covid_cases_analysis.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/divyanshu-mayank/python-data-analysis-projects/blob/main/covid_cases_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.25.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "MzAH1zAe8kSy"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/divyanshu-mayank/csv_datasets/main/covid_data.csv\")"
      ],
      "metadata": {
        "id": "cTP_tOvW8l2H"
      },
      "execution_count": 65,
      "outputs": []
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        "outputId": "684c01bf-87d7-452a-cf06-b398c8858b3a"
      },
      "execution_count": 66,
      "outputs": [
        {
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          "data": {
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              "          Date     State          Region  Confirmed  Deaths  Recovered\n",
              "0    4/29/2020       NaN     Afghanistan       1939      60        252\n",
              "1    4/29/2020       NaN         Albania        766      30        455\n",
              "2    4/29/2020       NaN         Algeria       3848     444       1702\n",
              "3    4/29/2020       NaN         Andorra        743      42        423\n",
              "4    4/29/2020       NaN          Angola         27       2          7\n",
              "..         ...       ...             ...        ...     ...        ...\n",
              "316  4/29/2020   Wyoming              US        545       7          0\n",
              "317  4/29/2020  Xinjiang  Mainland China         76       3         73\n",
              "318  4/29/2020     Yukon          Canada         11       0          0\n",
              "319  4/29/2020    Yunnan  Mainland China        185       2        181\n",
              "320  4/29/2020  Zhejiang  Mainland China       1268       1       1263\n",
              "\n",
              "[321 rows x 6 columns]"
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 321,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"4/29/2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 137,\n        \"samples\": [\n          \"Saint Pierre and Miquelon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 187,\n        \"samples\": [\n          \"Hong Kong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31923,\n        \"min\": 0,\n        \"max\": 299691,\n        \"num_unique_values\": 282,\n        \"samples\": [\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3236,\n        \"min\": 0,\n        \"max\": 27682,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          2373\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14364,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRC0ZVZL8wsC",
        "outputId": "dbaa6eb9-e2fa-4d6e-cab4-830a19873fe4"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date         321\n",
              "State        140\n",
              "Region       321\n",
              "Confirmed    321\n",
              "Deaths       321\n",
              "Recovered    321\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaCexi2Z9Vjz",
        "outputId": "ce0943c0-5af2-4990-bb37-d797d46ad840"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date           0\n",
              "State        181\n",
              "Region         0\n",
              "Confirmed      0\n",
              "Deaths         0\n",
              "Recovered      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "CrOo0URo-AuQ"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "2CMTPICk-QW8"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.isnull())\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "YQ0kvNe_-TtU",
        "outputId": "1414a58c-25d9-4fae-e3ce-5a285e0a0d0e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "CuaH30Gz_qtX",
        "outputId": "ae997f6c-6a32-4222-ded5-c25bf6b13607"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date State       Region  Confirmed  Deaths  Recovered\n",
              "0  4/29/2020   NaN  Afghanistan       1939      60        252\n",
              "1  4/29/2020   NaN      Albania        766      30        455"
            ],
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              "      <td>4/29/2020</td>\n",
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              "        const element = document.querySelector('#df-61853c65-e535-49eb-9d4c-4b5ac91f86eb');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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",
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              "\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-4ca14d44-2bbd-4f09-a7fc-bd8ea756773b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 321,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"4/29/2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 137,\n        \"samples\": [\n          \"Saint Pierre and Miquelon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 187,\n        \"samples\": [\n          \"Hong Kong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31923,\n        \"min\": 0,\n        \"max\": 299691,\n        \"num_unique_values\": 282,\n        \"samples\": [\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3236,\n        \"min\": 0,\n        \"max\": 27682,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          2373\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14364,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Region').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "9m5L_OPSAUz-",
        "outputId": "b6f2446c-dd3d-4661-c072-f364213d79f9"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Date State  Confirmed  Deaths  Recovered\n",
              "Region                                                           \n",
              "Afghanistan         4/29/2020     0       1939      60        252\n",
              "Albania             4/29/2020     0        766      30        455\n",
              "Algeria             4/29/2020     0       3848     444       1702\n",
              "Andorra             4/29/2020     0        743      42        423\n",
              "Angola              4/29/2020     0         27       2          7\n",
              "...                       ...   ...        ...     ...        ...\n",
              "West Bank and Gaza  4/29/2020     0        344       2         71\n",
              "Western Sahara      4/29/2020     0          6       0          5\n",
              "Yemen               4/29/2020     0          6       0          1\n",
              "Zambia              4/29/2020     0         97       3         54\n",
              "Zimbabwe            4/29/2020     0         32       4          5\n",
              "\n",
              "[187 rows x 5 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-80c246ad-92ae-438e-88c1-1f6e01e416cb\" class=\"colab-df-container\">\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>State</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>1939</td>\n",
              "      <td>60</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>766</td>\n",
              "      <td>30</td>\n",
              "      <td>455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>3848</td>\n",
              "      <td>444</td>\n",
              "      <td>1702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>743</td>\n",
              "      <td>42</td>\n",
              "      <td>423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>344</td>\n",
              "      <td>2</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Western Sahara</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yemen</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zambia</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>3</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>187 rows × 5 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-80c246ad-92ae-438e-88c1-1f6e01e416cb')\"\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",
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              "      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-80c246ad-92ae-438e-88c1-1f6e01e416cb 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-80c246ad-92ae-438e-88c1-1f6e01e416cb');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b994ef0-25a1-4ec4-9059-3bcd45e4e8d8')\"\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",
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              "    </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",
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              "      --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",
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              "  }\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",
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              "      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-8b994ef0-25a1-4ec4-9059-3bcd45e4e8d8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
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        "data.groupby('Region')['Confirmed'].sum().sort_values(ascending=False)"
      ],
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        },
        "id": "D_PKhG3rAZzN",
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      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "US                       1039909\n",
              "Spain                     236899\n",
              "Italy                     203591\n",
              "France                    166543\n",
              "UK                        166441\n",
              "                          ...   \n",
              "Sao Tome and Principe          8\n",
              "Papua New Guinea               8\n",
              "Bhutan                         7\n",
              "Western Sahara                 6\n",
              "Yemen                          6\n",
              "Name: Confirmed, Length: 187, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
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        "data.groupby('Region')[['Confirmed','Recovered']].sum()"
      ],
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      "execution_count": 75,
      "outputs": [
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              "                    Confirmed  Recovered\n",
              "Region                                  \n",
              "Afghanistan              1939        252\n",
              "Albania                   766        455\n",
              "Algeria                  3848       1702\n",
              "Andorra                   743        423\n",
              "Angola                     27          7\n",
              "...                       ...        ...\n",
              "West Bank and Gaza        344         71\n",
              "Western Sahara              6          5\n",
              "Yemen                       6          1\n",
              "Zambia                     97         54\n",
              "Zimbabwe                   32          5\n",
              "\n",
              "[187 rows x 2 columns]"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 187,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 187,\n        \"samples\": [\n          \"Zambia\",\n          \"Indonesia\",\n          \"Eswatini\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82550,\n        \"min\": 6,\n        \"max\": 1039909,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          480,\n          582,\n          82862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18836,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 154,\n        \"samples\": [\n          2072,\n          17,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 75
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "data[data.Confirmed < 10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
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        "id": "0xsuAYubBkJb",
        "outputId": "5e7c8a39-755a-4f41-99b7-26851b939d87"
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      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date                             State                 Region  \\\n",
              "18   4/29/2020                               NaN                 Bhutan   \n",
              "98   4/29/2020                               NaN             MS Zaandam   \n",
              "105  4/29/2020                               NaN             Mauritania   \n",
              "126  4/29/2020                               NaN       Papua New Guinea   \n",
              "140  4/29/2020                               NaN  Sao Tome and Principe   \n",
              "177  4/29/2020                               NaN         Western Sahara   \n",
              "178  4/29/2020                               NaN                  Yemen   \n",
              "184  4/29/2020                          Anguilla                     UK   \n",
              "192  4/29/2020  Bonaire, Sint Eustatius and Saba            Netherlands   \n",
              "194  4/29/2020            British Virgin Islands                     UK   \n",
              "203  4/29/2020      Diamond Princess cruise ship                 Canada   \n",
              "272  4/29/2020             Northwest Territories                 Canada   \n",
              "284  4/29/2020                         Recovered                 Canada   \n",
              "285  4/29/2020                         Recovered                     US   \n",
              "288  4/29/2020                  Saint Barthelemy                 France   \n",
              "289  4/29/2020         Saint Pierre and Miquelon                 France   \n",
              "305  4/29/2020                             Tibet         Mainland China   \n",
              "\n",
              "     Confirmed  Deaths  Recovered  \n",
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              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MS Zaandam</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mauritania</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Western Sahara</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Anguilla</td>\n",
              "      <td>UK</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Bonaire, Sint Eustatius and Saba</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>British Virgin Islands</td>\n",
              "      <td>UK</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Diamond Princess cruise ship</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Northwest Territories</td>\n",
              "      <td>Canada</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Recovered</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Recovered</td>\n",
              "      <td>US</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Saint Barthelemy</td>\n",
              "      <td>France</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Saint Pierre and Miquelon</td>\n",
              "      <td>France</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Tibet</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-ba215ed5-701b-4971-ac6d-a97d4988d5bb 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-ba215ed5-701b-4971-ac6d-a97d4988d5bb');\n",
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              "          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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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\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",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      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-0c33edf9-7c2b-4027-aa80-ecacfeb24c8e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[~(data['Confirmed'] < 10)]"
      ],
      "metadata": {
        "id": "9u6tJm1OCGvl"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mwPtM91cL39A",
        "outputId": "c7eea0db-f036-4623-8e9e-11dc41a5e1a5"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date     State          Region  Confirmed  Deaths  Recovered\n",
              "0    4/29/2020       NaN     Afghanistan       1939      60        252\n",
              "1    4/29/2020       NaN         Albania        766      30        455\n",
              "2    4/29/2020       NaN         Algeria       3848     444       1702\n",
              "3    4/29/2020       NaN         Andorra        743      42        423\n",
              "4    4/29/2020       NaN          Angola         27       2          7\n",
              "..         ...       ...             ...        ...     ...        ...\n",
              "316  4/29/2020   Wyoming              US        545       7          0\n",
              "317  4/29/2020  Xinjiang  Mainland China         76       3         73\n",
              "318  4/29/2020     Yukon          Canada         11       0          0\n",
              "319  4/29/2020    Yunnan  Mainland China        185       2        181\n",
              "320  4/29/2020  Zhejiang  Mainland China       1268       1       1263\n",
              "\n",
              "[304 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>State</th>\n",
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              "    <tr>\n",
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              "      <td>766</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>4/29/2020</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>743</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>US</td>\n",
              "      <td>545</td>\n",
              "      <td>7</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Xinjiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>76</td>\n",
              "      <td>3</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Yukon</td>\n",
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              "      <th>319</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Yunnan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>185</td>\n",
              "      <td>2</td>\n",
              "      <td>181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>4/29/2020</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1268</td>\n",
              "      <td>1</td>\n",
              "      <td>1263</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>304 rows × 6 columns</p>\n",
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              "  <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-d70d1dff-a16b-4615-8d31-e270382b81bf 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-d70d1dff-a16b-4615-8d31-e270382b81bf');\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-eadc8ae5-474a-4ebb-9d95-107998897466\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eadc8ae5-474a-4ebb-9d95-107998897466')\"\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",
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              "    </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",
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              "      --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-eadc8ae5-474a-4ebb-9d95-107998897466 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2650356f-e6a5-479a-bc1b-165fd7d4a0af\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2650356f-e6a5-479a-bc1b-165fd7d4a0af button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 304,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"4/29/2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 129,\n        \"samples\": [\n          \"Kentucky\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 180,\n        \"samples\": [\n          \"Bosnia and Herzegovina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32717,\n        \"min\": 10,\n        \"max\": 299691,\n        \"num_unique_values\": 274,\n        \"samples\": [\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3321,\n        \"min\": 0,\n        \"max\": 27682,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          2373\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13064,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Confirmed'] < 10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "1RYuOedVMQYb",
        "outputId": "cb5f21f9-6866-4384-927f-9adb2f3094dd"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Date, State, Region, Confirmed, Deaths, Recovered]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a6ed371e-51ff-4ed9-a996-e370ae405044\" 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>Date</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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-a6ed371e-51ff-4ed9-a996-e370ae405044')\"\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-a6ed371e-51ff-4ed9-a996-e370ae405044 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-a6ed371e-51ff-4ed9-a996-e370ae405044');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Region').Confirmed.sum().sort_values(ascending=False).head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-89gNX5xMbx8",
        "outputId": "7d962a96-f206-45a8-f445-cb4d81a09fb5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "US                1039909\n",
              "Spain              236899\n",
              "Italy              203591\n",
              "France             166536\n",
              "UK                 166432\n",
              "Germany            161539\n",
              "Turkey             117589\n",
              "Russia              99399\n",
              "Iran                93657\n",
              "Mainland China      82861\n",
              "Brazil              79685\n",
              "Canada              52860\n",
              "Belgium             47859\n",
              "Netherlands         38993\n",
              "Peru                33931\n",
              "India               33062\n",
              "Switzerland         29407\n",
              "Ecuador             24675\n",
              "Portugal            24505\n",
              "Saudi Arabia        21402\n",
              "Name: Confirmed, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "znjDK12DOVG7",
        "outputId": "2a9abbdb-86fb-4ba7-af63-21c08654cfb4"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date State                  Region  Confirmed  Deaths  Recovered\n",
              "0   4/29/2020   NaN             Afghanistan       1939      60        252\n",
              "1   4/29/2020   NaN                 Albania        766      30        455\n",
              "2   4/29/2020   NaN                 Algeria       3848     444       1702\n",
              "3   4/29/2020   NaN                 Andorra        743      42        423\n",
              "4   4/29/2020   NaN                  Angola         27       2          7\n",
              "5   4/29/2020   NaN     Antigua and Barbuda         24       3         11\n",
              "6   4/29/2020   NaN               Argentina       4285     214       1192\n",
              "7   4/29/2020   NaN                 Armenia       1932      30        900\n",
              "8   4/29/2020   NaN                 Austria      15402     580      12779\n",
              "9   4/29/2020   NaN              Azerbaijan       1766      23       1267\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 304,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"4/29/2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 129,\n        \"samples\": [\n          \"Kentucky\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 180,\n        \"samples\": [\n          \"Bosnia and Herzegovina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32717,\n        \"min\": 10,\n        \"max\": 299691,\n        \"num_unique_values\": 274,\n        \"samples\": [\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3321,\n        \"min\": 0,\n        \"max\": 27682,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          2373\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13064,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Region').Deaths.sum().sort_values(ascending=True).head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUZJCHN1OkpR",
        "outputId": "2131fc90-75e6-43fc-97d7-8cabe3e3aca7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Cambodia                            0\n",
              "Seychelles                          0\n",
              "Saint Lucia                         0\n",
              "Central African Republic            0\n",
              "Saint Kitts and Nevis               0\n",
              "South Sudan                         0\n",
              "Rwanda                              0\n",
              "Grenada                             0\n",
              "Macau                               0\n",
              "Madagascar                          0\n",
              "Nepal                               0\n",
              "Namibia                             0\n",
              "Saint Vincent and the Grenadines    0\n",
              "Mozambique                          0\n",
              "Holy See                            0\n",
              "Timor-Leste                         0\n",
              "Mongolia                            0\n",
              "Uganda                              0\n",
              "Laos                                0\n",
              "Eritrea                             0\n",
              "Name: Deaths, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
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        },
        "id": "68WZAbfmOv5D",
        "outputId": "adce3a51-2b6c-4bda-a332-5754d4df79d6"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date         object\n",
              "State        object\n",
              "Region       object\n",
              "Confirmed     int64\n",
              "Deaths        int64\n",
              "Recovered     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Date'] = pd.to_datetime(data['Date']).dt.strftime('%d-%m-%Y')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sj1G7_JIPCfT",
        "outputId": "2e714bf5-4233-4bea-b8b6-c504885bbd4c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-84-359686e127b2>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['Date'] = pd.to_datetime(data['Date']).dt.strftime('%d-%m-%Y')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Ih6cSusCPMRW",
        "outputId": "2aefc418-1359-45d2-b2a9-eb64a50f3bf3"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date     State          Region  Confirmed  Deaths  Recovered\n",
              "0    29-04-2020       NaN     Afghanistan       1939      60        252\n",
              "1    29-04-2020       NaN         Albania        766      30        455\n",
              "2    29-04-2020       NaN         Algeria       3848     444       1702\n",
              "3    29-04-2020       NaN         Andorra        743      42        423\n",
              "4    29-04-2020       NaN          Angola         27       2          7\n",
              "..          ...       ...             ...        ...     ...        ...\n",
              "316  29-04-2020   Wyoming              US        545       7          0\n",
              "317  29-04-2020  Xinjiang  Mainland China         76       3         73\n",
              "318  29-04-2020     Yukon          Canada         11       0          0\n",
              "319  29-04-2020    Yunnan  Mainland China        185       2        181\n",
              "320  29-04-2020  Zhejiang  Mainland China       1268       1       1263\n",
              "\n",
              "[304 rows x 6 columns]"
            ],
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              "      <th>0</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1939</td>\n",
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              "      <th>1</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
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              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>3848</td>\n",
              "      <td>444</td>\n",
              "      <td>1702</td>\n",
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              "      <th>3</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>743</td>\n",
              "      <td>42</td>\n",
              "      <td>423</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
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              "      <td>7</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>US</td>\n",
              "      <td>545</td>\n",
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              "      <th>317</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Xinjiang</td>\n",
              "      <td>Mainland China</td>\n",
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              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Yukon</td>\n",
              "      <td>Canada</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Yunnan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>185</td>\n",
              "      <td>2</td>\n",
              "      <td>181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1268</td>\n",
              "      <td>1</td>\n",
              "      <td>1263</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>304 rows × 6 columns</p>\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 304,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"29-04-2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 129,\n        \"samples\": [\n          \"Kentucky\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 180,\n        \"samples\": [\n          \"Bosnia and Herzegovina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Confirmed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32717,\n        \"min\": 10,\n        \"max\": 299691,\n        \"num_unique_values\": 274,\n        \"samples\": [\n          52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3321,\n        \"min\": 0,\n        \"max\": 27682,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          2373\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recovered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13064,\n        \"min\": 0,\n        \"max\": 132929,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data.Region == 'India']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "KwO975DGPNgd",
        "outputId": "4c919c38-95e0-4a1a-9e10-a9388e02eb2e"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date State Region  Confirmed  Deaths  Recovered\n",
              "74  29-04-2020   NaN  India      33062    1079       8437"
            ],
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      "execution_count": 93,
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          "data": {
            "text/plain": [
              "Recovered          0\n",
              "Deaths         60967\n",
              "Confirmed    1039909\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
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      "execution_count": 95,
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              "           Date          State          Region  Confirmed  Deaths  Recovered\n",
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              "57   29-04-2020            NaN          France     165093   24087      48228\n",
              "61   29-04-2020            NaN         Germany     161539    6467     120400\n",
              "167  29-04-2020            NaN          Turkey     117589    3081      44040\n",
              "262  29-04-2020     New Jersey              US     116365    6771          0\n",
              "134  29-04-2020            NaN          Russia      99399     972      10286\n",
              "76   29-04-2020            NaN            Iran      93657    5957      73791\n",
              "22   29-04-2020            NaN          Brazil      79685    5513      34132\n",
              "229  29-04-2020          Hubei  Mainland China      68128    4512      63616\n",
              "249  29-04-2020  Massachusetts              US      60265    3405          0\n",
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              "195  29-04-2020     California              US      48747    1946          0\n",
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              "278  29-04-2020   Pennsylvania              US      46327    2373          0\n",
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              "116  29-04-2020            NaN     Netherlands      38802    4711          0\n",
              "128  29-04-2020            NaN            Peru      33931     943      10037\n",
              "208  29-04-2020        Florida              US      33193    1218          0\n",
              "74   29-04-2020            NaN           India      33062    1079       8437\n",
              "158  29-04-2020            NaN     Switzerland      29407    1716      22600\n",
              "243  29-04-2020      Louisiana              US      27660    1845          0\n",
              "303  29-04-2020          Texas              US      27257     754          0\n",
              "200  29-04-2020    Connecticut              US      26767    2169          0\n",
              "282  29-04-2020         Quebec          Canada      26610    1762          0\n",
              "213  29-04-2020        Georgia              US      25775    1101          0\n",
              "47   29-04-2020            NaN         Ecuador      24675     883       1557\n",
              "131  29-04-2020            NaN        Portugal      24505     973       1470\n",
              "141  29-04-2020            NaN    Saudi Arabia      21402     157       2953\n",
              "248  29-04-2020       Maryland              US      20849    1078          0\n",
              "157  29-04-2020            NaN          Sweden      20302    2462       1005\n",
              "78   29-04-2020            NaN         Ireland      20253    1190      13386\n",
              "107  29-04-2020            NaN          Mexico      17799    1732      11423\n",
              "274  29-04-2020           Ohio              US      17303     937          0\n",
              "233  29-04-2020        Indiana              US      17182     964          0\n",
              "276  29-04-2020        Ontario          Canada      16978    1153          0\n",
              "79   29-04-2020            NaN          Israel      15834     215       8233\n",
              "146  29-04-2020            NaN       Singapore      15641      14       1188\n",
              "124  29-04-2020            NaN        Pakistan      15525     343       3425\n",
              "8    29-04-2020            NaN         Austria      15402     580      12779\n",
              "311  29-04-2020       Virginia              US      14962     522          0\n",
              "33   29-04-2020            NaN           Chile      14885     216       8057\n",
              "199  29-04-2020       Colorado              US      14758     766          0\n",
              "312  29-04-2020     Washington              US      14070     801          0\n",
              "83   29-04-2020            NaN           Japan      13895     413       2368\n",
              "14   29-04-2020            NaN         Belarus      13181      84       2072\n",
              "130  29-04-2020            NaN          Poland      12640     624       3025\n",
              "132  29-04-2020            NaN           Qatar      12564      10       1243"
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              "      <th>61</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>161539</td>\n",
              "      <td>6467</td>\n",
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              "      <th>167</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>117589</td>\n",
              "      <td>3081</td>\n",
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              "      <th>262</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>US</td>\n",
              "      <td>116365</td>\n",
              "      <td>6771</td>\n",
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              "      <th>134</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Russia</td>\n",
              "      <td>99399</td>\n",
              "      <td>972</td>\n",
              "      <td>10286</td>\n",
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              "      <th>76</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iran</td>\n",
              "      <td>93657</td>\n",
              "      <td>5957</td>\n",
              "      <td>73791</td>\n",
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              "      <th>22</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>79685</td>\n",
              "      <td>5513</td>\n",
              "      <td>34132</td>\n",
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              "      <th>229</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>Mainland China</td>\n",
              "      <td>68128</td>\n",
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              "      <th>249</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>US</td>\n",
              "      <td>60265</td>\n",
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              "      <td>US</td>\n",
              "      <td>50358</td>\n",
              "      <td>2215</td>\n",
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              "      <th>195</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>US</td>\n",
              "      <td>48747</td>\n",
              "      <td>1946</td>\n",
              "      <td>0</td>\n",
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              "      <th>15</th>\n",
              "      <td>29-04-2020</td>\n",
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              "      <td>47859</td>\n",
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              "      <td>29-04-2020</td>\n",
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              "      <td>US</td>\n",
              "      <td>46327</td>\n",
              "      <td>2373</td>\n",
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              "      <td>US</td>\n",
              "      <td>40399</td>\n",
              "      <td>3670</td>\n",
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              "      <td>33193</td>\n",
              "      <td>1218</td>\n",
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              "      <th>74</th>\n",
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              "      <td>1079</td>\n",
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              "      <th>243</th>\n",
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              "      <td>Louisiana</td>\n",
              "      <td>US</td>\n",
              "      <td>27660</td>\n",
              "      <td>1845</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Maryland</td>\n",
              "      <td>US</td>\n",
              "      <td>20849</td>\n",
              "      <td>1078</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>20302</td>\n",
              "      <td>2462</td>\n",
              "      <td>1005</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ireland</td>\n",
              "      <td>20253</td>\n",
              "      <td>1190</td>\n",
              "      <td>13386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>17799</td>\n",
              "      <td>1732</td>\n",
              "      <td>11423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Ohio</td>\n",
              "      <td>US</td>\n",
              "      <td>17303</td>\n",
              "      <td>937</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Indiana</td>\n",
              "      <td>US</td>\n",
              "      <td>17182</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Ontario</td>\n",
              "      <td>Canada</td>\n",
              "      <td>16978</td>\n",
              "      <td>1153</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Israel</td>\n",
              "      <td>15834</td>\n",
              "      <td>215</td>\n",
              "      <td>8233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>15641</td>\n",
              "      <td>14</td>\n",
              "      <td>1188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>15525</td>\n",
              "      <td>343</td>\n",
              "      <td>3425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Austria</td>\n",
              "      <td>15402</td>\n",
              "      <td>580</td>\n",
              "      <td>12779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>US</td>\n",
              "      <td>14962</td>\n",
              "      <td>522</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Chile</td>\n",
              "      <td>14885</td>\n",
              "      <td>216</td>\n",
              "      <td>8057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>US</td>\n",
              "      <td>14758</td>\n",
              "      <td>766</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>14070</td>\n",
              "      <td>801</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Japan</td>\n",
              "      <td>13895</td>\n",
              "      <td>413</td>\n",
              "      <td>2368</td>\n",
              "    </tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>Belarus</td>\n",
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              "      <td>2072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Poland</td>\n",
              "      <td>12640</td>\n",
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              "      <td>3025</td>\n",
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              "    <tr>\n",
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              "      <td>29-04-2020</td>\n",
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              "      <td>Qatar</td>\n",
              "      <td>12564</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "data.sort_values(by = 'Recovered', ascending = False).head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ishZtD5YSxVV",
        "outputId": "6d34a6d9-92b9-405b-9d41-b1d7683bf743"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date            State                Region  Confirmed  Deaths  \\\n",
              "153  29-04-2020              NaN                 Spain     236899   24275   \n",
              "61   29-04-2020              NaN               Germany     161539    6467   \n",
              "76   29-04-2020              NaN                  Iran      93657    5957   \n",
              "80   29-04-2020              NaN                 Italy     203591   27682   \n",
              "229  29-04-2020            Hubei        Mainland China      68128    4512   \n",
              "57   29-04-2020              NaN                France     165093   24087   \n",
              "167  29-04-2020              NaN                Turkey     117589    3081   \n",
              "22   29-04-2020              NaN                Brazil      79685    5513   \n",
              "158  29-04-2020              NaN           Switzerland      29407    1716   \n",
              "78   29-04-2020              NaN               Ireland      20253    1190   \n",
              "8    29-04-2020              NaN               Austria      15402     580   \n",
              "107  29-04-2020              NaN                Mexico      17799    1732   \n",
              "15   29-04-2020              NaN               Belgium      47859    7501   \n",
              "134  29-04-2020              NaN                Russia      99399     972   \n",
              "128  29-04-2020              NaN                  Peru      33931     943   \n",
              "151  29-04-2020              NaN           South Korea      10765     247   \n",
              "74   29-04-2020              NaN                 India      33062    1079   \n",
              "79   29-04-2020              NaN                Israel      15834     215   \n",
              "33   29-04-2020              NaN                 Chile      14885     216   \n",
              "42   29-04-2020              NaN               Denmark       9008     443   \n",
              "101  29-04-2020              NaN              Malaysia       5945     100   \n",
              "133  29-04-2020              NaN               Romania      11978     693   \n",
              "124  29-04-2020              NaN              Pakistan      15525     343   \n",
              "97   29-04-2020              NaN            Luxembourg       3769      89   \n",
              "41   29-04-2020              NaN        Czech Republic       7579     227   \n",
              "130  29-04-2020              NaN                Poland      12640     624   \n",
              "141  29-04-2020              NaN          Saudi Arabia      21402     157   \n",
              "56   29-04-2020              NaN               Finland       4906     206   \n",
              "162  29-04-2020              NaN              Thailand       2947      54   \n",
              "83   29-04-2020              NaN                 Japan      13895     413   \n",
              "171  29-04-2020              NaN  United Arab Emirates      11929      98   \n",
              "264  29-04-2020  New South Wales             Australia       3016      40   \n",
              "150  29-04-2020              NaN          South Africa       5350     103   \n",
              "14   29-04-2020              NaN               Belarus      13181      84   \n",
              "2    29-04-2020              NaN               Algeria       3848     444   \n",
              "73   29-04-2020              NaN               Iceland       1797      10   \n",
              "47   29-04-2020              NaN               Ecuador      24675     883   \n",
              "220  29-04-2020        Guangdong        Mainland China       1588       8   \n",
              "131  29-04-2020              NaN              Portugal      24505     973   \n",
              "11   29-04-2020              NaN               Bahrain       2921       8   \n",
              "34   29-04-2020              NaN              Colombia       6207     278   \n",
              "75   29-04-2020              NaN             Indonesia       9771     784   \n",
              "88   29-04-2020              NaN                Kuwait       3740      24   \n",
              "77   29-04-2020              NaN                  Iraq       2003      92   \n",
              "48   29-04-2020              NaN                 Egypt       5268     380   \n",
              "309  29-04-2020         Victoria             Australia       1361      18   \n",
              "38   29-04-2020              NaN               Croatia       2062      67   \n",
              "9    29-04-2020              NaN            Azerbaijan       1766      23   \n",
              "320  29-04-2020         Zhejiang        Mainland China       1268       1   \n",
              "227  29-04-2020            Henan        Mainland China       1276      22   \n",
              "\n",
              "     Recovered  \n",
              "153     132929  \n",
              "61      120400  \n",
              "76       73791  \n",
              "80       71252  \n",
              "229      63616  \n",
              "57       48228  \n",
              "167      44040  \n",
              "22       34132  \n",
              "158      22600  \n",
              "78       13386  \n",
              "8        12779  \n",
              "107      11423  \n",
              "15       11283  \n",
              "134      10286  \n",
              "128      10037  \n",
              "151       9059  \n",
              "74        8437  \n",
              "79        8233  \n",
              "33        8057  \n",
              "42        6366  \n",
              "101       4087  \n",
              "133       3569  \n",
              "124       3425  \n",
              "97        3134  \n",
              "41        3108  \n",
              "130       3025  \n",
              "141       2953  \n",
              "56        2800  \n",
              "162       2665  \n",
              "83        2368  \n",
              "171       2329  \n",
              "264       2284  \n",
              "150       2073  \n",
              "14        2072  \n",
              "2         1702  \n",
              "73        1656  \n",
              "47        1557  \n",
              "220       1557  \n",
              "131       1470  \n",
              "11        1455  \n",
              "34        1411  \n",
              "75        1391  \n",
              "88        1389  \n",
              "77        1346  \n",
              "48        1335  \n",
              "309       1291  \n",
              "38        1288  \n",
              "9         1267  \n",
              "320       1263  \n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>State</th>\n",
              "      <th>Region</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Spain</td>\n",
              "      <td>236899</td>\n",
              "      <td>24275</td>\n",
              "      <td>132929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Germany</td>\n",
              "      <td>161539</td>\n",
              "      <td>6467</td>\n",
              "      <td>120400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iran</td>\n",
              "      <td>93657</td>\n",
              "      <td>5957</td>\n",
              "      <td>73791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Italy</td>\n",
              "      <td>203591</td>\n",
              "      <td>27682</td>\n",
              "      <td>71252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Hubei</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>68128</td>\n",
              "      <td>4512</td>\n",
              "      <td>63616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>France</td>\n",
              "      <td>165093</td>\n",
              "      <td>24087</td>\n",
              "      <td>48228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>117589</td>\n",
              "      <td>3081</td>\n",
              "      <td>44040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>79685</td>\n",
              "      <td>5513</td>\n",
              "      <td>34132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>29407</td>\n",
              "      <td>1716</td>\n",
              "      <td>22600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ireland</td>\n",
              "      <td>20253</td>\n",
              "      <td>1190</td>\n",
              "      <td>13386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Austria</td>\n",
              "      <td>15402</td>\n",
              "      <td>580</td>\n",
              "      <td>12779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>17799</td>\n",
              "      <td>1732</td>\n",
              "      <td>11423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>47859</td>\n",
              "      <td>7501</td>\n",
              "      <td>11283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Russia</td>\n",
              "      <td>99399</td>\n",
              "      <td>972</td>\n",
              "      <td>10286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peru</td>\n",
              "      <td>33931</td>\n",
              "      <td>943</td>\n",
              "      <td>10037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>10765</td>\n",
              "      <td>247</td>\n",
              "      <td>9059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>33062</td>\n",
              "      <td>1079</td>\n",
              "      <td>8437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Israel</td>\n",
              "      <td>15834</td>\n",
              "      <td>215</td>\n",
              "      <td>8233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Chile</td>\n",
              "      <td>14885</td>\n",
              "      <td>216</td>\n",
              "      <td>8057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>9008</td>\n",
              "      <td>443</td>\n",
              "      <td>6366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>5945</td>\n",
              "      <td>100</td>\n",
              "      <td>4087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Romania</td>\n",
              "      <td>11978</td>\n",
              "      <td>693</td>\n",
              "      <td>3569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>15525</td>\n",
              "      <td>343</td>\n",
              "      <td>3425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>3769</td>\n",
              "      <td>89</td>\n",
              "      <td>3134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>7579</td>\n",
              "      <td>227</td>\n",
              "      <td>3108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Poland</td>\n",
              "      <td>12640</td>\n",
              "      <td>624</td>\n",
              "      <td>3025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>21402</td>\n",
              "      <td>157</td>\n",
              "      <td>2953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Finland</td>\n",
              "      <td>4906</td>\n",
              "      <td>206</td>\n",
              "      <td>2800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>2947</td>\n",
              "      <td>54</td>\n",
              "      <td>2665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Japan</td>\n",
              "      <td>13895</td>\n",
              "      <td>413</td>\n",
              "      <td>2368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>11929</td>\n",
              "      <td>98</td>\n",
              "      <td>2329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>3016</td>\n",
              "      <td>40</td>\n",
              "      <td>2284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>5350</td>\n",
              "      <td>103</td>\n",
              "      <td>2073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>13181</td>\n",
              "      <td>84</td>\n",
              "      <td>2072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>3848</td>\n",
              "      <td>444</td>\n",
              "      <td>1702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>1797</td>\n",
              "      <td>10</td>\n",
              "      <td>1656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>24675</td>\n",
              "      <td>883</td>\n",
              "      <td>1557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Guangdong</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1588</td>\n",
              "      <td>8</td>\n",
              "      <td>1557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>24505</td>\n",
              "      <td>973</td>\n",
              "      <td>1470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bahrain</td>\n",
              "      <td>2921</td>\n",
              "      <td>8</td>\n",
              "      <td>1455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>6207</td>\n",
              "      <td>278</td>\n",
              "      <td>1411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>9771</td>\n",
              "      <td>784</td>\n",
              "      <td>1391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kuwait</td>\n",
              "      <td>3740</td>\n",
              "      <td>24</td>\n",
              "      <td>1389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iraq</td>\n",
              "      <td>2003</td>\n",
              "      <td>92</td>\n",
              "      <td>1346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>5268</td>\n",
              "      <td>380</td>\n",
              "      <td>1335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Victoria</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1361</td>\n",
              "      <td>18</td>\n",
              "      <td>1291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Croatia</td>\n",
              "      <td>2062</td>\n",
              "      <td>67</td>\n",
              "      <td>1288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>1766</td>\n",
              "      <td>23</td>\n",
              "      <td>1267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1268</td>\n",
              "      <td>1</td>\n",
              "      <td>1263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>29-04-2020</td>\n",
              "      <td>Henan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1276</td>\n",
              "      <td>22</td>\n",
              "      <td>1254</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-f6731cdc-8041-43d6-a0ac-291719d7f5d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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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              "    .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",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-f6731cdc-8041-43d6-a0ac-291719d7f5d1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "     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-51e8990c-772a-4588-88f0-672aa7d669f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JxhKqzyQTWGA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}