
from bs4 import BeautifulSoup as soup
from urllib.request import Request, urlopen
from datetime import date, datetime
fname = 'https://www.worldometers.info/coronavirus/'
req = Request(fname, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req)
page_soup = soup(webpage, "html.parser")


today = datetime.now()
now_str = "%s %d, %d at %d:%s" % (date.today().strftime("%b"), today.day, today.year, today.hour, '0'+str(today.minute) if today.minute < 10 else str(today.minute))
containers = page_soup.findAll("div", {"class": "maincounter-number"})
print("As of %s UTC, there have been %s total COVID-19 cases." % (now_str, containers[0].findAll("span")[0].text.replace(' ', '')))

As of Jul 2, 2021 at 19:48 UTC, there have been 183,832,496 total COVID-19 cases.


#-----General------#
import numpy as np
import pandas as pd
import os
import sys
import math
import random

#-----Plotting-----#
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
import plotly.offline as py
py.init_notebook_mode(connected=True)
import seaborn as sns
from pandas_profiling import ProfileReport

#-----Utility-----#
import itertools
import warnings
warnings.filterwarnings("ignore")
import re
import gc
from bs4 import BeautifulSoup as soup
from urllib.request import Request, urlopen
from datetime import date, datetime

LOOK_AT = 5 # Controls how many bars the user can see in the bar graph
AT_LEAST = 50 # Controls what rank a country must be in terms of total cases to be shown on the bar graph


fname = 'https://www.worldometers.info/coronavirus/#countries'
req = Request(fname, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req)
page_soup = soup(webpage, "html.parser")
today = datetime.now()
today_str = "%s %d, %d" % (date.today().strftime("%b"), today.day, today.year)
yesterday_str = "%s %d, %d" % (date.today().strftime("%b"), today.day-1, today.year)
clean = True


print("This version of the notebook is being run on %s." % today_str)

This version of the notebook is being run on Jul 2, 2021.


table = page_soup.findAll("table", {"id": "main_table_countries_yesterday"})
containers = table[0].findAll("tr", {"style": ""})
del containers[0]

all_data = []
for country in containers:
    country_data = []
    country_container = country.findAll("td")
    if country_container[1].text == 'China':
        continue
    for i in range(1, len(country_container)):
        final_feature = country_container[i].text
        if clean:
            if i != 1 and i != len(country_container)-1:
                final_feature = final_feature.replace(',', '')
                if final_feature.find('+') != -1:
                    final_feature = final_feature.replace('+', '')
                    final_feature = float(final_feature)
                elif final_feature.find('-') != -1:
                    final_feature = final_feature.replace('-', '')
                    final_feature = float(final_feature)*-1
        if final_feature == 'N/A':
            final_feature = 0
        elif final_feature == '' or final_feature == ' ':
            final_feature = -1 #None
        country_data.append(final_feature)
    all_data.append(country_data)


df = pd.DataFrame(all_data)
df = df.drop([i for i in range(15, len(all_data[0]))], axis=1) # Get rid of unnecessary data


column_labels = ["Country", "Total Cases", "New Cases", "Total Deaths", "New Deaths", "Total Recovered", "New Recovered", "Active Cases", "Serious/Critical",
                "Tot Cases/1M", "Deaths/1M", "Total Tests", "Tests/1M", "Population", "Continent"]
df.columns = column_labels


country_labels = page_soup.findAll("a", {"class": "mt_a"})
c_label = []
for country in country_labels:
    c_label.append(country.text)
c_label = set(c_label)

not_counted = []
sorted_countries = set(df['Country']) #Increase computational speed
for country in c_label:
    if country not in sorted_countries:
        not_counted.append(country)


print(not_counted + ['China'])

['Montserrat', 'Greenland', 'Anguilla', 'Solomon Islands', 'Falkland Islands', 'Saint Pierre Miquelon', 'Antigua and Barbuda', 'Vatican City', 'Saint Helena', 'Vanuatu', 'Marshall Islands', 'Grenada', 'Samoa', 'Wallis and Futuna', 'China', 'Micronesia', 'China']


for label in df.columns:
    if label != 'Country' and label != 'Continent':
        df[label] = pd.to_numeric(df[label])


df['%Inc Cases'] = df['New Cases']/df['Total Cases']*100
df['%Inc Deaths'] = df['New Deaths']/df['Total Deaths']*100
df['%Inc Recovered'] = df['New Recovered']/df['Total Recovered']*100


pd.options.display.max_rows = None
df


EXPORT = True
today = datetime.now()
if EXPORT:
    today = date.today()
    df.to_csv(f'covid_stats_{today.year}_{today.month}_{today.day-1}')
    print("Dataset is %.2f MB" % (df.memory_usage(deep=True).sum()/1000000))

Dataset is 0.05 MB


cases_ser = df[["Total Recovered", "Active Cases", "Total Deaths"]].loc[0]
cases_df = pd.DataFrame(cases_ser).reset_index()
cases_df.columns = ['Type', 'Total']
cases_df['Percentage'] = np.round(100*cases_df['Total']/np.sum(cases_df['Total']), 2)
cases_df['Virus'] = ['COVID-19' for i in range(len(cases_df))]

fig = px.bar(cases_df, x='Virus', y='Percentage', color='Type', hover_data=['Total'])
fig.update_layout(title={'text': f"Total Number of Cases, Recoveries, and Deaths on {yesterday_str}", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", xaxis_title="")
fig.show()


new_ser = df[["New Cases", "New Recovered", "New Deaths"]].loc[0]
new_df = pd.DataFrame(new_ser).reset_index()
new_df.columns = ['Type', 'Total']
new_df['Percentage'] = np.round(100*new_df['Total']/np.sum(new_df['Total']), 2)
new_df['Virus'] = ['COVID-19' for i in range(len(new_df))]

fig = px.bar(new_df, x='Virus', y='Percentage', color='Type', hover_data=['Total'])
fig.update_layout(title={'text': f"New Cases, Recoveries, and Deaths on {yesterday_str}", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", xaxis_title="")
fig.show()


pinc_ser = np.round(df[["%Inc Cases", "%Inc Recovered", "%Inc Deaths"]].loc[0], 2)
pinc_df = pd.DataFrame(pinc_ser)
pinc_df.columns = ["Percentage"]

fig = go.Figure()
fig.add_trace(go.Bar(x=pinc_df.index, y=pinc_df['Percentage'], marker_color=["yellow", "green", "red"]))
fig.update_layout(title={'text': f"New Cases, Recoveries, and Deaths on {yesterday_str}", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", xaxis_title="")
fig.show()


continent_df = df.groupby('Continent').sum().drop('All')
continent_df = continent_df.reset_index()
continent_df


cases_vis_list = ['Total Cases', 'Active Cases', 'New Cases', 'Serious/Critical', 'Tot Cases/1M']
deaths_vis_list = ['Total Deaths', 'New Deaths', 'Deaths/1M']
recovered_vis_list = ['Total Recovered', 'New Recovered']
tests_vis_list = ['Total Tests', 'Tests/1M']
essentials = [['Total Cases', 'Active Cases', 'New Cases'], ['Total Deaths', 'New Deaths'], ['Total Recovered', 'New Recovered']]


def continent_visualization(vis_list):
    for label in vis_list:
        c_df = continent_df[['Continent', label]]
        c_df['Percentage'] = np.round(100*c_df[label]/np.sum(c_df[label]), 2)
        c_df['Virus'] = ['COVID-19' for i in range(len(c_df))]
        
        fig = px.bar(c_df, x='Virus', y='Percentage', color='Continent', hover_data=[label])
        fig.update_layout(title={'text': f"{label} at the end of {yesterday_str}", 'x': 0.5,
                                 'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", xaxis_title="")
        fig.show()
        gc.collect()


continent_visualization(cases_vis_list)


continent_visualization(deaths_vis_list)


continent_visualization(tests_vis_list)


def continent_visualization2(index, label, log_scale=False):
    c_df = continent_df[['Continent'] + essentials[index]]
    
    fig = px.bar(c_df, x="Continent", y=essentials[index], log_y=log_scale)
    log_str = "- log scale" if log_scale else ""
    fig.update_layout(title={'text': f"{label} at the end of {yesterday_str} {log_str}", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title=label, xaxis_title="")
    
    fig.show()
    gc.collect()


continent_visualization2(0, "Total Cases", log_scale=True)


continent_visualization2(1, "Deaths", log_scale=True)


continent_visualization2(2, "Recoveries", log_scale=True)


df = df.drop([len(df)-1])
country_df = df.drop([0])


country_l = country_df.columns[1:14]

fig = go.Figure()
c = 0
for i in country_df.index:
    if c < LOOK_AT:
        fig.add_trace(go.Bar(name=country_df['Country'][i], x=country_l, y=country_df.loc[i][1:14]))
    else:
        break
    c += 1
    
fig.update_layout(title={'text': f'{LOOK_AT} Countries with Most COVID Cases on %s' % yesterday_str, 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", yaxis_type="log", xaxis_tickangle=-90)
fig.show()


inc_l = country_df.columns[15:]
inc_df = country_df.sort_values("%Inc Cases", ascending=False)
fig = go.Figure()
c = 0
for i in inc_df.index:
    if i > AT_LEAST:
        continue
    if c < LOOK_AT:
        fig.add_trace(go.Bar(name=country_df['Country'][i], x=inc_l, y=inc_df.loc[i][15:]))
    else:
        break
    c += 1
    
fig.update_layout(title={'text': f'{LOOK_AT} Countries with Highest Increase in COVID Cases on %s' % yesterday_str, 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_title="Percentage", xaxis_tickangle=0)
fig.show()


country_labels = country_df.columns[1:14]

def country_visualization(continent):
    buttons_list = []
    base_list = [False for i in range(len(country_df))]
    c = 0
    for i in country_df.index:
        if country_df.loc[i]['Continent'] != continent:
            continue
        tmp_list = base_list.copy()
        tmp_list[c] = True
        c += 1
        buttons_list.append(dict(
                    args=[{"visible": tmp_list}],
                    label=country_df.loc[i]['Country'],
                    method="update"
                ))


    fig = go.Figure()
    c = 0
    for i in country_df.index:
        if country_df.loc[i]['Continent'] != continent:
            continue
        fig.add_trace(go.Bar(name=country_df.loc[i]['Country'], x=country_labels, y=country_df.loc[i][1:14], visible=False if c != 0 else True))
        c += 1

    fig.update_layout(
        updatemenus=[
            dict(
                buttons=buttons_list,
                direction="down",
                pad={"r": 10, "t": 10},
                showactive=True,
                x=0.1,
                xanchor="left",
                y=1.1,
                yanchor="top"
            ),
        ]
    )

    fig.update_layout(title={'text': '%s COVID-19 Cases Search on %s' % (continent, yesterday_str), 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_type="log", xaxis_tickangle=-90)
    fig.show()


country_visualization('Africa')


country_visualization('Asia')


country_visualization('Australia/Oceania')


country_visualization('Europe')


country_visualization('North America')


country_visualization('South America')


bar_list = []
for i in country_df.index:
    bar_list.append(go.Bar(name=country_df['Country'][i], y=[country_df['Total Cases'][i]]))
    
fig = go.Figure(data=bar_list)
fig.update_layout(title={'text': 'Stacked Bar Chart of All Countries COVID-19 Cases on %s' % yesterday_str, 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, barmode='stack', height=1200)
fig.show()


bar = go.Bar(x=country_df['Country'], y=country_df['Total Cases'], marker=dict(color=df['Total Cases'], colorscale='Reds', showscale=True))
fig = go.Figure(data=[bar])
fig.update_layout(title={'text': 'Number of COVID Cases by Country on %s, log scale' % yesterday_str, 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 20}}, yaxis_type="log", xaxis_tickangle=-90)


country_abbrev = {
'Afghanistan': 'AFG',
 'Albania': 'ALB',
 'Algeria': 'DZA',
 'American Samoa': 'ASM',
 'Andorra': 'AND',
 'Angola': 'AGO',
 'Anguilla': 'AIA',
 'Antigua and Barbuda': 'ATG',
 'Argentina': 'ARG',
 'Armenia': 'ARM',
 'Aruba': 'ABW',
 'Australia': 'AUS',
 'Austria': 'AUT',
 'Azerbaijan': 'AZE',
 'Bahamas, The': 'BHM',
 'Bahrain': 'BHR',
 'Bangladesh': 'BGD',
 'Barbados': 'BRB',
 'Belarus': 'BLR',
 'Belgium': 'BEL',
 'Belize': 'BLZ',
 'Benin': 'BEN',
 'Bermuda': 'BMU',
 'Bhutan': 'BTN',
 'Bolivia': 'BOL',
 'Bosnia and Herzegovina': 'BIH',
 'Botswana': 'BWA',
 'Brazil': 'BRA',
 'British Virgin Islands': 'VGB',
 'Brunei': 'BRN',
 'Bulgaria': 'BGR',
 'Burkina Faso': 'BFA',
 'Burma': 'MMR',
 'Burundi': 'BDI',
 'Cabo Verde': 'CPV',
 'Cambodia': 'KHM',
 'Cameroon': 'CMR',
 'Canada': 'CAN',
 'Cayman Islands': 'CYM',
 'Central African Republic': 'CAF',
 'Chad': 'TCD',
 'Chile': 'CHL',
 'China': 'CHN',
 'Colombia': 'COL',
 'Comoros': 'COM',
 'Congo, Democratic Republic of the': 'COD',
 'Congo, Republic of the': 'COG',
 'Cook Islands': 'COK',
 'Costa Rica': 'CRI',
 "Cote d'Ivoire": 'CIV',
 'Croatia': 'HRV',
 'Cuba': 'CUB',
 'Curacao': 'CUW',
 'Cyprus': 'CYP',
 'Czech Republic': 'CZE',
    'Czechia': 'CZE',
 'Denmark': 'DNK',
 'Djibouti': 'DJI',
 'Dominica': 'DMA',
 'Dominican Republic': 'DOM',
 'Ecuador': 'ECU',
 'Egypt': 'EGY',
 'El Salvador': 'SLV',
 'Equatorial Guinea': 'GNQ',
 'Eritrea': 'ERI',
 'Estonia': 'EST',
 'Ethiopia': 'ETH',
 'Falkland Islands (Islas Malvinas)': 'FLK',
 'Faroe Islands': 'FRO',
 'Fiji': 'FJI',
 'Finland': 'FIN',
 'France': 'FRA',
 'French Polynesia': 'PYF',
 'Gabon': 'GAB',
 'Gambia, The': 'GMB',
 'Georgia': 'GEO',
 'Germany': 'DEU',
 'Ghana': 'GHA',
 'Gibraltar': 'GIB',
 'Greece': 'GRC',
 'Greenland': 'GRL',
 'Grenada': 'GRD',
 'Guam': 'GUM',
 'Guatemala': 'GTM',
 'Guernsey': 'GGY',
 'Guinea-Bissau': 'GNB',
 'Guinea': 'GIN',
 'Guyana': 'GUY',
 'Haiti': 'HTI',
 'Honduras': 'HND',
 'Hong Kong': 'HKG',
 'Hungary': 'HUN',
 'Iceland': 'ISL',
 'India': 'IND',
 'Indonesia': 'IDN',
 'Iran': 'IRN',
 'Iraq': 'IRQ',
 'Ireland': 'IRL',
 'Isle of Man': 'IMN',
 'Israel': 'ISR',
 'Italy': 'ITA',
 'Jamaica': 'JAM',
 'Japan': 'JPN',
 'Jersey': 'JEY',
 'Jordan': 'JOR',
 'Kazakhstan': 'KAZ',
 'Kenya': 'KEN',
 'Kiribati': 'KIR',
 'Korea, North': 'PRK',
 'Korea, South': 'KOR',
 'Kosovo': 'KSV',
 'Kuwait': 'KWT',
 'Kyrgyzstan': 'KGZ',
 'Laos': 'LAO',
 'Latvia': 'LVA',
 'Lebanon': 'LBN',
 'Lesotho': 'LSO',
 'Liberia': 'LBR',
 'Libya': 'LBY',
 'Liechtenstein': 'LIE',
 'Lithuania': 'LTU',
 'Luxembourg': 'LUX',
 'Macau': 'MAC',
 'Macedonia': 'MKD',
 'Madagascar': 'MDG',
 'Malawi': 'MWI',
 'Malaysia': 'MYS',
 'Maldives': 'MDV',
 'Mali': 'MLI',
 'Malta': 'MLT',
 'Marshall Islands': 'MHL',
 'Mauritania': 'MRT',
 'Mauritius': 'MUS',
 'Mexico': 'MEX',
 'Micronesia, Federated States of': 'FSM',
 'Moldova': 'MDA',
 'Monaco': 'MCO',
 'Mongolia': 'MNG',
 'Montenegro': 'MNE',
 'Morocco': 'MAR',
 'Mozambique': 'MOZ',
 'Namibia': 'NAM',
 'Nepal': 'NPL',
 'Netherlands': 'NLD',
 'New Caledonia': 'NCL',
 'New Zealand': 'NZL',
 'Nicaragua': 'NIC',
 'Nigeria': 'NGA',
 'Niger': 'NER',
 'Niue': 'NIU',
 'Northern Mariana Islands': 'MNP',
 'Norway': 'NOR',
 'Oman': 'OMN',
 'Pakistan': 'PAK',
 'Palau': 'PLW',
 'Panama': 'PAN',
 'Papua New Guinea': 'PNG',
 'Paraguay': 'PRY',
 'Peru': 'PER',
 'Philippines': 'PHL',
 'Poland': 'POL',
 'Portugal': 'PRT',
 'Puerto Rico': 'PRI',
 'Qatar': 'QAT',
 'Romania': 'ROU',
 'Russia': 'RUS',
 'Rwanda': 'RWA',
 'Saint Kitts and Nevis': 'KNA',
 'Saint Lucia': 'LCA',
 'Saint Martin': 'MAF',
 'Saint Pierre and Miquelon': 'SPM',
 'Saint Vincent and the Grenadines': 'VCT',
 'Samoa': 'WSM',
 'San Marino': 'SMR',
 'Sao Tome and Principe': 'STP',
 'Saudi Arabia': 'SAU',
 'Senegal': 'SEN',
 'Serbia': 'SRB',
 'Seychelles': 'SYC',
 'Sierra Leone': 'SLE',
 'Singapore': 'SGP',
 'Sint Maarten': 'SXM',
 'Slovakia': 'SVK',
 'Slovenia': 'SVN',
 'Solomon Islands': 'SLB',
 'Somalia': 'SOM',
 'South Africa': 'ZAF',
 'South Sudan': 'SSD',
 'Spain': 'ESP',
 'Sri Lanka': 'LKA',
 'Sudan': 'SDN',
 'Suriname': 'SUR',
 'Swaziland': 'SWZ',
 'Sweden': 'SWE',
 'Switzerland': 'CHE',
 'Syria': 'SYR',
 'Taiwan': 'TWN',
 'Tajikistan': 'TJK',
 'Tanzania': 'TZA',
 'Thailand': 'THA',
 'Timor-Leste': 'TLS',
 'Togo': 'TGO',
 'Tonga': 'TON',
 'Trinidad and Tobago': 'TTO',
 'Tunisia': 'TUN',
 'Turkey': 'TUR',
 'Turkmenistan': 'TKM',
 'Tuvalu': 'TUV',
 'Uganda': 'UGA',
 'Ukraine': 'UKR',
 'United Arab Emirates': 'ARE',
    'UAE': 'ARE',
 'United Kingdom': 'GBR',
    'UK': 'GBR',
 'United States': 'USA',
    'USA': 'USA',
 'Uruguay': 'URY',
 'Uzbekistan': 'UZB',
 'Vanuatu': 'VUT',
 'Venezuela': 'VEN',
 'Vietnam': 'VNM',
 'Virgin Islands': 'VGB',
 'West Bank': 'WBG',
 'Yemen': 'YEM',
 'Zambia': 'ZMB',
 'Zimbabwe': 'ZWE',
    'S. Korea': 'KOR'
}


country_df['iso_code'] = country_df['Country'].map(country_abbrev)
map_country_df = country_df.loc[~country_df['iso_code'].isna()]
map_country_df['death_rate'] = (100*map_country_df['Total Deaths']/map_country_df['Total Cases']).round(2)
map_country_df.head()


fig = go.Figure(data=go.Choropleth(
    locations=map_country_df['iso_code'],
    z=map_country_df['Total Cases'].astype(float),
    colorscale="Reds",
    colorbar_title="Total"
))

fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    title={'text': f"Total COVID Cases in the World", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 16}}
)

fig.show()


fig = go.Figure(data=go.Choropleth(
    locations=map_country_df['iso_code'],
    z=map_country_df['Total Deaths'].astype(float),
    colorscale="Reds",
    colorbar_title="Total"
))

fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    title={'text': f"Total COVID Deaths in the World", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 16}}
)

fig.show()


fig = go.Figure(data=go.Choropleth(
    locations=map_country_df['iso_code'],
    z=map_country_df['death_rate'].astype(float),
    colorscale="Reds",
    colorbar_title="Percentage"
))

fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    title={'text': f"Death Rate of Different Countries in the World", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 16}}
)

fig.show()


fig = go.Figure(data=go.Choropleth(
    locations=map_country_df['iso_code'],
    z=map_country_df['Deaths/1M'].astype(float),
    colorscale="Reds",
    colorbar_title="Per Million"
))

fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    title={'text': f"Deaths Per Million in the World", 'x': 0.5,
                         'xanchor': 'center', 'font': {'size': 16}}
)

fig.show()

	Continent	Total Cases	New Cases	Total Deaths	New Deaths	Total Recovered	New Recovered	Active Cases	Serious/Critical	Tot Cases/1M	Deaths/1M	Total Tests	Tests/1M	Population	%Inc Cases	%Inc Deaths	%Inc Recovered
0	Africa	5597459	42324.0	144152	762.0	4865662	18797.0	587645	4566	632767.0	9008.0	53288244	5067522	1372550040	18.549048	-103.361394	3.254985
1	Asia	55895372	152190.0	789607	2362.0	53261352	131082.0	1844412	26628	1516336.0	14860.2	827190324	36273093	3204101596	26.007992	-41.771949	25.086106
2	Australia/Oceania	74469	460.0	1274	-2.0	68389	33.0	4805	26	76704.0	588.0	23489089	1716830	41387409	8.173346	107.261696	2.219896
3	Europe	48057971	77237.0	1103780	949.0	45470438	44986.0	1483753	6844	3561256.0	69386.0	973440513	85766626	748081910	5.934701	-114.484000	3.435491
4	North America	40643482	34094.0	919005	707.0	34134543	35722.0	5589933	10886	1267712.0	17499.0	568482326	26812243	593429579	18.766630	-219.557430	16.625885
5	South America	33037753	126062.0	1008667	3458.0	28299160	131844.0	1865059	29690	801247.0	22166.0	132952969	5014762	434309452	5.388683	7.379605	5.694784

	Country	Total Cases	New Cases	Total Deaths	New Deaths	Total Recovered	New Recovered	Active Cases	Serious/Critical	Tot Cases/1M	Deaths/1M	Total Tests	Tests/1M	Population	Continent	%Inc Cases	%Inc Deaths	%Inc Recovered	iso_code	death_rate
1	USA	34561607	17153.0	620657	319.0	29052087	25399.0	4888863	3868	103806.0	1864.0	506604612	1521587	332944930	North America	0.049630	0.051397	0.087426	USA	1.80
2	India	30453937	43360.0	400271	796.0	29536087	54565.0	517579	8944	21853.0	287.0	412021494	295659	1393568297	Asia	0.142379	0.198865	0.184740	IND	1.31
3	Brazil	18622304	63140.0	520189	1943.0	16931272	72640.0	1170843	8318	86991.0	2430.0	53209627	248561	214070952	South America	0.339056	0.373518	0.429029	BRA	2.79
4	France	5777965	2664.0	111111	29.0	5622756	3846.0	44098	1162	88324.0	1698.0	93465436	1428738	65418158	Europe	0.046106	0.026100	0.068401	FRA	1.92
5	Russia	5538142	23543.0	135886	672.0	5017321	16928.0	384935	2300	37933.0	931.0	150121327	1028248	145997153	Europe	0.425106	0.494532	0.337391	RUS	2.45

Daily COVID-19

What is the COVID-19/Coronavirus?

Aims of this Project

Table of Contents

Gathering the Data (Web Scraping)

Essential Imports

Web Scraping Foundation

Scraping Script

What Countries are not present in the Analysis?

Final Processing

Export

General Visualizations

By Continent

By Countries

COVID-19 Cases Search

Choropleth Visualizations

	Country	Total Cases	New Cases	Total Deaths	New Deaths	Total Recovered	New Recovered	Active Cases	Serious/Critical	Tot Cases/1M	Deaths/1M	Total Tests	Tests/1M	Population	Continent	%Inc Cases	%Inc Deaths	%Inc Recovered
0	World	183401218	432413.0	3971191	8320.0	167909780	362543.0	11520247	78708	23529.0	509.5	-1	-1	-1	All	0.235774	0.209509	0.215915
1	USA	34561607	17153.0	620657	319.0	29052087	25399.0	4888863	3868	103806.0	1864.0	506604612	1521587	332944930	North America	0.049630	0.051397	0.087426
2	India	30453937	43360.0	400271	796.0	29536087	54565.0	517579	8944	21853.0	287.0	412021494	295659	1393568297	Asia	0.142379	0.198865	0.184740
3	Brazil	18622304	63140.0	520189	1943.0	16931272	72640.0	1170843	8318	86991.0	2430.0	53209627	248561	214070952	South America	0.339056	0.373518	0.429029
4	France	5777965	2664.0	111111	29.0	5622756	3846.0	44098	1162	88324.0	1698.0	93465436	1428738	65418158	Europe	0.046106	0.026100	0.068401
5	Russia	5538142	23543.0	135886	672.0	5017321	16928.0	384935	2300	37933.0	931.0	150121327	1028248	145997153	Europe	0.425106	0.494532	0.337391
6	Turkey	5430940	5288.0	49774	42.0	5300504	6219.0	80662	706	63709.0	584.0	61012512	715722	85246138	Asia	0.097368	0.084381	0.117328
7	UK	4828044	27850.0	128162	22.0	4327556	2680.0	372326	287	70748.0	1878.0	213530473	3128977	68242904	Europe	0.576838	0.017166	0.061929
8	Argentina	4491551	21177.0	94772	468.0	4092053	15132.0	304726	6046	98476.0	2078.0	16798807	368310	45610453	South America	0.471485	0.493817	0.369790
9	Colombia	4269297	28315.0	107137	593.0	3964074	27918.0	198086	8155	83020.0	2083.0	20060182	390086	51424973	South America	0.663224	0.553497	0.704275
10	Italy	4260788	882.0	127587	21.0	4083843	1941.0	49358	229	70575.0	2113.0	71771680	1188810	60372723	Europe	0.020700	0.016459	0.047529
11	Spain	3821305	12345.0	80883	8.0	3603584	3860.0	136838	584	81699.0	1729.0	52691812	1126545	46772921	Europe	0.323057	0.009891	0.107116
12	Germany	3736940	717.0	91530	71.0	3625700	1500.0	19710	736	44460.0	1089.0	63813168	759209	84052180	Europe	0.019187	0.077570	0.041371
13	Iran	3218860	14303.0	84389	125.0	2889939	13111.0	244532	3187	37839.0	992.0	23308926	274007	85066920	Asia	0.444350	0.148124	0.453677
14	Poland	2880011	99.0	75044	23.0	2651906	119.0	153061	130	76181.0	1985.0	17444955	461445	37805071	Europe	0.003437	0.030649	0.004487
15	Mexico	2519269	6105.0	233047	244.0	2000530	3149.0	285692	4798	19336.0	1789.0	7507730	57625	130286775	North America	0.242332	0.104700	0.157408
16	Ukraine	2235801	705.0	52391	51.0	2168387	1719.0	15023	177	51433.0	1205.0	10861246	249855	43470207	Europe	0.031532	0.097345	0.079276
17	Indonesia	2203108	24836.0	58995	504.0	1890287	9874.0	253826	-1	7970.0	213.0	20079271	72642	276414625	Asia	1.127317	0.854310	0.522355
18	Peru	2057554	3079.0	192687	122.0	0	0.0	0	2346	61547.0	5764.0	14224136	425483	33430551	South America	0.149644	0.063315	NaN
19	South Africa	1995556	21584.0	61029	382.0	1754793	6751.0	179734	546	33228.0	1016.0	13209499	219954	60055730	Africa	1.081603	0.625932	0.384718
20	Netherlands	1685825	825.0	17748	3.0	1634035	2320.0	34042	126	98168.0	1033.0	14730069	857754	17172841	Europe	0.048937	0.016903	0.141980
21	Czechia	1667444	137.0	30307	1.0	1635727	16.0	1410	11	155416.0	2825.0	29875529	2784590	10728879	Europe	0.008216	0.003300	0.000978
22	Chile	1558557	2655.0	32588	43.0	1502411	8127.0	23558	2943	80837.0	1690.0	17012734	882390	19280298	South America	0.170350	0.131950	0.540931
23	Philippines	1418326	5784.0	24796	134.0	1341851	2737.0	51679	1957	12773.0	223.0	14988187	134983	111037892	Asia	0.407805	0.540410	0.203972
24	Canada	1415310	26.0	26295	-1.0	1381943	41.0	7072	466	37174.0	691.0	36768392	965732	38073065	North America	0.001837	-0.003803	0.002967
25	Iraq	1353458	7554.0	17216	30.0	1250121	4593.0	86121	633	32913.0	419.0	11771294	286254	41121807	Asia	0.558126	0.174257	0.367404
26	Sweden	1090880	328.0	14592	-1.0	1061185	2786.0	15103	41	107346.0	1436.0	10828578	1065566	10162275	Europe	0.030067	-0.006853	0.262537
27	Belgium	1085131	581.0	25173	3.0	1036831	799.0	23127	143	93223.0	2163.0	15270322	1311871	11640113	Europe	0.053542	0.011918	0.077062
28	Romania	1080823	31.0	33897	5.0	1045351	-1.0	1575	67	56561.0	1774.0	9831916	514519	19108946	Europe	0.002868	0.014751	-0.000096
29	Pakistan	958408	1037.0	22321	40.0	904320	836.0	31767	1844	4256.0	99.0	14590230	64797	225169489	Asia	0.108200	0.179203	0.092445
30	Bangladesh	921559	8301.0	14646	143.0	820913	4663.0	86000	1192	5541.0	88.0	6640982	39927	166329638	Asia	0.900756	0.976376	0.568026
31	Portugal	882006	2449.0	17101	5.0	830224	1234.0	34681	113	86753.0	1682.0	13332860	1311398	10166903	Europe	0.277663	0.029238	0.148635
32	Israel	842067	290.0	6429	-1.0	833600	134.0	2038	27	90292.0	689.0	15091313	1618198	9326000	Asia	0.034439	-0.015555	0.016075
33	Hungary	808160	32.0	29992	-1.0	739204	422.0	38964	20	83871.0	3113.0	6138529	637057	9635762	Europe	0.003960	-0.003334	0.057088
34	Japan	799975	1816.0	14781	41.0	768328	1712.0	16866	517	6345.0	117.0	16445549	130429	126088301	Asia	0.227007	0.277383	0.222822
35	Malaysia	758967	6988.0	5254	84.0	688260	5580.0	65453	917	23153.0	160.0	14645806	446787	32780259	Asia	0.920725	1.598782	0.810740
36	Jordan	751937	533.0	9756	6.0	735610	516.0	6571	467	72972.0	947.0	7892481	765934	10304394	Asia	0.070884	0.061501	0.070146
37	Serbia	716643	81.0	7047	-1.0	707706	115.0	1890	13	82353.0	810.0	4422502	508212	8702081	Europe	0.011303	-0.014190	0.016250
38	Switzerland	703176	172.0	10894	3.0	684621	756.0	7661	39	80659.0	1250.0	8329750	955473	8717935	Europe	0.024460	0.027538	0.110426
39	Austria	650474	62.0	10706	4.0	637909	158.0	1859	52	71814.0	1182.0	55451224	6121916	9057822	Europe	0.009532	0.037362	0.024768