Covid_19.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/javidhasanov/Covid-19----FbProphet/blob/main/Covid_19.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install prophet"
      ],
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        },
        "id": "gIO9uLXAaFIs",
        "outputId": "4d90fbc2-a27e-407f-8a50-bc656f0becf5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: prophet in /usr/local/lib/python3.10/dist-packages (1.1.4)\n",
            "Requirement already satisfied: cmdstanpy>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.23.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (3.7.1)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.5.3)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.10/dist-packages (from prophet) (2.4.0)\n",
            "Requirement already satisfied: holidays>=0.25 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.30)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (2.8.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from prophet) (4.66.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from prophet) (6.0.1)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.10/dist-packages (from convertdate>=2.1.2->prophet) (0.5.12)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.10/dist-packages (from LunarCalendar>=0.0.9->prophet) (4.1.4)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from LunarCalendar>=0.0.9->prophet) (2023.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.1.0)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (3.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->prophet) (1.16.0)\n"
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    {
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      "execution_count": 2,
      "metadata": {
        "id": "EBf79Hi9ZgZS"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "\n",
        "from prophet import Prophet\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Uploading** **data**"
      ],
      "metadata": {
        "id": "FJl0cq8XmA9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "df1 = pd.read_csv(\"CONVENIENT_global_confirmed_cases.csv\")\n",
        "df2 = pd.read_csv(\"CONVENIENT_global_deaths.csv\")"
      ],
      "metadata": {
        "id": "lsUR0VsZaa4z"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
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          "height": 351
        },
        "id": "5ELIfp-_bbsV",
        "outputId": "8dfe3511-7c52-4447-b85b-f43d8e71f381"
      },
      "execution_count": 4,
      "outputs": [
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          "metadata": {},
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      },
      "execution_count": 5,
      "outputs": [
        {
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-102f3968-8a15-4773-899c-ce55c81edf64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Creating** **new** **table**"
      ],
      "metadata": {
        "id": "uoIHkS4PmG0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world = pd.DataFrame({\"Countries\":[],\"Cases\":[]})\n",
        "world[\"Countries\"] = df1.iloc[:,1:].columns\n",
        "cases = []\n",
        "for i in world[\"Countries\"]:\n",
        "    cases.append(pd.to_numeric(df1[i][1:]).sum())\n",
        "world[\"Cases\"]=cases"
      ],
      "metadata": {
        "id": "gdndbpqEdOqh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "world"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "8DilmXD5dbuE",
        "outputId": "d243b99c-ca02-4205-cceb-36630922a08d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Countries     Cases\n",
              "0             Afghanistan  209451.0\n",
              "1                 Albania  334457.0\n",
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              "3                 Andorra   47890.0\n",
              "4                  Angola  105288.0\n",
              "..                    ...       ...\n",
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              "288              Zimbabwe  264276.0\n",
              "\n",
              "[289 rows x 2 columns]"
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              "      <td>Zimbabwe</td>\n",
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              "<p>289 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68d3ca2a-59da-46c1-a79a-1f65edf812e9')\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-95ca4e8f-ffda-4d7f-bc72-ccc5e2313e6b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95ca4e8f-ffda-4d7f-bc72-ccc5e2313e6b')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-95ca4e8f-ffda-4d7f-bc72-ccc5e2313e6b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-68d3ca2a-59da-46c1-a79a-1f65edf812e9 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-68d3ca2a-59da-46c1-a79a-1f65edf812e9');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world.info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3IcFuUDgwAU",
        "outputId": "258bfa7e-6cc6-4438-83d7-118867a6a054"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of                 Countries     Cases\n",
              "0             Afghanistan  209451.0\n",
              "1                 Albania  334457.0\n",
              "2                 Algeria  271496.0\n",
              "3                 Andorra   47890.0\n",
              "4                  Angola  105288.0\n",
              "..                    ...       ...\n",
              "284    West Bank and Gaza  703228.0\n",
              "285  Winter Olympics 2022     535.0\n",
              "286                 Yemen   11945.0\n",
              "287                Zambia  343135.0\n",
              "288              Zimbabwe  264276.0\n",
              "\n",
              "[289 rows x 2 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Eliminating unneeded symbols from rows such as (. & '(' )"
      ],
      "metadata": {
        "id": "Huqw9I3zmXFL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "country_list=list(world[\"Countries\"].values)\n",
        "dx = 0\n",
        "for i in country_list:\n",
        "    ac = 0\n",
        "    for j in i:\n",
        "        if j==\".\":\n",
        "            i = i[:ac]\n",
        "            country_list[dx]=i\n",
        "        elif j==\"(\":\n",
        "            i = i[:ac-1]\n",
        "            country_list[dx]=i\n",
        "        else:\n",
        "            ac += 1\n",
        "    dx += 1\n",
        "world[\"Countries\"]=country_list\n",
        "\n",
        "world"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zOLxuFEYieLt",
        "outputId": "672cba29-4a90-4062-cf91-a52da80b277d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Countries     Cases\n",
              "0             Afghanistan  209451.0\n",
              "1                 Albania  334457.0\n",
              "2                 Algeria  271496.0\n",
              "3                 Andorra   47890.0\n",
              "4                  Angola  105288.0\n",
              "..                    ...       ...\n",
              "284    West Bank and Gaza  703228.0\n",
              "285  Winter Olympics 2022     535.0\n",
              "286                 Yemen   11945.0\n",
              "287                Zambia  343135.0\n",
              "288              Zimbabwe  264276.0\n",
              "\n",
              "[289 rows x 2 columns]"
            ],
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              "      <td>703228.0</td>\n",
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              "      <th>285</th>\n",
              "      <td>Winter Olympics 2022</td>\n",
              "      <td>535.0</td>\n",
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              "      <td>11945.0</td>\n",
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              "    <tr>\n",
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              "      <td>Zambia</td>\n",
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              "      <th>288</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>264276.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>289 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed55f0d8-a936-44fd-a723-ffbb86bb6516')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "<style>\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-cf5142f8-27eb-4a13-b3a0-2094c4a21077 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ed55f0d8-a936-44fd-a723-ffbb86bb6516 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-ed55f0d8-a936-44fd-a723-ffbb86bb6516');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Removing repeated countries' names and summing"
      ],
      "metadata": {
        "id": "HgexnsbVm3js"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world = world.groupby(\"Countries\")[\"Cases\"].sum().reset_index()\n",
        "world.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nz4nSxJGlvur",
        "outputId": "7f4656af-6dd9-4d7a-a822-87356df74a7d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Countries     Cases\n",
              "0  Afghanistan  209451.0\n",
              "1      Albania  334457.0\n",
              "2      Algeria  271496.0\n",
              "3      Andorra   47890.0\n",
              "4       Angola  105288.0"
            ],
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              "\n",
              "\n",
              "  <div id=\"df-a7a8c466-cc63-4b18-a2d0-cc1cddc13cfa\">\n",
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              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Countries</th>\n",
              "      <th>Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>209451.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>334457.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>271496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>47890.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
              "      <td>105288.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7a8c466-cc63-4b18-a2d0-cc1cddc13cfa')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-a38292ac-557d-4283-84b3-b756363ea76a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a38292ac-557d-4283-84b3-b756363ea76a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a38292ac-557d-4283-84b3-b756363ea76a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a7a8c466-cc63-4b18-a2d0-cc1cddc13cfa 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-a7a8c466-cc63-4b18-a2d0-cc1cddc13cfa');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uploading new file"
      ],
      "metadata": {
        "id": "K-zXZEqBngnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "continent=pd.read_csv(\"continents2.csv\")\n",
        "continent.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "hFKTbhD2naAi",
        "outputId": "b5d1ecd7-faa8-4156-e122-7057df698c15"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name alpha-2 alpha-3  country-code     iso_3166-2   region  \\\n",
              "0     Afghanistan      AF     AFG             4  ISO 3166-2:AF     Asia   \n",
              "1   Åland Islands      AX     ALA           248  ISO 3166-2:AX   Europe   \n",
              "2         Albania      AL     ALB             8  ISO 3166-2:AL   Europe   \n",
              "3         Algeria      DZ     DZA            12  ISO 3166-2:DZ   Africa   \n",
              "4  American Samoa      AS     ASM            16  ISO 3166-2:AS  Oceania   \n",
              "\n",
              "        sub-region intermediate-region  region-code  sub-region-code  \\\n",
              "0    Southern Asia                 NaN        142.0             34.0   \n",
              "1  Northern Europe                 NaN        150.0            154.0   \n",
              "2  Southern Europe                 NaN        150.0             39.0   \n",
              "3  Northern Africa                 NaN          2.0             15.0   \n",
              "4        Polynesia                 NaN          9.0             61.0   \n",
              "\n",
              "   intermediate-region-code  \n",
              "0                       NaN  \n",
              "1                       NaN  \n",
              "2                       NaN  \n",
              "3                       NaN  \n",
              "4                       NaN  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>Asia</td>\n",
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              "      <td>Åland Islands</td>\n",
              "      <td>AX</td>\n",
              "      <td>ALA</td>\n",
              "      <td>248</td>\n",
              "      <td>ISO 3166-2:AX</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Albania</td>\n",
              "      <td>AL</td>\n",
              "      <td>ALB</td>\n",
              "      <td>8</td>\n",
              "      <td>ISO 3166-2:AL</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Southern Europe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>DZ</td>\n",
              "      <td>DZA</td>\n",
              "      <td>12</td>\n",
              "      <td>ISO 3166-2:DZ</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Northern Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>American Samoa</td>\n",
              "      <td>AS</td>\n",
              "      <td>ASM</td>\n",
              "      <td>16</td>\n",
              "      <td>ISO 3166-2:AS</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>Polynesia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b24e263-3201-4ed7-ac9a-bedeb06f4ce2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-eb863886-a20b-4e84-8e14-3abb2d772bae\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb863886-a20b-4e84-8e14-3abb2d772bae')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-eb863886-a20b-4e84-8e14-3abb2d772bae button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6b24e263-3201-4ed7-ac9a-bedeb06f4ce2 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-6b24e263-3201-4ed7-ac9a-bedeb06f4ce2');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "continent[\"name\"]=continent[\"name\"].str.upper()\n",
        "continent"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "sVOh-WM5nrPi",
        "outputId": "42980a9d-760a-47fc-a966-6f420e8cac52"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  name alpha-2 alpha-3  country-code     iso_3166-2   region  \\\n",
              "0          AFGHANISTAN      AF     AFG             4  ISO 3166-2:AF     Asia   \n",
              "1        ÅLAND ISLANDS      AX     ALA           248  ISO 3166-2:AX   Europe   \n",
              "2              ALBANIA      AL     ALB             8  ISO 3166-2:AL   Europe   \n",
              "3              ALGERIA      DZ     DZA            12  ISO 3166-2:DZ   Africa   \n",
              "4       AMERICAN SAMOA      AS     ASM            16  ISO 3166-2:AS  Oceania   \n",
              "..                 ...     ...     ...           ...            ...      ...   \n",
              "244  WALLIS AND FUTUNA      WF     WLF           876  ISO 3166-2:WF  Oceania   \n",
              "245     WESTERN SAHARA      EH     ESH           732  ISO 3166-2:EH   Africa   \n",
              "246              YEMEN      YE     YEM           887  ISO 3166-2:YE     Asia   \n",
              "247             ZAMBIA      ZM     ZMB           894  ISO 3166-2:ZM   Africa   \n",
              "248           ZIMBABWE      ZW     ZWE           716  ISO 3166-2:ZW   Africa   \n",
              "\n",
              "             sub-region intermediate-region  region-code  sub-region-code  \\\n",
              "0         Southern Asia                 NaN        142.0             34.0   \n",
              "1       Northern Europe                 NaN        150.0            154.0   \n",
              "2       Southern Europe                 NaN        150.0             39.0   \n",
              "3       Northern Africa                 NaN          2.0             15.0   \n",
              "4             Polynesia                 NaN          9.0             61.0   \n",
              "..                  ...                 ...          ...              ...   \n",
              "244           Polynesia                 NaN          9.0             61.0   \n",
              "245     Northern Africa                 NaN          2.0             15.0   \n",
              "246        Western Asia                 NaN        142.0            145.0   \n",
              "247  Sub-Saharan Africa      Eastern Africa          2.0            202.0   \n",
              "248  Sub-Saharan Africa      Eastern Africa          2.0            202.0   \n",
              "\n",
              "     intermediate-region-code  \n",
              "0                         NaN  \n",
              "1                         NaN  \n",
              "2                         NaN  \n",
              "3                         NaN  \n",
              "4                         NaN  \n",
              "..                        ...  \n",
              "244                       NaN  \n",
              "245                       NaN  \n",
              "246                       NaN  \n",
              "247                      14.0  \n",
              "248                      14.0  \n",
              "\n",
              "[249 rows x 11 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>alpha-2</th>\n",
              "      <th>alpha-3</th>\n",
              "      <th>country-code</th>\n",
              "      <th>iso_3166-2</th>\n",
              "      <th>region</th>\n",
              "      <th>sub-region</th>\n",
              "      <th>intermediate-region</th>\n",
              "      <th>region-code</th>\n",
              "      <th>sub-region-code</th>\n",
              "      <th>intermediate-region-code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AFGHANISTAN</td>\n",
              "      <td>AF</td>\n",
              "      <td>AFG</td>\n",
              "      <td>4</td>\n",
              "      <td>ISO 3166-2:AF</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>142.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ÅLAND ISLANDS</td>\n",
              "      <td>AX</td>\n",
              "      <td>ALA</td>\n",
              "      <td>248</td>\n",
              "      <td>ISO 3166-2:AX</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ALBANIA</td>\n",
              "      <td>AL</td>\n",
              "      <td>ALB</td>\n",
              "      <td>8</td>\n",
              "      <td>ISO 3166-2:AL</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Southern Europe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALGERIA</td>\n",
              "      <td>DZ</td>\n",
              "      <td>DZA</td>\n",
              "      <td>12</td>\n",
              "      <td>ISO 3166-2:DZ</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Northern Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AMERICAN SAMOA</td>\n",
              "      <td>AS</td>\n",
              "      <td>ASM</td>\n",
              "      <td>16</td>\n",
              "      <td>ISO 3166-2:AS</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>Polynesia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>WALLIS AND FUTUNA</td>\n",
              "      <td>WF</td>\n",
              "      <td>WLF</td>\n",
              "      <td>876</td>\n",
              "      <td>ISO 3166-2:WF</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>Polynesia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>WESTERN SAHARA</td>\n",
              "      <td>EH</td>\n",
              "      <td>ESH</td>\n",
              "      <td>732</td>\n",
              "      <td>ISO 3166-2:EH</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Northern Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>YEMEN</td>\n",
              "      <td>YE</td>\n",
              "      <td>YEM</td>\n",
              "      <td>887</td>\n",
              "      <td>ISO 3166-2:YE</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Western Asia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>142.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>ZAMBIA</td>\n",
              "      <td>ZM</td>\n",
              "      <td>ZMB</td>\n",
              "      <td>894</td>\n",
              "      <td>ISO 3166-2:ZM</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Eastern Africa</td>\n",
              "      <td>2.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>ZIMBABWE</td>\n",
              "      <td>ZW</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>716</td>\n",
              "      <td>ISO 3166-2:ZW</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>Eastern Africa</td>\n",
              "      <td>2.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>249 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f59f9e68-19d2-46e2-9276-4c35143cb13d')\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "<style>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-f59f9e68-19d2-46e2-9276-4c35143cb13d 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-f59f9e68-19d2-46e2-9276-4c35143cb13d');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating bins for cases"
      ],
      "metadata": {
        "id": "fvU-zIdxvVga"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world[\"Ranged cases\"]=pd.cut(world[\"Cases\"],[-150000,50000,200000,800000,1500000,15000000],\n",
        "                            labels=[\"UpTo50K\",\"50Kto200K\",\"200Kto800K\",\"800Kto1.5M\",\"1.5M+\"])"
      ],
      "metadata": {
        "id": "2cqAPuDErySb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adding alpha3 from continent to world table"
      ],
      "metadata": {
        "id": "jocTyOIfviCh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpha3 =[]\n",
        "for i in world[\"Countries\"].str.upper().values:\n",
        "    if i == \"BRUNEI\":\n",
        "        i=\"BRUNEI DARUSSALAM\"\n",
        "    elif  i==\"US\":\n",
        "        i=\"UNITED STATES\"\n",
        "    if len(continent[continent[\"name\"]==i][\"alpha-3\"].values)==0:\n",
        "        alpha3.append(np.nan)\n",
        "    else:\n",
        "        alpha3.append(continent[continent[\"name\"]==i][\"alpha-3\"].values[0])\n",
        "world[\"Alpha3\"]=alpha3"
      ],
      "metadata": {
        "id": "S-Y8vqlZtRYC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing Antractica for the sake of better visualization\n",
        "world = world.drop(index= 5)"
      ],
      "metadata": {
        "id": "3DnE3Bqh8DBK"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "world"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "05ZNncieFHwC",
        "outputId": "aa6f5132-18dc-4e8a-cd50-195817e51ee5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Countries     Cases Ranged cases Alpha3\n",
              "0             Afghanistan  209451.0   200Kto800K    AFG\n",
              "1                 Albania  334457.0   200Kto800K    ALB\n",
              "2                 Algeria  271496.0   200Kto800K    DZA\n",
              "3                 Andorra   47890.0      UpTo50K    AND\n",
              "4                  Angola  105288.0    50Kto200K    AGO\n",
              "..                    ...       ...          ...    ...\n",
              "195    West Bank and Gaza  703228.0   200Kto800K    NaN\n",
              "196  Winter Olympics 2022     535.0      UpTo50K    NaN\n",
              "197                 Yemen   11945.0      UpTo50K    YEM\n",
              "198                Zambia  343135.0   200Kto800K    ZMB\n",
              "199              Zimbabwe  264276.0   200Kto800K    ZWE\n",
              "\n",
              "[199 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0e03af2a-464a-4b74-a5a4-f52a352e4631\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Countries</th>\n",
              "      <th>Cases</th>\n",
              "      <th>Ranged cases</th>\n",
              "      <th>Alpha3</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>209451.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>AFG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>334457.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>ALB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>271496.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>DZA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>47890.0</td>\n",
              "      <td>UpTo50K</td>\n",
              "      <td>AND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
              "      <td>105288.0</td>\n",
              "      <td>50Kto200K</td>\n",
              "      <td>AGO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>West Bank and Gaza</td>\n",
              "      <td>703228.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>Winter Olympics 2022</td>\n",
              "      <td>535.0</td>\n",
              "      <td>UpTo50K</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>Yemen</td>\n",
              "      <td>11945.0</td>\n",
              "      <td>UpTo50K</td>\n",
              "      <td>YEM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>343135.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>ZMB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>264276.0</td>\n",
              "      <td>200Kto800K</td>\n",
              "      <td>ZWE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>199 rows × 4 columns</p>\n",
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              "              title=\"Suggest charts.\"\n",
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              "    fill: #1967D2;\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
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              "\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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e03af2a-464a-4b74-a5a4-f52a352e4631 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-0e03af2a-464a-4b74-a5a4-f52a352e4631');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.choropleth(world.dropna(),\n",
        "                   locations=\"Alpha3\",\n",
        "                   color=\"Ranged cases\",\n",
        "                    projection=\"natural earth\",\n",
        "                    hover_data= \"Cases\")\n",
        "                    #color_discrete_sequence=[\"white\",\"khaki\",\"yellow\",\"orange\",\"red\"])\n",
        "fig.update_geos(fitbounds=\"locations\", visible=True)\n",
        "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "uWTLen49vNlr",
        "outputId": "354fc2d9-43c5-4698-cf1e-b18099e1410e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Counting total cases and deaths for each day"
      ],
      "metadata": {
        "id": "g5PNsTr3mvHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count = []\n",
        "for i in range(1,len(df1)):\n",
        "    count.append(sum(pd.to_numeric(df1.iloc[i,1:].values)))"
      ],
      "metadata": {
        "id": "JnU6Ek4uFsgW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "df[\"Date\"] = df1[\"Country/Region\"][1:]\n",
        "df[\"Cases\"] = count\n",
        "df=df.set_index(\"Date\")"
      ],
      "metadata": {
        "id": "2vFSLZu3Fyqe"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "deathCount = []\n",
        "for i in range(1,len(df2)):\n",
        "    deathCount.append(sum(pd.to_numeric(df2.iloc[i,1:].values)))\n",
        "\n",
        "df[\"Deaths\"] = deathCount"
      ],
      "metadata": {
        "id": "oPqQGPOXGVJf"
      },
      "execution_count": 20,
      "outputs": []
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        "outputId": "a4be06ef-1afa-4ccd-a9d1-71efe8a73e10"
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      "execution_count": 21,
      "outputs": [
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              "            Cases  Deaths\n",
              "Date                     \n",
              "1/23/20     100.0     1.0\n",
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              "1/25/20     493.0    16.0\n",
              "1/26/20     683.0    14.0\n",
              "1/27/20     809.0    26.0\n",
              "...           ...     ...\n",
              "3/5/23    56126.0   148.0\n",
              "3/6/23    58040.0   366.0\n",
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              "3/8/23   179446.0  1445.0\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-10858373-72e2-47ea-9ddc-b6ab32f0ea83 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-af08c124-e9cf-4edb-8933-2e4ee8642a8c 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-af08c124-e9cf-4edb-8933-2e4ee8642a8c');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Cases.plot(title=\"Daily Covid19 Cases around the World\",marker=\".\",figsize=(12,6),label=\"daily cases\")\n",
        "df.Cases.rolling(window=5).mean().plot(figsize=(10,5),label=\"MA5\")\n",
        "plt.ylabel(\"Cases\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "xh_gMQVqm6Mk",
        "outputId": "e039bb1d-59de-43fd-b729-9b809867781c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Deaths.plot(title=\"Daily Covid19 Deaths around the World\",marker=\".\",figsize=(10,5),label=\"daily deaths\")\n",
        "df.Deaths.rolling(window=5).mean().plot(figsize=(10,5),label=\"MA5\")\n",
        "plt.ylabel(\"Deaths\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "QIU5tMOMnrYb",
        "outputId": "ddcab016-eea7-480d-8ca3-dd4bf0c48c86"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using Fbprophet for forecasting"
      ],
      "metadata": {
        "id": "zrM7oK8CIFdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Fbprophet(object):\n",
        "    def fit(self,data):\n",
        "\n",
        "        self.data  = data\n",
        "        self.model = Prophet(weekly_seasonality=True,daily_seasonality=False,yearly_seasonality=False)\n",
        "        self.model.fit(self.data)\n",
        "\n",
        "    def forecast(self,periods,freq):\n",
        "\n",
        "        self.future = self.model.make_future_dataframe(periods=periods,freq=freq)\n",
        "        self.df_forecast = self.model.predict(self.future)\n",
        "\n",
        "    def plot(self,xlabel=\"Years\",ylabel=\"Values\"):\n",
        "\n",
        "        self.model.plot(self.df_forecast,xlabel=xlabel,ylabel=ylabel,figsize=(9,4))\n",
        "        self.model.plot_components(self.df_forecast,figsize=(9,6))\n",
        "\n",
        "    def R2(self):\n",
        "        return r2_score(self.data.y, self.df_forecast.yhat[:len(df)])"
      ],
      "metadata": {
        "id": "B2cdYS9qB6Vc"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating new df for forecasting since fbprophet should have ds and y columns\n",
        "df_fb  = pd.DataFrame({\"ds\":[],\"y\":[]})\n",
        "df_fb[\"ds\"] = pd.to_datetime(df.index)\n",
        "df_fb[\"y\"]  = df.iloc[:,0].values"
      ],
      "metadata": {
        "id": "equxNDu1Dy7M"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9dvj5dPBESe5",
        "outputId": "33c598e9-450b-415b-ec60-5b5e8fc1328e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds         y\n",
              "0    2020-01-23     100.0\n",
              "1    2020-01-24     287.0\n",
              "2    2020-01-25     493.0\n",
              "3    2020-01-26     683.0\n",
              "4    2020-01-27     809.0\n",
              "...         ...       ...\n",
              "1137 2023-03-05   56126.0\n",
              "1138 2023-03-06   58040.0\n",
              "1139 2023-03-07  130437.0\n",
              "1140 2023-03-08  179446.0\n",
              "1141 2023-03-09  177325.0\n",
              "\n",
              "[1142 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-defea30a-b253-4e7e-9832-1c3159369971\">\n",
              "    <div 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>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>287.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>493.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>809.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1137</th>\n",
              "      <td>2023-03-05</td>\n",
              "      <td>56126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1138</th>\n",
              "      <td>2023-03-06</td>\n",
              "      <td>58040.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>2023-03-07</td>\n",
              "      <td>130437.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>2023-03-08</td>\n",
              "      <td>179446.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>2023-03-09</td>\n",
              "      <td>177325.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1142 rows × 2 columns</p>\n",
              "</div>\n",
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              "              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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-beafaa82-4d01-47ef-b3b3-4247fd7c60a9\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-beafaa82-4d01-47ef-b3b3-4247fd7c60a9')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-beafaa82-4d01-47ef-b3b3-4247fd7c60a9 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-defea30a-b253-4e7e-9832-1c3159369971 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-defea30a-b253-4e7e-9832-1c3159369971');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#feeding model with data\n",
        "model = Fbprophet()\n",
        "model.fit(df_fb)\n",
        "model.forecast(30,\"D\")\n",
        "model.R2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbG2acWxFEEg",
        "outputId": "8757205f-4da4-4b80-9b28-fcc15f0358b8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpb_fejnqv/_ku_c6lb.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpb_fejnqv/nkyciig3.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=5248', 'data', 'file=/tmp/tmpb_fejnqv/_ku_c6lb.json', 'init=/tmp/tmpb_fejnqv/nkyciig3.json', 'output', 'file=/tmp/tmpb_fejnqv/prophet_modeleo71ch5f/prophet_model-20230813172810.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "17:28:10 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "17:28:10 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Fbprophet.R2 of <__main__.Fbprophet object at 0x7f5cebdf78e0>>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.df_forecast.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "jstlWkScHPhQ",
        "outputId": "14d85e40-2b10-4df0-b2c6-a168be0aa3cb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds         trend     yhat_lower     yhat_upper   trend_lower  \\\n",
              "0 2020-01-23 -62418.666678 -385024.529142  424171.742043 -62418.666678   \n",
              "1 2020-01-24 -60651.235854 -409133.114147  404910.386521 -60651.235854   \n",
              "2 2020-01-25 -58883.805030 -589285.239626  235741.219545 -58883.805030   \n",
              "3 2020-01-26 -57116.374206 -664106.052336  156554.369480 -57116.374206   \n",
              "4 2020-01-27 -55348.943382 -463331.322418  355679.478583 -55348.943382   \n",
              "\n",
              "    trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
              "0 -62418.666678    92999.950567          92999.950567          92999.950567   \n",
              "1 -60651.235854    68527.406875          68527.406875          68527.406875   \n",
              "2 -58883.805030  -114740.939059        -114740.939059        -114740.939059   \n",
              "3 -57116.374206  -188753.059451        -188753.059451        -188753.059451   \n",
              "4 -55348.943382   -10045.915845         -10045.915845         -10045.915845   \n",
              "\n",
              "          weekly   weekly_lower   weekly_upper  multiplicative_terms  \\\n",
              "0   92999.950567   92999.950567   92999.950567                   0.0   \n",
              "1   68527.406875   68527.406875   68527.406875                   0.0   \n",
              "2 -114740.939059 -114740.939059 -114740.939059                   0.0   \n",
              "3 -188753.059451 -188753.059451 -188753.059451                   0.0   \n",
              "4  -10045.915845  -10045.915845  -10045.915845                   0.0   \n",
              "\n",
              "   multiplicative_terms_lower  multiplicative_terms_upper           yhat  \n",
              "0                         0.0                         0.0   30581.283889  \n",
              "1                         0.0                         0.0    7876.171021  \n",
              "2                         0.0                         0.0 -173624.744089  \n",
              "3                         0.0                         0.0 -245869.433657  \n",
              "4                         0.0                         0.0  -65394.859227  "
            ],
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              "\n",
              "\n",
              "  <div id=\"df-78776036-fce4-4ac4-8462-80e342ac96ce\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>-62418.666678</td>\n",
              "      <td>-385024.529142</td>\n",
              "      <td>424171.742043</td>\n",
              "      <td>-62418.666678</td>\n",
              "      <td>-62418.666678</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>92999.950567</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30581.283889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>-60651.235854</td>\n",
              "      <td>-409133.114147</td>\n",
              "      <td>404910.386521</td>\n",
              "      <td>-60651.235854</td>\n",
              "      <td>-60651.235854</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>68527.406875</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7876.171021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>-58883.805030</td>\n",
              "      <td>-589285.239626</td>\n",
              "      <td>235741.219545</td>\n",
              "      <td>-58883.805030</td>\n",
              "      <td>-58883.805030</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>-114740.939059</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-173624.744089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>-57116.374206</td>\n",
              "      <td>-664106.052336</td>\n",
              "      <td>156554.369480</td>\n",
              "      <td>-57116.374206</td>\n",
              "      <td>-57116.374206</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>-188753.059451</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-245869.433657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>-55348.943382</td>\n",
              "      <td>-463331.322418</td>\n",
              "      <td>355679.478583</td>\n",
              "      <td>-55348.943382</td>\n",
              "      <td>-55348.943382</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>-10045.915845</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-65394.859227</td>\n",
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              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-21d494c1-0d59-44f9-bfac-a56bb807055a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-78776036-fce4-4ac4-8462-80e342ac96ce 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-78776036-fce4-4ac4-8462-80e342ac96ce');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = model.df_forecast[[\"ds\",\"yhat_lower\",\"yhat_upper\",\"yhat\"]].tail(30).reset_index().set_index(\"ds\").drop(\"index\",axis=1)"
      ],
      "metadata": {
        "id": "1B-o2_vPGZKw"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "y945sCG6H2-0",
        "outputId": "bc2039c6-3714-4e6a-a301-f38b5dec06a8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               yhat_lower     yhat_upper           yhat\n",
              "ds                                                     \n",
              "2023-03-10 -144729.109018  708129.793379  269720.178927\n",
              "2023-03-11 -324420.834738  519023.735052   84367.869589\n",
              "2023-03-12 -416189.020221  429167.910740    8271.785792\n",
              "2023-03-13 -222470.490315  617671.929805  184894.965993\n",
              "2023-03-14 -138850.912023  693822.588049  250101.285268"
            ],
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              "      <th>2023-03-12</th>\n",
              "      <td>-416189.020221</td>\n",
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              "      <th>2023-03-13</th>\n",
              "      <td>-222470.490315</td>\n",
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              "      <th>2023-03-14</th>\n",
              "      <td>-138850.912023</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
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              "\n",
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              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-431b1b7d-ebdd-464f-9165-170b85c2f246 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-431b1b7d-ebdd-464f-9165-170b85c2f246');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast[\"yhat\"].plot(marker=\".\",figsize=(10,5))\n",
        "plt.fill_between(x=forecast.index, y1=forecast[\"yhat_lower\"], y2=forecast[\"yhat_upper\"],color=\"black\")\n",
        "plt.legend([\"forecast\",\"Bound\"],loc=\"upper left\")\n",
        "plt.title(\"Forecasting of Next 30 Days Cases\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "UNS1tDU6S1lF",
        "outputId": "9d2ebcf9-d850-48e1-d5db-8e791adb3598"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}