covid-19-world-vaccination-progress-visualization.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# <p style=\"text-align: center;\">COVID-19 World Vaccination Progress </p>"},{"metadata":{},"cell_type":"markdown","source":"### Problem Definition"},{"metadata":{},"cell_type":"markdown","source":"Covid-19 pandemic has been a challenge with over hundred million cases and over two million deaths around the globe. Wearing a mask and social distancing have become a new culture to limit the transmission of this deadly virus. While we hope to return back to our normal life from few years back, vaccination is the key, not only in one county or region but through out the entire world. Therefore it is necessary to track the vaccination progress of every country to save lives, limit spread, and move back to our old normal. In this project, I will track the vaccination progress around the globe based on the vaccine they use, total vaccinations, and daily vaccinations rate."},{"metadata":{},"cell_type":"markdown","source":"### Understanding Dataset"},{"metadata":{},"cell_type":"markdown","source":"The vaccination progress dataset is obtained from Kaggle. The data contains the following information:\n\n1. Country- this is the country for which the vaccination information is provided;\n2. Country ISO Code - ISO code for the country;\n3. Date - date for the data entry; for some of the dates we have only the daily vaccinations, for others, only the (cumulative) total;\n4. Total number of vaccinations - this is the absolute number of total immunizations in the country;\n5. Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people;\n6. Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme;\n7. Daily vaccinations (raw) - for a certain data entry, the number of vaccination for that date/country;\n8. Daily vaccinations - for a certain data entry, the number of vaccination for that date/country;\n9. Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country;\n10. Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country;\n11. Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country;\n12. Number of vaccinations per day - number of daily vaccination for that day and country;\n13. Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country;\n14. Vaccines used in the country - total number of vaccines used in the country (up to date);\n15. Source name - source of the information (national authority, international organization, local organization etc.);\n16. Source website - website of the source of information\n\n<u><b>Acknowledgement:</u></b><br>\n    The data is made available in Kaggle by [Gabriel Preda](https://www.kaggle.com/gpreda). The dataset information provided above is as mentioned by the author."},{"metadata":{},"cell_type":"markdown","source":"### Libraries\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"#dataframe\nimport pandas as pd\nimport numpy as np\n\n#visualization\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport cufflinks as cf\n\n%matplotlib inline\n\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\ninit_notebook_mode(connected=True)\ncf.go_offline()\n\n\n# suppressing warnings\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":293,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Importing Dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"../input/covid-world-vaccination-progress/country_vaccinations.csv\")\ndf.head()","execution_count":294,"outputs":[{"output_type":"execute_result","execution_count":294,"data":{"text/plain":"   country iso_code        date  total_vaccinations  people_vaccinated  \\\n0  Albania      ALB  2021-01-10                 0.0                0.0   \n1  Albania      ALB  2021-01-11                 NaN                NaN   \n2  Albania      ALB  2021-01-12               128.0              128.0   \n3  Albania      ALB  2021-01-13               188.0              188.0   \n4  Albania      ALB  2021-01-14               266.0              266.0   \n\n   people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n0                      NaN                     NaN                 NaN   \n1                      NaN                     NaN                64.0   \n2                      NaN                     NaN                64.0   \n3                      NaN                    60.0                63.0   \n4                      NaN                    78.0                66.0   \n\n   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n0                            0.00                           0.00   \n1                             NaN                            NaN   \n2                            0.00                           0.00   \n3                            0.01                           0.01   \n4                            0.01                           0.01   \n\n   people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n0                                  NaN                             NaN   \n1                                  NaN                            22.0   \n2                                  NaN                            22.0   \n3                                  NaN                            22.0   \n4                                  NaN                            23.0   \n\n          vaccines         source_name  \\\n0  Pfizer/BioNTech  Ministry of Health   \n1  Pfizer/BioNTech  Ministry of Health   \n2  Pfizer/BioNTech  Ministry of Health   \n3  Pfizer/BioNTech  Ministry of Health   \n4  Pfizer/BioNTech  Ministry of Health   \n\n                                      source_website  \n0  https://shendetesia.gov.al/covid19-ministria-e...  \n1  https://shendetesia.gov.al/covid19-ministria-e...  \n2  https://shendetesia.gov.al/covid19-ministria-e...  \n3  https://shendetesia.gov.al/covid19-ministria-e...  \n4  https://shendetesia.gov.al/covid19-ministria-e...  ","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>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>Albania</td>\n      <td>ALB</td>\n      <td>2021-01-10</td>\n      <td>0.0</td>\n      <td>0.0</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>Pfizer/BioNTech</td>\n      <td>Ministry of Health</td>\n      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>ALB</td>\n      <td>2021-01-11</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>64.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>22.0</td>\n      <td>Pfizer/BioNTech</td>\n      <td>Ministry of Health</td>\n      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Albania</td>\n      <td>ALB</td>\n      <td>2021-01-12</td>\n      <td>128.0</td>\n      <td>128.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>64.0</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>22.0</td>\n      <td>Pfizer/BioNTech</td>\n      <td>Ministry of Health</td>\n      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Albania</td>\n      <td>ALB</td>\n      <td>2021-01-13</td>\n      <td>188.0</td>\n      <td>188.0</td>\n      <td>NaN</td>\n      <td>60.0</td>\n      <td>63.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>NaN</td>\n      <td>22.0</td>\n      <td>Pfizer/BioNTech</td>\n      <td>Ministry of Health</td>\n      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Albania</td>\n      <td>ALB</td>\n      <td>2021-01-14</td>\n      <td>266.0</td>\n      <td>266.0</td>\n      <td>NaN</td>\n      <td>78.0</td>\n      <td>66.0</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>NaN</td>\n      <td>23.0</td>\n      <td>Pfizer/BioNTech</td>\n      <td>Ministry of Health</td>\n      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Exploratory Data Analysis"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Dataframe column characteristics\ndf.info()","execution_count":295,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3946 entries, 0 to 3945\nData columns (total 15 columns):\n #   Column                               Non-Null Count  Dtype  \n---  ------                               --------------  -----  \n 0   country                              3946 non-null   object \n 1   iso_code                             3662 non-null   object \n 2   date                                 3946 non-null   object \n 3   total_vaccinations                   2577 non-null   float64\n 4   people_vaccinated                    2176 non-null   float64\n 5   people_fully_vaccinated              1431 non-null   float64\n 6   daily_vaccinations_raw               2179 non-null   float64\n 7   daily_vaccinations                   3800 non-null   float64\n 8   total_vaccinations_per_hundred       2577 non-null   float64\n 9   people_vaccinated_per_hundred        2176 non-null   float64\n 10  people_fully_vaccinated_per_hundred  1431 non-null   float64\n 11  daily_vaccinations_per_million       3800 non-null   float64\n 12  vaccines                             3946 non-null   object \n 13  source_name                          3946 non-null   object \n 14  source_website                       3946 non-null   object \ndtypes: float64(9), object(6)\nmemory usage: 462.5+ KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#rows and columns in the dataframe\ndf.shape\n","execution_count":296,"outputs":[{"output_type":"execute_result","execution_count":296,"data":{"text/plain":"(3946, 15)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#number of empty cells in the data frame\ndf.isnull().sum()","execution_count":297,"outputs":[{"output_type":"execute_result","execution_count":297,"data":{"text/plain":"country                                   0\niso_code                                284\ndate                                      0\ntotal_vaccinations                     1369\npeople_vaccinated                      1770\npeople_fully_vaccinated                2515\ndaily_vaccinations_raw                 1767\ndaily_vaccinations                      146\ntotal_vaccinations_per_hundred         1369\npeople_vaccinated_per_hundred          1770\npeople_fully_vaccinated_per_hundred    2515\ndaily_vaccinations_per_million          146\nvaccines                                  0\nsource_name                               0\nsource_website                            0\ndtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#list of different names of countries in the dataset\ndf[\"country\"].unique()","execution_count":298,"outputs":[{"output_type":"execute_result","execution_count":298,"data":{"text/plain":"array(['Albania', 'Algeria', 'Andorra', 'Anguilla', 'Argentina',\n       'Australia', 'Austria', 'Azerbaijan', 'Bahrain', 'Bangladesh',\n       'Barbados', 'Belarus', 'Belgium', 'Bermuda', 'Bolivia', 'Brazil',\n       'Bulgaria', 'Cambodia', 'Canada', 'Cayman Islands', 'Chile',\n       'China', 'Colombia', 'Costa Rica', 'Croatia', 'Cyprus', 'Czechia',\n       'Denmark', 'Dominican Republic', 'Ecuador', 'Egypt', 'England',\n       'Estonia', 'Faeroe Islands', 'Falkland Islands', 'Finland',\n       'France', 'Germany', 'Gibraltar', 'Greece', 'Greenland',\n       'Guernsey', 'Guyana', 'Hungary', 'Iceland', 'India', 'Indonesia',\n       'Iran', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Japan',\n       'Jersey', 'Kuwait', 'Latvia', 'Lebanon', 'Liechtenstein',\n       'Lithuania', 'Luxembourg', 'Macao', 'Maldives', 'Malta',\n       'Mauritius', 'Mexico', 'Monaco', 'Morocco', 'Myanmar', 'Nepal',\n       'Netherlands', 'New Zealand', 'Northern Cyprus',\n       'Northern Ireland', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Peru',\n       'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Saint Helena',\n       'Saudi Arabia', 'Scotland', 'Serbia', 'Seychelles', 'Singapore',\n       'Slovakia', 'Slovenia', 'South Africa', 'Spain', 'Sri Lanka',\n       'Sweden', 'Switzerland', 'Turkey', 'Turks and Caicos Islands',\n       'United Arab Emirates', 'United Kingdom', 'United States', 'Wales'],\n      dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#list of vaccines names\ndf['vaccines'].unique()","execution_count":299,"outputs":[{"output_type":"execute_result","execution_count":299,"data":{"text/plain":"array(['Pfizer/BioNTech', 'Sputnik V', 'Oxford/AstraZeneca',\n       'Moderna, Oxford/AstraZeneca, Pfizer/BioNTech',\n       'Oxford/AstraZeneca, Sputnik V',\n       'Pfizer/BioNTech, Sinopharm/Beijing',\n       'Oxford/AstraZeneca, Sinovac', 'Sinopharm/Beijing',\n       'Moderna, Pfizer/BioNTech', 'Pfizer/BioNTech, Sinovac',\n       'Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac',\n       'Oxford/AstraZeneca, Pfizer/BioNTech',\n       'Covaxin, Oxford/AstraZeneca', 'Sinovac',\n       'Oxford/AstraZeneca, Sinopharm/Beijing',\n       'Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V',\n       'Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V', 'Johnson&Johnson',\n       'Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sinopharm/Wuhan, Sputnik V'],\n      dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#total number of countries\nlen(df['country'].unique())\n","execution_count":300,"outputs":[{"output_type":"execute_result","execution_count":300,"data":{"text/plain":"102"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# total number of vaccines/vaccine combinations\nlen(df['vaccines'].unique())","execution_count":301,"outputs":[{"output_type":"execute_result","execution_count":301,"data":{"text/plain":"19"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Data Visualization"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Daily vaccination rate in the US\npx.line(df[df['country']=='United States'],x='date',y='daily_vaccinations').update_layout(title = 'Daily Vaccination Rate in the US',xaxis_title='Date',yaxis_title='Daily Vaccination' 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gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Number of counties using different vaccine types/combinations\ndf.groupby(['country'],sort=False, as_index=False)['vaccines'].first()\nplt.figure(figsize=(15,5))\nplt.xticks(rotation=90)\nsns.set(style = 'whitegrid')\nsns.countplot(x=\"vaccines\", data=df.groupby(['country'],sort=False, as_index=False)['vaccines'].first())\nplt.title(\"Countries using Different Vaccines Types\\n\",size =20)\nplt.ylabel(\"No of Countries\",size=15)\nplt.xlabel(\"\\nVaccine Types\\n\\n\",size=15)\n","execution_count":306,"outputs":[{"output_type":"execute_result","execution_count":306,"data":{"text/plain":"Text(0.5, 0, '\\nVaccine Types\\n\\n')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x360 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Finding Maximum number of people vaccinated in each country\nvaccine = df.groupby([\"country\",\"vaccines\"])['total_vaccinations','total_vaccinations_per_hundred',\n                                       'daily_vaccinations','daily_vaccinations_per_million'].max().reset_index()\nvaccine","execution_count":307,"outputs":[{"output_type":"execute_result","execution_count":307,"data":{"text/plain":"                      country  \\\n0                     Albania   \n1                     Algeria   \n2                     Andorra   \n3                    Anguilla   \n4                   Argentina   \n..                        ...   \n97   Turks and Caicos Islands   \n98       United Arab Emirates   \n99             United Kingdom   \n100             United States   \n101                     Wales   \n\n                                              vaccines  total_vaccinations  \\\n0                                      Pfizer/BioNTech              4177.0   \n1                                            Sputnik V             75000.0   \n2                                      Pfizer/BioNTech              2141.0   \n3                                   Oxford/AstraZeneca              2762.0   \n4                                            Sputnik V            722234.0   \n..                                                 ...                 ...   \n97                                     Pfizer/BioNTech              6433.0   \n98   Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...           5557793.0   \n99                 Oxford/AstraZeneca, Pfizer/BioNTech          18348165.0   \n100                           Moderna, Pfizer/BioNTech          64177474.0   \n101                Oxford/AstraZeneca, Pfizer/BioNTech            905000.0   \n\n     total_vaccinations_per_hundred  daily_vaccinations  \\\n0                              0.15               405.0   \n1                              0.17              3748.0   \n2                              2.77               100.0   \n3                             18.41               331.0   \n4                              1.60             19820.0   \n..                              ...                 ...   \n97                            16.62               222.0   \n98                            56.19            160020.0   \n99                            27.03            445204.0   \n100                           19.19           1716311.0   \n101                           28.70             28015.0   \n\n     daily_vaccinations_per_million  \n0                             141.0  \n1                              85.0  \n2                            1294.0  \n3                           22064.0  \n4                             439.0  \n..                              ...  \n97                           5734.0  \n98                          16179.0  \n99                           6558.0  \n100                          5132.0  \n101                          8886.0  \n\n[102 rows x 6 columns]","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>country</th>\n      <th>vaccines</th>\n      <th>total_vaccinations</th>\n      <th>total_vaccinations_per_hundred</th>\n      <th>daily_vaccinations</th>\n      <th>daily_vaccinations_per_million</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Albania</td>\n      <td>Pfizer/BioNTech</td>\n      <td>4177.0</td>\n      <td>0.15</td>\n      <td>405.0</td>\n      <td>141.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Algeria</td>\n      <td>Sputnik V</td>\n      <td>75000.0</td>\n      <td>0.17</td>\n      <td>3748.0</td>\n      <td>85.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Andorra</td>\n      <td>Pfizer/BioNTech</td>\n      <td>2141.0</td>\n      <td>2.77</td>\n      <td>100.0</td>\n      <td>1294.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Anguilla</td>\n      <td>Oxford/AstraZeneca</td>\n      <td>2762.0</td>\n      <td>18.41</td>\n      <td>331.0</td>\n      <td>22064.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Argentina</td>\n      <td>Sputnik V</td>\n      <td>722234.0</td>\n      <td>1.60</td>\n      <td>19820.0</td>\n      <td>439.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>Turks and Caicos Islands</td>\n      <td>Pfizer/BioNTech</td>\n      <td>6433.0</td>\n      <td>16.62</td>\n      <td>222.0</td>\n      <td>5734.0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>United Arab Emirates</td>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...</td>\n      <td>5557793.0</td>\n      <td>56.19</td>\n      <td>160020.0</td>\n      <td>16179.0</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>United Kingdom</td>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>18348165.0</td>\n      <td>27.03</td>\n      <td>445204.0</td>\n      <td>6558.0</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>United States</td>\n      <td>Moderna, Pfizer/BioNTech</td>\n      <td>64177474.0</td>\n      <td>19.19</td>\n      <td>1716311.0</td>\n      <td>5132.0</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>Wales</td>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>905000.0</td>\n      <td>28.70</td>\n      <td>28015.0</td>\n      <td>8886.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>102 rows × 6 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Map showing vaccine types and total vaccination\nfig=px.scatter_geo(vaccine, locations='country',locationmode='country names',color='vaccines', size='total_vaccinations',hover_name='vaccines', projection='natural earth')\nfig.update_layout(title='Vaccine Used by Countries with Total Vaccinated', legend_orientation='h',legend_title_text='Vaccine', autosize=False, width=800,height=600,)","execution_count":308,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"geo":"geo","hovertemplate":"<b>%{hovertext}</b><br><br>vaccines=Pfizer/BioNTech<br>total_vaccinations=%{marker.size}<br>country=%{location}<extra></extra>","hovertext":["Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech","Pfizer/BioNTech"],"legendgroup":"Pfizer/BioNTech","locationmode":"country names","locations":["Albania","Andorra","Australia","Bermuda","Cayman Islands","Colombia","Costa 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Pfizer/BioNTech              2141.0   \n\n    total_vaccinations_per_hundred  daily_vaccinations  \\\n5                             0.00                 3.0   \n70                            0.00                50.0   \n83                            1.76                 NaN   \n30                            0.00               219.0   \n57                            3.94                69.0   \n34                           43.50               189.0   \n42                            0.24               169.0   \n60                            0.31               595.0   \n2                             2.77               100.0   \n\n    daily_vaccinations_per_million  \n5                              0.0  \n70                            10.0  \n83                             NaN  \n30                             2.0  \n57                          1809.0  \n34                         54264.0  \n42                           215.0  \n60                           916.0  \n2                       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     <td>Sinopharm/Beijing</td>\n      <td>2000.0</td>\n      <td>0.31</td>\n      <td>595.0</td>\n      <td>916.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Andorra</td>\n      <td>Pfizer/BioNTech</td>\n      <td>2141.0</td>\n      <td>2.77</td>\n      <td>100.0</td>\n      <td>1294.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Bargraph showing low total vaccinated counties\nfig= px.bar(adf, x= 'country', y ='total_vaccinations',color='vaccines')\nfig.update_layout(title='Countries with Low Total Vaccination and Vaccine 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 United States                           Moderna, Pfizer/BioNTech   \n21                  China        Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac   \n99         United Kingdom                Oxford/AstraZeneca, Pfizer/BioNTech   \n31                England                Oxford/AstraZeneca, Pfizer/BioNTech   \n45                  India                        Covaxin, Oxford/AstraZeneca   \n50                 Israel                           Moderna, Pfizer/BioNTech   \n15                 Brazil                        Oxford/AstraZeneca, Sinovac   \n96                 Turkey                                            Sinovac   \n98   United Arab Emirates  Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...   \n\n     total_vaccinations  total_vaccinations_per_hundred  daily_vaccinations  \\\n100          64177474.0                           19.19           1716311.0   \n21           40520000.0                            2.82           1916190.0   \n99           18348165.0                 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</tr>\n    <tr>\n      <th>96</th>\n      <td>Turkey</td>\n      <td>Sinovac</td>\n      <td>6837302.0</td>\n      <td>8.11</td>\n      <td>435596.0</td>\n      <td>5165.0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>United Arab Emirates</td>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...</td>\n      <td>5557793.0</td>\n      <td>56.19</td>\n      <td>160020.0</td>\n      <td>16179.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Bargraph showing high total vaccinated counties\nfig= px.bar(adf, x= 'country', y ='total_vaccinations',color='vaccines')\nfig.update_layout(title='Countries with High Total Vaccination and Vaccine Used',showlegend=False)\nfig","execution_count":318,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"alignmentgroup":"True","hovertemplate":"vaccines=Moderna, 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Pfizer/BioNTech              1504.0   \n2            Andorra           Pfizer/BioNTech              2141.0   \n65            Monaco           Pfizer/BioNTech              2400.0   \n33    Faeroe Islands           Pfizer/BioNTech              5682.0   \n42            Guyana        Oxford/AstraZeneca              1852.0   \n34  Falkland Islands        Oxford/AstraZeneca              1515.0   \n30             Egypt         Sinopharm/Beijing              1315.0   \n\n    total_vaccinations_per_hundred  daily_vaccinations  \\\n5                             0.00                 3.0   \n70                            0.00                50.0   \n57                            3.94                69.0   \n2                             2.77               100.0   \n65                            6.12               126.0   \n33                           11.63               135.0   \n42                            0.24               169.0   \n34                           43.50               189.0   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 country                                     vaccines  \\\n21            China  Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac   \n100   United States                     Moderna, Pfizer/BioNTech   \n99   United Kingdom          Oxford/AstraZeneca, Pfizer/BioNTech   \n96           Turkey                                      Sinovac   \n45            India                  Covaxin, Oxford/AstraZeneca   \n31          England          Oxford/AstraZeneca, Pfizer/BioNTech   \n15           Brazil                  Oxford/AstraZeneca, Sinovac   \n66          Morocco        Oxford/AstraZeneca, Sinopharm/Beijing   \n9        Bangladesh                           Oxford/AstraZeneca   \n\n     total_vaccinations  total_vaccinations_per_hundred  daily_vaccinations  \\\n21           40520000.0                            2.82           1916190.0   \n100          64177474.0                           19.19           1716311.0   \n99           18348165.0                           27.03            445204.0   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<td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>18348165.0</td>\n      <td>27.03</td>\n      <td>445204.0</td>\n      <td>6558.0</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>Turkey</td>\n      <td>Sinovac</td>\n      <td>6837302.0</td>\n      <td>8.11</td>\n      <td>435596.0</td>\n      <td>5165.0</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>India</td>\n      <td>Covaxin, Oxford/AstraZeneca</td>\n      <td>11424094.0</td>\n      <td>0.83</td>\n      <td>415332.0</td>\n      <td>301.0</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>England</td>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>15471509.0</td>\n      <td>27.49</td>\n      <td>376532.0</td>\n      <td>6690.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Brazil</td>\n      <td>Oxford/AstraZeneca, Sinovac</td>\n      <td>7028356.0</td>\n      <td>3.31</td>\n      <td>262717.0</td>\n      <td>1236.0</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Morocco</td>\n      <td>Oxford/AstraZeneca, Sinopharm/Beijing</td>\n      <td>2575684.0</td>\n      <td>6.98</td>\n      <td>196829.0</td>\n      <td>5333.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Bangladesh</td>\n      <td>Oxford/AstraZeneca</td>\n      <td>2308157.0</td>\n      <td>1.40</td>\n      <td>196796.0</td>\n      <td>1195.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Bargraph showing high daily vaccinated counties\nfig= px.bar(adf, x= 'country', y ='daily_vaccinations',color='vaccines')\nfig.update_layout(title='Countries with High daily Vaccination and Vaccine Used',legend_orientation='v',autosize=False, width=800,height=600,)\nfig","execution_count":322,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"alignmentgroup":"True","hovertemplate":"vaccines=Sinopharm/Beijing, Sinopharm/Wuhan, 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df.groupby(\"vaccines\")['total_vaccinations'].sum().astype(int).reset_index()\nvac","execution_count":323,"outputs":[{"output_type":"execute_result","execution_count":323,"data":{"text/plain":"                                             vaccines  total_vaccinations\n0                         Covaxin, Oxford/AstraZeneca           186443528\n1                                     Johnson&Johnson               57645\n2        Moderna, Oxford/AstraZeneca, Pfizer/BioNTech           527177568\n3                            Moderna, Pfizer/BioNTech          1680457238\n4                                  Oxford/AstraZeneca            18475202\n5                 Oxford/AstraZeneca, Pfizer/BioNTech           912139461\n6   Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...           147334941\n7               Oxford/AstraZeneca, Sinopharm/Beijing            23825912\n8    Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V              152878\n9                         Oxford/AstraZeneca, Sinovac           112816107\n10                      Oxford/AstraZeneca, Sputnik V               65000\n11                                    Pfizer/BioNTech            31988798\n12                 Pfizer/BioNTech, Sinopharm/Beijing             8371251\n13      Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V             7955009\n14                           Pfizer/BioNTech, Sinovac            36187993\n15                                  Sinopharm/Beijing             1215405\n16        Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac           158487000\n17                                            Sinovac           131522691\n18                                          Sputnik V            21197597","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>vaccines</th>\n      <th>total_vaccinations</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Covaxin, Oxford/AstraZeneca</td>\n      <td>186443528</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Johnson&amp;Johnson</td>\n      <td>57645</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Moderna, Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>527177568</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Moderna, Pfizer/BioNTech</td>\n      <td>1680457238</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Oxford/AstraZeneca</td>\n      <td>18475202</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n      <td>912139461</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...</td>\n      <td>147334941</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Oxford/AstraZeneca, Sinopharm/Beijing</td>\n      <td>23825912</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sputnik V</td>\n      <td>152878</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Oxford/AstraZeneca, Sinovac</td>\n      <td>112816107</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Oxford/AstraZeneca, Sputnik V</td>\n      <td>65000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Pfizer/BioNTech</td>\n      <td>31988798</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Pfizer/BioNTech, Sinopharm/Beijing</td>\n      <td>8371251</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V</td>\n      <td>7955009</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Pfizer/BioNTech, Sinovac</td>\n      <td>36187993</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Sinopharm/Beijing</td>\n      <td>1215405</td>\n    </tr>\n    <tr>\n      <th>16</th>\n  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