EDA_COVID_19_Vaccines.ipynb

{
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  "metadata": {
    "colab": {
      "name": "EDA-COVID-19-Vaccines.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0hEp4z69NKF"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from fbprophet import Prophet\n",
        "from fbprophet.plot import plot_plotly\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5Kp0FUr9knN",
        "outputId": "b993387d-a3bb-42a9-bd3d-5d03733ad945"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "VC1ejwJU9U9s",
        "outputId": "0579b4e6-006e-42b3-c845-bdae8f68b64d"
      },
      "source": [
        "vaccinations = pd.read_csv('/content/drive/MyDrive/dataset/country_vaccinations.csv')\n",
        "vaccinations.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>iso_code</th>\n",
              "      <th>date</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>daily_vaccinations_raw</th>\n",
              "      <th>daily_vaccinations</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>daily_vaccinations_per_million</th>\n",
              "      <th>vaccines</th>\n",
              "      <th>source_name</th>\n",
              "      <th>source_website</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2021-02-22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Government of Afghanistan</td>\n",
              "      <td>https://reliefweb.int/report/afghanistan/afgha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2021-02-23</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>35.00</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Government of Afghanistan</td>\n",
              "      <td>https://reliefweb.int/report/afghanistan/afgha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2021-02-24</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>35.00</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Government of Afghanistan</td>\n",
              "      <td>https://reliefweb.int/report/afghanistan/afgha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2021-02-25</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>35.00</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Government of Afghanistan</td>\n",
              "      <td>https://reliefweb.int/report/afghanistan/afgha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>35.00</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Government of Afghanistan</td>\n",
              "      <td>https://reliefweb.int/report/afghanistan/afgha...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       country  ...                                     source_website\n",
              "0  Afghanistan  ...  https://reliefweb.int/report/afghanistan/afgha...\n",
              "1  Afghanistan  ...  https://reliefweb.int/report/afghanistan/afgha...\n",
              "2  Afghanistan  ...  https://reliefweb.int/report/afghanistan/afgha...\n",
              "3  Afghanistan  ...  https://reliefweb.int/report/afghanistan/afgha...\n",
              "4  Afghanistan  ...  https://reliefweb.int/report/afghanistan/afgha...\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fbcl56Mz9vur"
      },
      "source": [
        "vaccinations = vaccinations[['date', \n",
        "                             'country', \n",
        "                             'iso_code', \n",
        "                             'daily_vaccinations', \n",
        "                             'people_vaccinated_per_hundred',\n",
        "                             'vaccines',\n",
        "                            ]]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kX6ZvQBz9xxT",
        "outputId": "e3e14a9d-0856-4660-c3f1-210531518420"
      },
      "source": [
        "vaccinations.select_dtypes(include=['object']).isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date        0\n",
              "country     0\n",
              "iso_code    0\n",
              "vaccines    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beYC5VwA92oz",
        "outputId": "1376d0ee-2f7f-48df-b8fb-91ca4d784354"
      },
      "source": [
        "vaccinations.loc[vaccinations.iso_code.isnull(), 'country'].value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: country, dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nckw40ty95ys",
        "outputId": "3568c5b4-684d-4f60-dd6b-92ab402dda57"
      },
      "source": [
        "vaccinations.loc[vaccinations.country == 'United Kingdom', 'iso_code'].unique()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['GBR'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89UbUK0A979T"
      },
      "source": [
        "vaccinations.loc[vaccinations.country.isin(['England', 'Scotland', 'Wales', 'Northern Ireland']), 'iso_code'] = 'GBR'"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "pSrbrldL9-LL",
        "outputId": "51d0968d-93e4-4830-db7a-a8ce58dc2f00"
      },
      "source": [
        "continents = pd.read_csv('/content/drive/MyDrive/dataset/continents.csv')\n",
        "continents.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>142.00</td>\n",
              "      <td>34.00</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>150.00</td>\n",
              "      <td>154.00</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>150.00</td>\n",
              "      <td>39.00</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>2.00</td>\n",
              "      <td>15.00</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>9.00</td>\n",
              "      <td>61.00</td>\n",
              "      <td>nan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             name alpha-2  ... sub-region-code  intermediate-region-code\n",
              "0     Afghanistan      AF  ...           34.00                       nan\n",
              "1   Åland Islands      AX  ...          154.00                       nan\n",
              "2         Albania      AL  ...           39.00                       nan\n",
              "3         Algeria      DZ  ...           15.00                       nan\n",
              "4  American Samoa      AS  ...           61.00                       nan\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "uJGVhMSQ-c47",
        "outputId": "046b8381-ed6b-4985-c466-00bd894a7038"
      },
      "source": [
        "data = vaccinations.merge(continents[['alpha-3', 'region', 'sub-region']],\n",
        "                          how='left',\n",
        "                          left_on = 'iso_code',\n",
        "                          right_on = 'alpha-3',\n",
        "                         ).drop(columns=['alpha-3'])\n",
        "data.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>country</th>\n",
              "      <th>iso_code</th>\n",
              "      <th>daily_vaccinations</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>vaccines</th>\n",
              "      <th>region</th>\n",
              "      <th>sub-region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-02-22</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-02-23</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-02-24</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-02-25</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-02-26</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1367.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>Oxford/AstraZeneca</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Southern Asia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      country iso_code  ...            vaccines  region     sub-region\n",
              "0  2021-02-22  Afghanistan      AFG  ...  Oxford/AstraZeneca    Asia  Southern Asia\n",
              "1  2021-02-23  Afghanistan      AFG  ...  Oxford/AstraZeneca    Asia  Southern Asia\n",
              "2  2021-02-24  Afghanistan      AFG  ...  Oxford/AstraZeneca    Asia  Southern Asia\n",
              "3  2021-02-25  Afghanistan      AFG  ...  Oxford/AstraZeneca    Asia  Southern Asia\n",
              "4  2021-02-26  Afghanistan      AFG  ...  Oxford/AstraZeneca    Asia  Southern Asia\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5y09cKd-gWq"
      },
      "source": [
        "data.columns = ['date', 'country', 'iso_code', 'daily_vaccinations', 'people_vaccinated_per_hundred', 'vaccines', \n",
        "                'continent', 'region']"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRnqV9Zp-ici",
        "outputId": "1016e84f-a460-41d5-f325-2d4724777470"
      },
      "source": [
        "data.continent.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Europe      6063\n",
              "Asia        3822\n",
              "Americas    3508\n",
              "Africa      1869\n",
              "Oceania      277\n",
              "Name: continent, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhMuka2H-lzC"
      },
      "source": [
        "data.loc[data.region == 'Northern America', 'continent'] = 'North America'\n",
        "data.loc[data.region == 'Latin America and the Caribbean', 'continent'] = 'South America'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mb2ftk82-oe6",
        "outputId": "8432150b-cf71-43cb-e514-68acf9e9417b"
      },
      "source": [
        "data[data.continent == 'Oceania'].country.value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Australia           81\n",
              "New Zealand         76\n",
              "Solomon Islands     36\n",
              "Fiji                31\n",
              "Papua New Guinea    28\n",
              "Tonga               13\n",
              "Nauru                8\n",
              "Samoa                4\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5iBn6hi-r9K",
        "outputId": "f5f78730-e593-42b6-cd62-c5546450f965"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                                0\n",
              "country                             0\n",
              "iso_code                            0\n",
              "daily_vaccinations                201\n",
              "people_vaccinated_per_hundred    6912\n",
              "vaccines                            0\n",
              "continent                         127\n",
              "region                            127\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEqrS7gl-uZD",
        "outputId": "b62bec96-f3b6-4985-999d-638b29684d3a"
      },
      "source": [
        "data.loc[data.continent.isnull(), 'iso_code'].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OWID_CYN    100\n",
              "OWID_KOS     27\n",
              "Name: iso_code, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "id": "54HfxNnd-w76",
        "outputId": "9edf41c5-96de-49dc-827a-440d2441cc77"
      },
      "source": [
        "data[data.country.isin(['Cyprus', 'Turkey'])][['continent', 'region']].drop_duplicates()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>continent</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3387</th>\n",
              "      <td>Asia</td>\n",
              "      <td>Western Asia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     continent        region\n",
              "3387      Asia  Western Asia"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tj-SeBw7-zCL"
      },
      "source": [
        "data.loc[data.country == 'Northern Cyprus', 'continent'] = 'Asia'\n",
        "data.loc[data.country == 'Northern Cyprus', 'region'] = 'Western Asia'"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gicdGPTs-1Pj",
        "outputId": "0c0c5269-a1f5-4e53-cd44-9de4e908bd0d"
      },
      "source": [
        "data.loc[data.daily_vaccinations.isnull(), 'country'].value_counts()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Guinea          3\n",
              "Sierra Leone    2\n",
              "Oman            2\n",
              "Guyana          2\n",
              "Russia          1\n",
              "               ..\n",
              "Czechia         1\n",
              "Malta           1\n",
              "Saint Lucia     1\n",
              "Jersey          1\n",
              "Bangladesh      1\n",
              "Name: country, Length: 196, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFhj7LKV-4di",
        "outputId": "fdfd4ebe-29a6-4c0c-e220-250df92f26e0"
      },
      "source": [
        "data.loc[data.daily_vaccinations.isnull(), 'date'].value_counts().head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-12-27    13\n",
              "2020-12-28     8\n",
              "2021-03-01     6\n",
              "2021-02-22     6\n",
              "2021-01-03     5\n",
              "Name: date, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KLxb7uw-7M6",
        "outputId": "ac586035-2a72-4d26-ab22-b89d3558e892"
      },
      "source": [
        "data.loc[data.people_vaccinated_per_hundred.isnull()].shape[0] / data.shape[0]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4412102642665645"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "eJSBufnq-_IL",
        "outputId": "ccab0e6c-3756-415e-9556-f481af802855"
      },
      "source": [
        "(\n",
        "    data[data.people_vaccinated_per_hundred.isnull()].region.value_counts() / data.region.value_counts()\n",
        ").mul(100).plot.barh(color=\"teal\", figsize=(15,5))\n",
        "\n",
        "plt.title('Missing people_vaccinated_per_hundred')\n",
        "plt.xlabel('percentage (%)')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwZiGGSc_Dfa"
      },
      "source": [
        "data.sort_values(by='date', inplace=True)\n",
        "all_countries = list(data.country.unique())\n",
        "\n",
        "for country in all_countries:\n",
        "    data.loc[data.country == country, 'people_vaccinated_per_hundred'] = \\\n",
        "    data.loc[data.country == country, 'people_vaccinated_per_hundred'].fillna(method='bfill').fillna(method='ffill')"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "n0tfTmFM_GQM",
        "outputId": "5b8d0cda-ce5a-47fb-b5bd-aa5fafa150cd"
      },
      "source": [
        "(\n",
        "    data[data.people_vaccinated_per_hundred.isnull()].region.value_counts() / data.region.value_counts()\n",
        ").mul(100).plot.barh(color=\"teal\", figsize=(15,5))\n",
        "\n",
        "plt.title('Missing people_vaccinated_per_hundred')\n",
        "plt.xlabel('percentage (%)')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_mMbbR6_bAL",
        "outputId": "4130d8f7-b6de-4e7a-aad6-79b77ee8f78b"
      },
      "source": [
        "data.vaccines.value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Oxford/AstraZeneca                                                                    2607\n",
              "Moderna, Oxford/AstraZeneca, Pfizer/BioNTech                                          2389\n",
              "Johnson&Johnson, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech                         1532\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech                                                   1418\n",
              "Pfizer/BioNTech                                                                       1227\n",
              "Moderna, Pfizer/BioNTech                                                               604\n",
              "Oxford/AstraZeneca, Sinopharm/Beijing                                                  587\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac                                           499\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V                      487\n",
              "Oxford/AstraZeneca, Sinovac                                                            472\n",
              "Sputnik V                                                                              438\n",
              "Pfizer/BioNTech, Sinovac                                                               391\n",
              "Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V                                       311\n",
              "Sinopharm/Beijing                                                                      310\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing                                 242\n",
              "Pfizer/BioNTech, Sinopharm/Beijing                                                     201\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V                                193\n",
              "Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac                                         167\n",
              "Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac                                            143\n",
              "EpiVacCorona, Sputnik V                                                                143\n",
              "Johnson&Johnson, Moderna, Pfizer/BioNTech                                              138\n",
              "CanSino, Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V                       133\n",
              "Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V             129\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V     122\n",
              "Covaxin, Oxford/AstraZeneca                                                            112\n",
              "Moderna, Oxford/AstraZeneca                                                            111\n",
              "Oxford/AstraZeneca, Sputnik V                                                          111\n",
              "CanSino, Sinopharm/Beijing, Sinovac, Sputnik V                                          93\n",
              "Oxford/AstraZeneca, Pfizer/BioNTech, Sputnik V                                          79\n",
              "Johnson&Johnson                                                                         79\n",
              "Sinopharm/Beijing, Sputnik V                                                            74\n",
              "Pfizer/BioNTech, Sputnik V                                                              68\n",
              "Pfizer/BioNTech, Sinovac, Sputnik V                                                     55\n",
              "Oxford/AstraZeneca, Sinovac, Sputnik V                                                   1\n",
              "Name: vaccines, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSUQf4Q2_fOz",
        "outputId": "4d32e334-7f48-4f9f-d63a-bfd5754dd3be"
      },
      "source": [
        "df = data[['iso_code', 'vaccines']].drop_duplicates()\n",
        "df.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUygWtNs_pZC",
        "outputId": "2889edbb-b280-4c21-b04c-8fda714b58c1"
      },
      "source": [
        "data.iso_code.unique().shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eItr0r0_tz7",
        "outputId": "442e8695-21f8-4e80-a921-c8f1d0c46416"
      },
      "source": [
        "map_plot_df = pd.concat([pd.Series(row['iso_code'], row['vaccines'].split(','))              \n",
        "                         for _, row in df.iterrows()]).reset_index()\n",
        "\n",
        "#rename columns\n",
        "map_plot_df.columns = ['vaccine', 'iso_code']\n",
        "\n",
        "#strip white spaces\n",
        "map_plot_df['vaccine'] = map_plot_df['vaccine'].str.strip(' ')\n",
        "\n",
        "map_plot_df.vaccine.value_counts()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Oxford/AstraZeneca    142\n",
              "Pfizer/BioNTech        88\n",
              "Moderna                38\n",
              "Sinopharm/Beijing      38\n",
              "Sputnik V              32\n",
              "Sinovac                24\n",
              "Johnson&Johnson        14\n",
              "CanSino                 2\n",
              "Sinopharm/Wuhan         2\n",
              "EpiVacCorona            1\n",
              "Covaxin                 1\n",
              "Name: vaccine, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "S6AonfmHAEfy",
        "outputId": "40aea59c-c4b1-4a3b-cabf-a7018a3a958e"
      },
      "source": [
        "data.groupby(['country']).agg({'daily_vaccinations':'mean'}).sort_values(by='daily_vaccinations', ascending=False)[:10].plot.barh(color=\"teal\", figsize=(15,5))\n",
        "plt.title('Average daily vaccinations by country - Top 10')\n",
        "plt.legend('')\n",
        "plt.xlabel('average daily vaccinations')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ua3ZZUm3AJ4b"
      },
      "source": [
        "def plot_daily_vaccination_data(data, region_column, regions_list, vaccination_metric, plot_title, min_date = '2020-12-13', max_date='2021-02-13'):\n",
        "    '''\n",
        "    Plots a vaccination metric using Plotly library\n",
        "    \n",
        "    INPUT \n",
        "        data : Pandas dataframe\n",
        "            a dataframe holding the data to be plotted \n",
        "            must contain a date column (unique dates), a region column and a vaccination related metric\n",
        "        region_column : string\n",
        "            name of the region column \n",
        "            (continent/region/country)\n",
        "        regions_list : list\n",
        "            list of strings with regions to be plotted\n",
        "            must contain values of region_column\n",
        "        vaccination_metric : string\n",
        "            name of the vaccination metric column \n",
        "            (daily_vaccinations/people_fully_vaccinated_per_hundred)\n",
        "        plot_title : string \n",
        "            the title of your plot\n",
        "        min_date : string\n",
        "            lower date limit for the plot\n",
        "        max_date : string\n",
        "            upper date limit for the plot\n",
        "    '''\n",
        "    df = data[(data[region_column].isin(regions_list)) & (data.date <= max_date) & (data.date >= min_date)]\n",
        "    fig = px.line(df, \n",
        "                  x = 'date', \n",
        "                  y = vaccination_metric,\n",
        "                  color = region_column,\n",
        "                  title = plot_title\n",
        "                 )\n",
        "    fig.show()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXujOLTvAL_j"
      },
      "source": [
        "continents_daily_df = data.groupby(['date', 'continent']).agg({'daily_vaccinations':'sum',\n",
        "                                                               'people_vaccinated_per_hundred':'mean',\n",
        "                                                              }).reset_index()\n",
        "regions_daily_df = data.groupby(['date', 'region']).agg({'daily_vaccinations':'sum',\n",
        "                                                         'people_vaccinated_per_hundred':'mean',\n",
        "                                                        }).reset_index()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAviG33PAOF7"
      },
      "source": [
        "all_continents = list(continents_daily_df.continent.unique())\n",
        "all_regions = list(regions_daily_df.region.unique())"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "mvRUoJ5qAQpy",
        "outputId": "063bd121-bb02-45f9-9f24-ed395ff6616c"
      },
      "source": [
        "plot_daily_vaccination_data(data = continents_daily_df,  \n",
        "                            region_column = 'continent',\n",
        "                            regions_list = all_continents,\n",
        "                            vaccination_metric = 'daily_vaccinations',\n",
        "                            plot_title = 'Daily COVID-19 Vaccinations - Summary per Continent in the first three months',\n",
        "                            min_date = '2020-12-13',\n",
        "                            max_date = '2021-02-13'\n",
        "                           )"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        },
        "id": "cKqPaHubAdPC",
        "outputId": "7bd7a688-71ee-4687-ef76-d11b7916fd19"
      },
      "source": [
        "print('Maximum vaccinations in a day in a country - summary per continent: \\n====================================================================')\n",
        "for continent in all_continents:\n",
        "    df = data[data.continent == continent].sort_values(by=['daily_vaccinations'], ascending=False).iloc[0]\n",
        "    print(f'\\t{df.continent} -> {round(int(df.daily_vaccinations)/1000000,2)} million vaccinations in {df.country} on {df.date}')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum vaccinations in a day in a country - summary per continent: \n",
            "====================================================================\n",
            "\tNorth America -> 3.38 million vaccinations in United States on 2021-04-13\n",
            "\tAsia -> 7.21 million vaccinations in China on 2021-05-02\n",
            "\tEurope -> 0.67 million vaccinations in Germany on 2021-05-04\n",
            "\tSouth America -> 1.14 million vaccinations in Brazil on 2021-04-13\n",
            "\tAfrica -> 0.21 million vaccinations in Morocco on 2021-03-16\n",
            "\tOceania -> 0.06 million vaccinations in Australia on 2021-04-24\n"
          ],
          "name": "stdout"
        }
      ]
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        "outputId": "e656473f-e7b2-4c25-ff65-b52a53536761"
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      "source": [
        "plot_daily_vaccination_data(data = regions_daily_df,  \n",
        "                            region_column = 'region',\n",
        "                            regions_list = all_regions,\n",
        "                            vaccination_metric = 'daily_vaccinations',\n",
        "                            plot_title = 'Daily COVID-19 Vaccinations - Summary per Region',\n",
        "                            min_date = '2020-12-13',\n",
        "                            max_date = '2021-02-13'\n",
        "                           )"
      ],
      "execution_count": 35,
      "outputs": [
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        },
        "id": "b61xn8zxAlAb",
        "outputId": "85de0d83-0f3a-4cbf-d37f-a3b07ae4ab52"
      },
      "source": [
        "print('Maximum vaccinations in a country in a day - summary per region: \\n================================================================')\n",
        "for region in all_regions:\n",
        "    df = data[data.region == region].sort_values(by=['daily_vaccinations'], ascending=False).iloc[0]\n",
        "    print(f'\\t{df.region} -> {round(int(df.daily_vaccinations)/1000000,4)} million vaccinations in {df.country} on {df.date}')\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum vaccinations in a country in a day - summary per region: \n",
            "================================================================\n",
            "\tNorthern America -> 3.3844 million vaccinations in United States on 2021-04-13\n",
            "\tEastern Asia -> 7.2053 million vaccinations in China on 2021-05-02\n",
            "\tEastern Europe -> 0.3452 million vaccinations in Russia on 2021-03-29\n",
            "\tWestern Asia -> 0.4356 million vaccinations in Turkey on 2021-02-18\n",
            "\tWestern Europe -> 0.6745 million vaccinations in Germany on 2021-05-04\n",
            "\tLatin America and the Caribbean -> 1.1358 million vaccinations in Brazil on 2021-04-13\n",
            "\tNorthern Europe -> 0.6023 million vaccinations in United Kingdom on 2021-03-21\n",
            "\tSouthern Europe -> 0.4513 million vaccinations in Italy on 2021-05-05\n",
            "\tSub-Saharan Africa -> 0.0689 million vaccinations in Nigeria on 2021-04-01\n",
            "\tSouth-eastern Asia -> 0.4493 million vaccinations in Indonesia on 2021-03-23\n",
            "\tSouthern Asia -> 3.6594 million vaccinations in India on 2021-04-10\n",
            "\tNorthern Africa -> 0.209 million vaccinations in Morocco on 2021-03-16\n",
            "\tCentral Asia -> 0.0952 million vaccinations in Kazakhstan on 2021-04-28\n",
            "\tAustralia and New Zealand -> 0.0628 million vaccinations in Australia on 2021-04-24\n",
            "\tMelanesia -> 0.0049 million vaccinations in Fiji on 2021-04-16\n",
            "\tMicronesia -> 0.0002 million vaccinations in Nauru on 2021-04-09\n",
            "\tPolynesia -> 0.0005 million vaccinations in Samoa on 2021-05-01\n"
          ],
          "name": "stdout"
        }
      ]
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      "cell_type": "code",
      "metadata": {
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      "source": [
        "Europe = ['Northern Europe', 'Eastern Europe', 'Western Europe', 'Southern Europe']\n",
        "Asia = ['Eastern Asia', 'Western Asia', 'South-eastern Asia', 'Southern Asia']\n",
        "Africa = ['Sub-Saharan Africa', 'Northern Africa']\n",
        "Americas = ['Northern America', 'Latin America and the Caribbean']"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "czifSqaiAq3C",
        "outputId": "1987b81c-e18d-4e81-e12c-a1235eab6868"
      },
      "source": [
        "plot_daily_vaccination_data(data = regions_daily_df,  \n",
        "                            region_column = 'region',\n",
        "                            regions_list = Europe,\n",
        "                            vaccination_metric = 'daily_vaccinations',\n",
        "                            plot_title = 'Daily COVID-19 Vaccinations - Summary per Region in Europe'\n",
        "                           )"
      ],
      "execution_count": 38,
      "outputs": [
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Daily COVID-19 Vaccinations - Summary per Region in Europe\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"daily_vaccinations\"}}},\n",
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              "            Plotly.purge(gd);\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "RYWVQnn9Az-E",
        "outputId": "f893aae8-bfd3-45fd-a3b8-d3f8bd84058a"
      },
      "source": [
        "uk_countries = ['England', 'Scotland', 'Wales', 'Northern Ireland', 'Jersey', 'Isle of Man', 'Bermuda', 'Gibraltar']\n",
        "data[~data.country.isin(uk_countries)].groupby(['country']).agg({'people_vaccinated_per_hundred':'max'}).sort_values(by='people_vaccinated_per_hundred', ascending=False)[:10].plot.barh(color=\"teal\", figsize=(15,5))\n",
        "plt.title('people_vaccinated_per_hundred- Top 10')\n",
        "plt.legend('')\n",
        "plt.xlabel('percentage of people vaccinated (%)')\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "kK5NNTGpA5ta",
        "outputId": "bed46484-de37-4561-d7b1-5e5aab6e56ef"
      },
      "source": [
        "plot_daily_vaccination_data(data = regions_daily_df,  \n",
        "                            region_column = 'region',\n",
        "                            regions_list = Europe + ['Northern America'],\n",
        "                            vaccination_metric = 'people_vaccinated_per_hundred',\n",
        "                            plot_title = 'people_vaccinated_per_hundred evolution in the first three months',\n",
        "                           )"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                \n",
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              "                    Plotly.newPlot(\n",
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