtbr9HRYWhrZt2+K5557Dddddp1rDyNzMeIEi35zdcKfLqlDtEKh2CJRXnUf5uWqUlPvuYjHrsGc1MiV6Z1uM0P1KxmERQgi9G6Gl0tJSWK1WlJSUICEhQe/mkB/MeoEi/yg9P0rKqzA+2+axLqVPZlJID3s+XnwWh/9Xjtc3HMDW3NOu7VdnJuGl2zuhmYLRW96yLf4+BpFSWly/6xX02O12OBwOt22yBRYMeozFzBcoqlsg58fBk3b0m7/J62PmTO6LNslxqrdVDfXJeDqP1Q+HizC6dzqy0hqh8rwDUQ3CcPJMJW7s0FTRZ8nZFmZbKFi0uH4r7t7Ky8vD+PHjsXHjRlRUVLi2CyFgsVhQXV2tSsPInDgvB/kSyPlhhGHPnoKbsqrqemU8ax6rRRtya93erVUTRZ8l2YvdifyhOOi55557IITA4sWLkZKSovkio2QuRrhAkX4COT9kH/bsqbtu9m0d8e+fjuPb3MDnxuFniag2xUHP7t278cMPP6Bt27ZatIdMTvYLFOkrkPND70JcX7xN/JccH1Ur4HHyN+PJzxJRbWFK7/CnP/0JR48e1aItRK4LlCd6X6BIf4GcH85hz5feT+mw55LyKhw8aYftSBEOnrKjpLxK+Qu4hLfuusrzDg97X+RPloafJaLaFGd63nvvPTz88MM4duwYOnTogIgI918LnTp1Uq1xZD6cl4N8CfT8qO+wZ61GFHrrgopq4Pv3qD9ZGn6WiGpTPHpr+/btuPvuu/Hbb79dfBCLRdpCZo7eMiaOFDEXpaOUgnl+aDmi0NvIsvF/yYDtSJHbMPNAn5OfJTIqKUZvjR49GllZWcjOzmYhswecVE8dHClinnMpkCxKMM8PLUcUeqs3WrwlD4vv/xPCLZZ6Z2n4WSK6SHHQc/jwYXz++efIyMjQoj2Gxkn1SC1mOZe0WsFbTVqOgvLWBdWrTSJaNI7BMze3Q/HZc4iLDEdsZAM0io3Q/XgQGZnioOcvf/kLdu/ezaDnEkb48qbgCjRTY6ZzyQjzMl06Cio2Mtxtsr/oyPA6l8Hw5dJ6o4SYCESGh2HKyj0eg15rbL1eDpGpKQ56Bg0ahMceewx79uxBx44daxUy33LLLao1zkiM8OUtm1DuvqlPpsZM55IR5pKp2QUVGxmOhcOzsGRrntuEf/XNwtXsgvJWQxSKQS9RsCkOeh5++GEAwHPPPVfrNhkLmYPFCF/eMgnl7pv6ZmrMdC6pNZeMlgF0zS6oTmmNsGRrXq0CYzUDEjMFvUTBpjjouXStLbqAE4H5L9S7b+p70TLTuaTGxIHBCKCdXVDHSyo8LukAqBeQmCnoJQo2xZMTkmecCMx//gQFRlbfi5aZzqX6ThxYVwCtxgSCNdt69pzvTLYaAYmZgl6iYPMr07Nw4UKMGTMG0dHRWLhwoc99J0yYoErDjIYTgfkv1H/J1veiZbZzqT4TBwa7KygYAYnMy2YQGZ1fQc+rr76KESNGIDo6Gq+++qrX/SwWi2mDHqD+s76aRaj/klXjomW2cynQuWSCHUAHIyAxW9BLFEx+BT15eXke/59q40RgdQv1X7JqXbR4LtUt2AF0sAISswW9RMGieBmKiooKREdHe7zt+PHjaNasmSoNUwuXoZBTfvFZrxeOZgYfveXE6f+1V1JehUezbV4DaK2K4vneEmlPi+u34qCnXbt2+Pjjj9G5c2e37StWrMDDDz+MU6dOqdIwtTDokVd9LhyhPMcPKWOGAJrIjKRYe+uaa67Bn//8Z8ycORNTpkxBWVkZxo0bh+XLl2PWrFmqNIrMIdDum1Ce44eUY1cQEflLcaYHAL788ks8+OCDyMjIwPHjxxEXF4cPP/wQHTp00KKN9cJMT+goKa9Ccfk5PL1qD75VYfVpIiKSlxSZHgC44YYbcNttt+Gtt95CgwYNsHr1aikDHtKHFl1PzuzO/T1beQx4AM5WS0REvikOeg4ePIi7774bBQUFWLduHTZt2oRbbrkFEydOxKxZs2qtxUXmokXXU80J6IZ3a+FzX6PP8UNERNpRPCNz586dkZ6ejt27d2PAgAF44YUX8M0332DlypXo1q2bFm2kICopr8LBk3bYjhTh4Cm7ohlttZodt+YEdFENfJ+yNYco1+e1UGB4zIlIZoozPW+++Sbuvfdet209e/aEzWbDpEmT1GoX6aC+WRqtZsetOQGd7WgxemUk1lrw0dlW5xw/LHYOPh5zIpKd4kzPpQGPU3x8PN5///16N4j0oUaWRqvZcWtOQLd4Sx5G9UpHr4xEt31qTg4XzPWY6AIecyIygoAKmQHg559/xpEjR1BVdfHLzGKxYNCgQao0jIJLjSyNVrPj1pzBubyqGhOybRjdOx2je6UDAFo0iUVyfJSrfcFej4l4zInIGBQHPYcOHcKtt96KPXv2wGKxwDni3WKxAACqq32vQky1yTDRnhpZGq2Wl7h06v/yqmos2pDrdQK6UF/QVEY85kRkBIqDnokTJyI9PR05OTlIT0/Hjh07cPr0aTz++OOYN2+eFm0MabLUQaiRpdFyXSIlE9CF+oKmMuIxJyIjUBz0bNu2DRs2bEBSUhLCwsIQFhaG3r17Y/bs2ZgwYQJsNpsW7QxJddVBBHOiPbWyNFrOjuvvDM6hvqCpjHjMicgIFBcyV1dXIz4+HgCQlJSE/Px8AEDLli2xf/9+dVsX4vypgwgWZ5amT2aS2/ZAsjTW2Ei0SY5D5xaN0SY5LuhddWq+FvIPjzkRGYHiTE+HDh2we/dupKeno3v37njppZcQGRmJd955B61bt9aijSFLtjqIUFrDKJivRYaaLBmE0vlDRKFJcdDz9NNPo6ysDADw3HPP4eabb8bVV1+NxMRELFu2TPUGhjIZ6yACXQRURsF4LbLUZMkilM4fIgo9ioOegQMHuv4/IyMDv/zyC/73v/+hcePGrhFc5J9Qr4M4UVqBorIqlFacR0JMAzSOjURKQrTezaqTv5kbmWqyiIiobgHP01NTkyZN1HgY09FytJPejpwuw7RP97jNnNw7IxEv3toRLRIb6tgy35Rkbjg3DRGRsfgd9IwePdqv/RYvXhxwY8woFOsgTpRW1Ap4AGBL7mk89ekevDKss5QZH6WZG9lqskhbwardYo0YkXb8DnqWLl2Kli1bIisryzUhIakj1OogisqqPK6NBVwIfIrKqqQMepRmbmSsySJtBKt2izViRNryO+gZO3YssrOzkZeXh1GjRuGee+5htxZ5VFpxvl6360Vp5ibUa7LogmDVbrFGjEh7fs/T88Ybb+D48eN48sknsXr1aqSlpWHYsGFYt24dMz/kJiHadyxd1+16UZq54dw05hCs+bRkmreLKFQpuvpERUVh+PDhGD58OA4fPoylS5fikUcewfnz5/Hf//4XcXFxWrWTDKRxw0j0zkjEFg9dXL0zEtG4oZzBQCCZm1CsySJ3wardYo0YkfYUz8jsumNYmGvBUS4ySjWlJETjxVs7ondGott25+gtGet5gMAzN3rPQE3aClbtFmvEiLSnKNNTWVmJlStXYvHixdiyZQtuvvlmLFq0CNdffz3CwgKOnygEtUhsiFeGdb44T090AzRu6P88PXqNYGHmhi4VrNot1ogRac8i/CzIeeSRR/DJJ58gLS0No0ePxogRI5CUlFT3HXVWWloKq9WKkpISJCQk6N0c8gNHsJBs8ovPep1Pq5nKo7eC8TxERqDF9dvvoCcsLAwtWrRAVlaWz5mXV65cqUrD1MKgx1hKyqswPtvmsaCzT2YSR7CQbpzZx2Ct5cZMI5mdFtdvv7u37rvvPi4zEaJkmgzt5JlKznJMUgrWfFqhNm8XkUwUTU5IoUemrqT84rM48r9yn/twBAsREQWK1ccmVtdkaCXlwZsXxNmWunAECxERBYpBj4nJNBmasy22o8XodclQdyeOYCEiovqQc2pcCgqZJkNztmXxljwsHJ4FAG7rd12dmYQXb+1ouloHmeqtiIiMjkGPick0GZqzLeVV1ZiQbcPo3ukY3SsdlecdiGoQhsviozBz9X8xc3AH0wxbl6neiogoFDDoMTGZJkOr2Zbyqmos2pDruq1XRiKyWjTG+n0nUXneHAsvcvFJIiL1sabHxGRaMNNbW3plJGJUr3Qs3pIHwL3WqKS8CgdP2mE7UoSDp+xBLbzWmkz1VkREoYKZHpOTadkFZ1uOFZ/Fb6fLEdUgDLajxZiQbUN51cX13coqz4V8149M9VZERKGCQQ9JNRmaNTYShfYqPPLRj973iYkM+a4fmeqtiIhChdTdW7Nnz8af/vQnxMfHIzk5GUOGDMH+/fv1bhZpzFnf40mfzCRUVTtCvuunrmPAoftERMpJHfRs2rQJ48aNw/bt27F+/XqcO3cO1113HcrKyvRuGmmorloje+V5n/cPha4fmeqtiIhChd8Ljsrg1KlTSE5OxqZNm9CnTx+/7sMFR43L28KLB0/a0W/+Jq/3y5ncF22S44LYUu1w8Ulz4bxMRBfpuuCoDEpKSgAATZo08bpPZWUlKisrXX+XlpZq3i7ShrdaI5mG2mtNpnor0laoF+cTyUDq7q2aHA4HJk2ahF69eqFDhw5e95s9ezasVqvrX1paWhBbScHArh8KNTKtg0cUygzTvTV27FisWbMGW7ZsQfPmzb3u5ynTk5aWxu6tEMSuHwoVZuqyJfKXabu3xo8fjy+++AKbN2/2GfAAQFRUFKKiooLUMtITu34oVHBeJqLgkDroEULg0UcfxaeffoqNGzciPT1d7yYFjAWKpDWeY8bFeZmIgkPqoGfcuHH4+OOP8dlnnyE+Ph4FBQUAAKvVipgY4xT2sUCRtMZzzNjMVJxPpCepa3osFovH7UuWLMH999/v12PoPWS9pLwK47NtHifT65OZFBKzB5O+eI6Fhvzis5i64ie3wMdZnN+MgSuZkOlqeiSOx/zmz8KRvCBRffAcCw0yrYNHFKqkDnpCAQsUSWs8x0IHi/OJtGWYeXqMigWKpDWeY0RE/mHQozEuHHlBSXkVDp60w3akCAdP2V2TrXnbTv7jOUZE5B+pC5nVoHchM8ACRW8ji14Y0gHPffEzvt530m07RxwpZ/ZzjIhCjxbXbwY9QWLW2YN9jSzqnZGIzi0aY9GGXLftHHEUGLOeY0QUmkw3eiuUmLVA0dfIoi25pzGqV+0JJzniKDBmPceIiPzFmh7SVF0jiyrPOzxu54gjIiJSG4Me0lRdI4uiGng+BTniiIiI1MaghzTla2RR74xE2I4W19rOEUdERKQFBj2kKWtsJObc3qlW4NMnMwkv3toR+4+X1to+9/ZOrE0hIiLVcfQWBYW3kUUccURERJ5w9BYZlreRRRxxREREwcLuLSIiIjIFBj1ERERkCgx6iIiIyBQY9BAREZEpMOghIiIiU2DQQ0RERKbAoIeIiIhMgfP0EJHmnJNQllacQ0JMBJIaBm9+Jj2fm4jkwqCHiDSVX3wWU1b8hG8PFLq29clMwpzbOyG1UUzIPjcRyYfdW0SkmZLyqlpBBwBsPlCIqSt+Qkl5VUg+NxHJiUEPEWmm0F5VK+hw2nygEIV27QIPPZ+biOTEoIeINFNacc7n7WfquN2oz01EcmLQQ0SaSYiO8Hl7fB23G/W5iUhODHqISDNJcZHok5nk8bY+mUlIitNuFJWez01EcmLQQ0SascZGYs7tnWoFH30ykzD39k6aDh3X87mJSE4WIYTQuxFaKi0thdVqRUlJCRISEvRuDpEpOefKOVNxDvHREUiKC/48PXo8NxEFTovrN+fpISLNWWP1CzT0fG4ikgu7t4iIiMgUGPQQERGRKTDoISIiIlNgTQ+RhGRYJFOGNhARqYlBD5FkZFgkU4Y2EBGpjd1bRBKRYZFMGdpARKQFBj1EEpFhkUwZ2kBEpAUGPUQSkWGRTBnaQESkBQY9RBKRYZFMGdpARKQFBj1EEpFhkUwZ2kBEpAUGPUQSkWGRTBnaQESkBS44SiQhGRbJlKENRGReXHCUyCRkWCRThjYQEamJ3VtERERkCgx6iIiIyBQY9BAREZEpMOghIiIiU2DQQ0RERKbAoIeIiIhMgUEPERERmQKDHiIiIjIFBj1ERERkCgx6iIiIyBQY9BAREZEpMOghIiIiU2DQQ0RERKZgiKDnjTfeQKtWrRAdHY3u3btjx44dejeJ/FBSXoWDJ+2wHSnCwVN2lJRX6d0kMhCeP0SktgZ6N6Auy5Ytw+TJk/H222+je/fuWLBgAQYOHIj9+/cjOTlZ7+aRF/nFZzFlxU/49kCha1ufzCTMub0TUhvF6NgyMgKeP0SkBekzPfPnz8dDDz2EUaNGoV27dnj77bcRGxuLxYsX69008qKkvKrWBQsANh8oxNQVP/EXO/nE84eItCJ10FNVVYUffvgB/fv3d20LCwtD//79sW3bNo/3qaysRGlpqds/Cq5Ce1WtC5bT5gOFKLTzokXe8fwhIq1IHfQUFhaiuroaKSkpbttTUlJQUFDg8T6zZ8+G1Wp1/UtLSwtGU6mG0opzPm8/U8ftZG48f4hIK1IHPYGYNm0aSkpKXP+OHj2qd5NMJyE6wuft8XXcTubG84eItCJ10JOUlITw8HCcOHHCbfuJEyfQtGlTj/eJiopCQkKC2z8KrqS4SPTJTPJ4W5/MJCTFRQa5RWQkPH+ISCtSBz2RkZG46qqrkJOT49rmcDiQk5ODHj166Ngy8sUaG4k5t3eqdeHqk5mEubd3gjWWFy3yjucPEWlF+iHrkydPxsiRI9G1a1d069YNCxYsQFlZGUaNGqV308iH1EYxeH14FgrtVThTcQ7x0RFIiovkBYv8wvOHiLQgfdBz55134tSpU5g+fToKCgrQuXNnrF27tlZxM8nHGsuLFAWO5w8Rqc0ihBB6N0JLpaWlsFqtKCkpYX0PERGRQWhx/Za6poeIiIhILQx6iIiIyBQY9BAREZEpMOghIiIiU2DQQ0RERKbAoIeIiIhMgUEPERERmQKDHiIiIjIFBj1ERERkCtIvQ1FfzgmnS0tLdW4JERER+ct53VZz4YiQD3rOnDkDAEhLS9O5JURERKTUmTNnYLVaVXmskF97y+FwID8/H/Hx8bBYLKo9bmlpKdLS0nD06FHTr+nFY3ERj8VFPBYX8VhcxGNxEY/FRZ6OhRACZ86cQWpqKsLC1KnGCflMT1hYGJo3b67Z4yckJJj+ZHXisbiIx+IiHouLeCwu4rG4iMfiokuPhVoZHicWMhMREZEpMOghIiIiU2DQE6CoqCjMmDEDUVFRejdFdzwWF/FYXMRjcRGPxUU8FhfxWFwUrGMR8oXMRERERAAzPURERGQSDHqIiIjIFBj0EBERkSkw6CEiIiJTYNBTwxtvvIFWrVohOjoa3bt3x44dO3zu///+3//D5ZdfjujoaHTs2BH//ve/3W4XQmD69Olo1qwZYmJi0L9/fxw4cEDLl6AaJcfi3XffxdVXX43GjRujcePG6N+/f63977//flgsFrd/119/vdYvQxVKjsXSpUtrvc7o6Gi3fcxyXlxzzTW1joXFYsFNN93k2seI58XmzZsxaNAgpKamwmKxYNWqVXXeZ+PGjejSpQuioqKQkZGBpUuX1tpH6fePDJQei5UrV2LAgAG47LLLkJCQgB49emDdunVu+zz77LO1zonLL79cw1ehDqXHYuPGjR4/HwUFBW77meG88PQ9YLFY0L59e9c+ap0XDHr+f8uWLcPkyZMxY8YM/Pjjj7jyyisxcOBAnDx50uP+3333HYYPH44HHngANpsNQ4YMwZAhQ7B3717XPi+99BIWLlyIt99+G99//z0aNmyIgQMHoqKiIlgvKyBKj8XGjRsxfPhwfPPNN9i2bRvS0tJw3XXX4dixY277XX/99Th+/LjrX3Z2djBeTr0oPRbAhRlFa77Ow4cPu91ulvNi5cqVbsdh7969CA8Px9ChQ932M9p5UVZWhiuvvBJvvPGGX/vn5eXhpptuwrXXXotdu3Zh0qRJePDBB90u9oGcZzJQeiw2b96MAQMG4N///jd++OEHXHvttRg0aBBsNpvbfu3bt3c7J7Zs2aJF81Wl9Fg47d+/3+21Jicnu24zy3nx2muvuR2Do0ePokmTJrW+K1Q5LwQJIYTo1q2bGDdunOvv6upqkZqaKmbPnu1x/2HDhombbrrJbVv37t3FX//6VyGEEA6HQzRt2lS8/PLLrtuLi4tFVFSUyM7O1uAVqEfpsbjU+fPnRXx8vPjggw9c20aOHCkGDx6sdlM1p/RYLFmyRFitVq+PZ+bz4tVXXxXx8fHCbre7thn1vHACID799FOf+zz55JOiffv2btvuvPNOMXDgQNff9T22MvDnWHjSrl07MXPmTNffM2bMEFdeeaV6DdOBP8fim2++EQBEUVGR133Mel58+umnwmKxiN9++821Ta3zgpkeAFVVVfjhhx/Qv39/17awsDD0798f27Zt83ifbdu2ue0PAAMHDnTtn5eXh4KCArd9rFYrunfv7vUxZRDIsbhUeXk5zp07hyZNmrht37hxI5KTk9G2bVuMHTsWp0+fVrXtagv0WNjtdrRs2RJpaWkYPHgw/vvf/7puM/N58f777+Ouu+5Cw4YN3bYb7bxQqq7vCjWOrVE5HA6cOXOm1nfFgQMHkJqaitatW2PEiBE4cuSITi3UXufOndGsWTMMGDAAW7dudW0383nx/vvvo3///mjZsqXbdjXOCwY9AAoLC1FdXY2UlBS37SkpKbX6V50KCgp87u/8r5LHlEEgx+JSU6ZMQWpqqtuH9frrr8c//vEP5OTkYO7cudi0aRNuuOEGVFdXq9p+NQVyLNq2bYvFixfjs88+w4cffgiHw4GePXvi999/B2De82LHjh3Yu3cvHnzwQbftRjwvlPL2XVFaWoqzZ8+q8pkzqnnz5sFut2PYsGGubd27d8fSpUuxdu1avPXWW8jLy8PVV1+NM2fO6NhS9TVr1gxvv/02VqxYgRUrViAtLQ3XXHMNfvzxRwDqfBcbUX5+PtasWVPru0Kt8yLkV1mn4JozZw4++eQTbNy40a2A96677nL9f8eOHdGpUye0adMGGzduRL9+/fRoqiZ69OiBHj16uP7u2bMnrrjiCvz973/H888/r2PL9PX++++jY8eO6Natm9t2s5wXVNvHH3+MmTNn4rPPPnOrY7nhhhtc/9+pUyd0794dLVu2xPLly/HAAw/o0VRNtG3bFm3btnX93bNnTxw8eBCvvvoq/vnPf+rYMn198MEHaNSoEYYMGeK2Xa3zgpkeAElJSQgPD8eJEyfctp84cQJNmzb1eJ+mTZv63N/5XyWPKYNAjoXTvHnzMGfOHHz11Vfo1KmTz31bt26NpKQk5Obm1rvNWqnPsXCKiIhAVlaW63Wa8bwoKyvDJ5984tcXkxHOC6W8fVckJCQgJiZGlfPMaD755BM8+OCDWL58ea2uv0s1atQIf/zjH0PqnPCmW7durtdpxvNCCIHFixfj3nvvRWRkpM99Az0vGPQAiIyMxFVXXYWcnBzXNofDgZycHLdf7TX16NHDbX8AWL9+vWv/9PR0NG3a1G2f0tJSfP/9914fUwaBHAvgwoik559/HmvXrkXXrl3rfJ7ff/8dp0+fRrNmzVRptxYCPRY1VVdXY8+ePa7XabbzArgwtUNlZSXuueeeOp/HCOeFUnV9V6hxnhlJdnY2Ro0ahezsbLfpC7yx2+04ePBgSJ0T3uzatcv1Os12XgDApk2bkJub69cPpIDPi3qXQoeITz75RERFRYmlS5eKn3/+WYwZM0Y0atRIFBQUCCGEuPfee8XUqVNd+2/dulU0aNBAzJs3T+zbt0/MmDFDREREiD179rj2mTNnjmjUqJH47LPPxE8//SQGDx4s0tPTxdmzZ4P++pRQeizmzJkjIiMjxb/+9S9x/Phx178zZ84IIYQ4c+aMeOKJJ8S2bdtEXl6e+Prrr0WXLl1EZmamqKio0OU1+kvpsZg5c6ZYt26dOHjwoPjhhx/EXXfdJaKjo8V///tf1z5mOS+cevfuLe68885a2416Xpw5c0bYbDZhs9kEADF//nxhs9nE4cOHhRBCTJ06Vdx7772u/Q8dOiRiY2PF3/72N7Fv3z7xxhtviPDwcLF27VrXPnUdW1kpPRYfffSRaNCggXjjjTfcviuKi4td+zz++ONi48aNIi8vT2zdulX0799fJCUliZMnTwb99Smh9Fi8+uqrYtWqVeLAgQNiz549YuLEiSIsLEx8/fXXrn3Mcl443XPPPaJ79+4eH1Ot84JBTw2vv/66aNGihYiMjBTdunUT27dvd93Wt29fMXLkSLf9ly9fLv74xz+KyMhI0b59e/Hll1+63e5wOMQzzzwjUlJSRFRUlOjXr5/Yv39/MF5KvSk5Fi1bthQAav2bMWOGEEKI8vJycd1114nLLrtMREREiJYtW4qHHnpI+g+uk5JjMWnSJNe+KSkp4sYbbxQ//vij2+OZ5bwQQohffvlFABBfffVVrccy6nnhHGp86T/nax85cqTo27dvrft07txZREZGitatW4slS5bUelxfx1ZWSo9F3759fe4vxIXh/M2aNRORkZHiD3/4g7jzzjtFbm5ucF9YAJQei7lz54o2bdqI6Oho0aRJE3HNNdeIDRs21HpcM5wXQlyYuiMmJka88847Hh9TrfPCIoQQynJDRERERMbDmh4iIiIyBQY9REREZAoMeoiIiMgUGPQQERGRKTDoISIiIlNg0ENERESmwKCHiIiITIFBDxEREZkCgx4iIpW0atUKCxYs0LsZROQFgx4iEyooKMDEiRORkZGB6OhopKSkoFevXnjrrbdQXl7u2q9Vq1awWCywWCyIiYlBq1atMGzYMGzYsMHt8X777TfXfhaLBYmJibjuuutgs9mC/dJ0tXPnTowZM8b1t8ViwapVq/RrEBG5YdBDZDKHDh1CVlYWvvrqK7z44ouw2WzYtm0bnnzySXzxxRf4+uuv3fZ/7rnncPz4cezfvx//+Mc/0KhRI/Tv3x+zZs2q9dhff/01jh8/jnXr1sFut+OGG25AcXFxkF7ZBefOnQvq89V02WWXITY2VrfnJ6I6KF6ti4gMbeDAgaJ58+bCbrd7vN3hcLj+v2XLluLVV1+ttc/06dNFWFiY+OWXX4QQQuTl5QkAwmazufbZunWrAOC2mnhNM2bMEFdeeaV4++23RfPmzUVMTIwYOnSo24rbQgjx7rvvissvv1xERUWJtm3bijfeeMN1m/N5P/nkE9GnTx8RFRXlcTFPIYQoKioSY8aMEcnJySIqKkq0b99erF69WgghRGFhobjrrrtEamqqiImJER06dBAff/yx2/379u0rxo0bJ8aNGycSEhJEYmKiePrpp70er0sX4m3ZsqUQQojc3Fxxyy23iOTkZNGwYUPRtWtXsX79eo9tJiJ1MdNDZCKnT5/GV199hXHjxqFhw4Ye97FYLHU+zsSJEyGEwGeffeZ1n5iYGABAVVWV131yc3OxfPlyrF69GmvXroXNZsMjjzziuv2jjz7C9OnTMWvWLOzbtw8vvvginnnmGXzwwQdujzN16lRMnDgR+/btw8CBA2s9j8PhwA033ICtW7fiww8/xM8//4w5c+YgPDwcAFBRUYGrrroKX375Jfbu3YsxY8bg3nvvxY4dO9we54MPPkCDBg2wY8cOvPbaa5g/fz7ee+89j69t586dAIAlS5bg+PHjrr/tdjtuvPFG5OTkwGaz4frrr8egQYNw5MgRr8eJiFSid9RFRMGzfft2AUCsXLnSbXtiYqJo2LChaNiwoXjyySdd271leoQQIiUlRYwdO1YIUTvTU1RUJG699VYRFxcnCgoKPN5/xowZIjw8XPz++++ubWvWrBFhYWHi+PHjQggh2rRpUyvj8vzzz4sePXq4Pe+CBQt8vu5169aJsLAwsX//fp/71XTTTTeJxx9/3PV33759xRVXXOGW2ZkyZYq44oorXH9ferwAiE8//bTO52rfvr14/fXX/W4bEQWmgb4hFxHJYMeOHXA4HBgxYgQqKyv9uo8QolZWqGfPnggLC0NZWRlat26NZcuWISUlxetjtGjRAn/4wx9cf/fo0QMOhwP79+9HfHw8Dh48iAceeAAPPfSQa5/z58/DarW6PU7Xrl19tnXXrl1o3rw5/vjHP3q8vbq6Gi+++CKWL1+OY8eOoaqqCpWVlbXqc/785z+7veYePXrglVdeQXV1tStrVBe73Y5nn30WX375JY4fP47z58/j7NmzzPQQBQGDHiITycjIgMViwf79+922t27dGsDFLqm6nD59GqdOnUJ6errb9mXLlqFdu3ZITExEo0aN6tVWu90OAHj33XfRvXt3t9suDTC8ddU51fW6Xn75Zbz22mtYsGABOnbsiIYNG2LSpEk+u+YC9cQTT2D9+vWYN28eMjIyEBMTgzvuuEOT5yIidwx6iEwkMTERAwYMwKJFi/Doo4/WGSx489prryEsLAxDhgxx256WloY2bdr4/ThHjhxBfn4+UlNTAQDbt29HWFgY2rZti5SUFKSmpuLQoUMYMWJEQO106tSpE37//Xf8+uuvHrM9W7duxeDBg3HPPfcAuFAD9Ouvv6Jdu3Zu+33//fduf2/fvh2ZmZleszwRERGorq6u9Vz3338/br31VgAXgrvffvst0JdGRAqwkJnIZN58802cP38eXbt2xbJly7Bv3z7s378fH374IX755ZdaF/AzZ86goKAAR48exebNmzFmzBi88MILmDVrFjIyMurVlujoaIwcORK7d+/Gt99+iwkTJmDYsGFo2rQpAGDmzJmYPXs2Fi5ciF9//RV79uzBkiVLMH/+fEXP07dvX/Tp0we333471q9fj7y8PKxZswZr164FAGRmZmL9+vX47rvvsG/fPvz1r3/FiRMnaj3OkSNHMHnyZOzfvx/Z2dl4/fXXMXHiRK/P26pVK+Tk5KCgoABFRUWu51q5ciV27dqF3bt34+6774bD4VD0eogoQHoXFRFR8OXn54vx48eL9PR0ERERIeLi4kS3bt3Eyy+/LMrKylz71Rx2HRkZKVq0aCGGDRsmNmzY4PZ4noas18U5ZP3NN98UqampIjo6Wtxxxx3if//7n9t+H330kejcubOIjIwUjRs3Fn369HEVYit53tOnT4tRo0aJxMREER0dLTp06CC++OIL122DBw8WcXFxIjk5WTz99NPivvvuE4MHD3bdv2/fvuKRRx4RDz/8sEhISBCNGzcWTz31lM8h/p9//rnIyMgQDRo0cA1Zz8vLE9dee62IiYkRaWlpYtGiRaJv375i4sSJfh87IgqMRQgh9A27iMiMnn32WaxatQq7du3Suyl+ueaaa9C5c2cuM0FkYOzeIiIiIlNg0ENERESmwO4tIiIiMgVmeoiIiMgUGPQQERGRKTDoISIiIlNg0ENERESmwKCHiIiITIFBDxEREZkCgx4iIiIyBQY9REREZAr/H2l9KBCLaEUAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# regression model plot\n",
        "\n",
        "sns.regplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "mF0ZZjiArn6F",
        "outputId": "3b53c804-b174-4272-8287-b7ad489a1392"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GDP per capita', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation between Social Support and Maximum Infection Rates\n",
        "x = data[\"Social support\"]\n",
        "\n",
        "sns.scatterplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Imw3aHGqrzyN",
        "outputId": "671c6943-5ad1-4ab2-f41d-71dfff46a4fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Social support', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "KCY43ru0sCmF",
        "outputId": "fabd5e62-d9ad-4973-bf43-68107a20ea7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Social support', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation between Max Infec Rates and Healthy life expectancy\n",
        "\n",
        "x = data[\"Healthy life expectancy\"]\n",
        "\n",
        "sns.scatterplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "7BG5VbsmsIwF",
        "outputId": "07217d66-2d98-4a00-b5a1-298a2aa3b3c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Healthy life expectancy', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "upAhltA_sdDn",
        "outputId": "c21dcbf3-690f-4b7e-8325-07b358d7c189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Healthy life expectancy', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation between Max Infec Rates and Freedom to make life choices\n",
        "\n",
        "x = data[\"Freedom to make life choices\"]\n",
        "\n",
        "sns.scatterplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "mM9g_Xa4sf20",
        "outputId": "774f2701-2576-4e39-a11a-786584904709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Freedom to make life choices', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=x, y=np.log(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "sVS8fAA2s1f9",
        "outputId": "4638cf7a-3518-4446-e58e-20acbfcf39a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Freedom to make life choices', ylabel='Maximum Infection Rates'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}