
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go

import os
import math


from datetime import datetime, timedelta
dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d')


df = pd.read_csv('data/worldometer_coronavirus_daily_data.csv',
                 parse_dates=['date'], date_parser=dateparse)

df


df_summary = pd.read_csv('data/worldometer_coronavirus_summary_data.csv')

df_summary


df['continent'] = df.apply(lambda row: df_summary[df_summary.country == row.country].iloc[0].continent, axis=1)

df


trace = go.Pie(labels=['Total Recovered', 'Total Active', 'Total Deaths'],
               values=[df_summary.total_recovered.sum(), df_summary.active_cases.sum(), df_summary.total_deaths.sum()],
               title="<b>Coronavirus Cases</b>",
               title_font_size=18,
               hovertemplate="<b>%{label}</b><br>%{value}<br><i>%{percent}</i>",
               #hoverinfo='percent+value+label',
               textinfo='percent',
               textposition='inside',
               hole=0.6,
               showlegend=True,
               marker=dict(colors=["#8dd3c7", "ffffb3", "#fb8072"],
                           line=dict(color='#000000',
                                     width=2),
                          ),
               name=""
              )
fig=go.Figure(data=[trace])
fig.show()


def add_commas(num):
    out = ""
    counter = 0
    for n in num[::-1]:
        counter += 1
        if counter == 4:
            counter = 1
            out = "," + out
        out = n + out
    return out

print(f"As of {df.date.max().strftime('%Y-%m-%d')}, here are the numbers:\n")

print(add_commas(str(int(df_summary.total_deaths.sum()))), "total deaths. That is more than the entire population of ", end="")
deaths_ref = df_summary[df_summary.population < df_summary.total_deaths.sum()].sort_values("population", ascending=False).iloc[:2]
print(deaths_ref.iloc[0].country, f"({add_commas(str(int(deaths_ref.iloc[0].population)))}) or",
      deaths_ref.iloc[1].country, f"({add_commas(str(int(deaths_ref.iloc[1].population)))})!")

print(add_commas(str(int(df_summary.active_cases.sum()))), "active cases. You can think of that as if the entire population of ", end="")
active_ref = df_summary[df_summary.population < df_summary.active_cases.sum()].sort_values("population", ascending=False).iloc[:2]
print(active_ref.iloc[0].country, f"({add_commas(str(int(active_ref.iloc[0].population)))}), or",
      active_ref.iloc[1].country, f"({add_commas(str(int(active_ref.iloc[1].population)))}), were sick right now!")

print(add_commas(str(int(df_summary.total_recovered.sum()))), "total recoveries. It's as if the entire population of ", end="")
recover_ref = df_summary[df_summary.population < df_summary.total_recovered.sum()].sort_values("population", ascending=False).iloc[:2]
print(recover_ref.iloc[0].country, f"({add_commas(str(int(recover_ref.iloc[0].population)))}), or",
      recover_ref.iloc[1].country, f"({add_commas(str(int(recover_ref.iloc[1].population)))}), went through and recovered from Covid-19!")

As of 2022-05-14, here are the numbers:

6,288,083 total deaths. That is more than the entire population of Singapore (5,936,034) or Denmark (5,830,190)!
13,996,500 active cases. You can think of that as if the entire population of Guinea (13,795,931), or Rwanda (13,549,956), were sick right now!
460,397,633 total recoveries. It's as if the entire population of USA (334,617,623), or Indonesia (278,910,317), went through and recovered from Covid-19!


df_summary['log(Total Confirmed)'] = np.log2(df_summary['total_confirmed'])
df_summary['Total Confirmed'] = df_summary['total_confirmed'].apply(lambda x: add_commas(str(x)))

fig = px.choropleth(df_summary,
                    locations="country",
                    color="log(Total Confirmed)",
                    locationmode = 'country names',
                    hover_name='country',
                    hover_data=['Total Confirmed'],
                    color_continuous_scale='reds',
                    title = '<b>Coronavirus Confirmed Cases Around The Globe</b>')


log_scale_vals = list(range(0,25,2))
scale_vals = (np.exp2(log_scale_vals)).astype(int).astype(str)

scale_vals = list(map(add_commas, scale_vals))

fig.update_layout(title_font_size=22,
                  margin={"r":20, "l":30},
                  coloraxis={#"showscale":False,
                            "colorbar":dict(title="<b>Confirmed Cases</b><br>",
                                            #range=[np.log(50), np.log(6400)],
                                            titleside="top",
                                            tickmode="array",
                                            tickvals=log_scale_vals,
                                            ticktext=scale_vals
                                        )},
                 )

fig.show()


fig = px.treemap(df_summary, path=["country"], values="total_confirmed", height = 750,
                 title="<b>Total Coronavirus Confirmed Cases Breakdown by Country</b>",
                 color_discrete_sequence = px.colors.qualitative.Set3)

fig.update_traces(textinfo = "label+text+value")
fig.show()


def plot_stats(country):
    if country in ["USA", "UK"]:
        country_prefix = "the "
    else:
        country_prefix = ""
    df_country = df[df.country == country]
    df_country.set_index('date', inplace=True)

    # Plot 1
    if not all(df_country.cumulative_total_cases.isna()):
        layout = go.Layout(
            yaxis={'range':[0, df_country.cumulative_total_cases[-1] * 1.05],
                  'title':'Coronavirus Confirmed Cases'},
            xaxis={'title':''},
            )

        fig = px.area(df_country, x=df_country.index, y="cumulative_total_cases",
                      title=f"<b>Cumulative Total Confirmed Cases in {country_prefix}{country}<br>from {df_country.index[0].strftime('%Y-%m-%d')} till {df_country.index[-1].strftime('%Y-%m-%d')}</b>",
                      template='plotly_dark')

        fig.update_traces(line={'width':5})

        fig.update_layout(layout)
        fig.show()

    # Plot 2
    if not all(df_country.daily_new_cases.isna()):
        layout = go.Layout(
            yaxis={'range':[0, df_country.daily_new_cases.max() * 1.05],
                  'title':'Daily New Coronavirus Confirmed Cases'},
            xaxis={'title':''},
            template='plotly_dark',
            title=f"<b>Daily New Cases in {country_prefix}{country}<br>from {df_country.index[0].strftime('%Y-%m-%d')} till {df_country.index[-1].strftime('%Y-%m-%d')}</b>",
            )

        MA7 = df_country.daily_new_cases.rolling(7).mean().dropna().astype(int)

        fig = go.Figure()
        fig.add_trace(go.Bar(name="Daily Cases", x=df_country.index, y=df_country.daily_new_cases))
        fig.add_trace(go.Scatter(name="7-Day Moving Average", x=df_country.index[df_country.shape[0] - MA7.shape[0]:], y=MA7, line=dict(width=3)))

        fig.update_layout(layout)
        fig.show()

    # Plot 3
    if not all(df_country.cumulative_total_deaths.isna()):
        layout = go.Layout(
            yaxis={'range':[0, df_country.cumulative_total_deaths[-1] * 1.05],
                  'title':'Coronavirus Deaths'},
            xaxis={'title':''},
            )

        fig = px.area(df_country, x=df_country.index, y="cumulative_total_deaths",
                      title=f"<b>Cumulative Total Deaths in {country_prefix}{country}<br>from {df_country.index[0].strftime('%Y-%m-%d')} till {df_country.index[-1].strftime('%Y-%m-%d')}</b>",
                      template='plotly_dark')

        fig.update_traces(line={'color':'red', 'width':5})

        fig.update_layout(layout)
        fig.show()

    # Plot 4
    if not all(df_country.daily_new_deaths.isna()):
        layout = go.Layout(
            yaxis={'range':[0, df_country.daily_new_deaths.max() * 1.05],
                  'title':'Daily New Coronavirus Deaths'},
            xaxis={'title':''},
            template='plotly_dark',
            title=f"<b>Daily Deaths in {country_prefix}{country}<br>from {df_country.index[0].strftime('%Y-%m-%d')} till {df_country.index[-1].strftime('%Y-%m-%d')}</b>",
            )

        MA7 = df_country.daily_new_deaths.rolling(7).mean().dropna().astype(int)

        fig = go.Figure()
        fig.add_trace(go.Bar(name="Daily Deaths", x=df_country.index, y=df_country.daily_new_deaths, marker_color='red'))
        fig.add_trace(go.Scatter(name="7-Day Moving Average", x=df_country.index[df_country.shape[0] - MA7.shape[0]:], y=MA7, line={'width':3, 'color':'white'}))

        fig.update_layout(layout)
        fig.show()

    # Plot 5
    if not all(df_country.active_cases.isna()):
        layout = go.Layout(
            yaxis={'range':[0, df_country.active_cases.max() * 1.05],
                  'title':'Active Coronavirus Cases'},
            xaxis={'title':''},
            )

        fig = px.line(df_country, x=df_country.index, y="active_cases",
                      title=f"<b>Active Cases in {country_prefix}{country}<br>from {df_country.index[0].strftime('%Y-%m-%d')} till {df_country.index[-1].strftime('%Y-%m-%d')}</b>",
                      template='plotly_dark')

        fig.update_traces(line={'color':'yellow', 'width':5})

        fig.update_layout(layout)
        fig.show()


plot_stats('USA')


plot_stats('China')


plot_stats('UK')


plot_stats('Italy')


plot_stats("India")


plot_stats("Australia")


plot_stats("France")


def plot_continent(continent):
    df_continent = df[df.continent == continent]
    fig = px.line(df_continent, x="date", y="cumulative_total_cases", color="country", #log_y=True,
                  line_group="country", hover_name="country", template="plotly_dark")

    annotations = []
    # Adding labels
    ys = []
    for tr in fig.select_traces():
        ys.append(tr.y[-1])
    y_scale = 0.155 / max(ys)
    for tr in fig.select_traces():
        # labeling the right_side of the plot
        size = max(1, int(math.log(tr.y[-1], 1.1) * tr.y[-1] * y_scale))
        annotations.append(dict(x=tr.x[-1] + timedelta(hours=int((2 + size/5) * 24)), y=tr.y[-1],
                                xanchor='left', yanchor='middle',
                                text=tr.name,
                                font=dict(family='Arial',
                                          size=7+int(size/2)
                                         ),
                                showarrow=False))
        fig.add_trace(go.Scatter(
            x=[tr.x[-1]],
            y=[tr.y[-1]],
            mode='markers',
            name=tr.name,
            marker=dict(color=tr.line.color, size=size)
        ))
    fig.update_traces(line={'width':1})
    fig.update_layout(annotations=annotations, showlegend=False, uniformtext_mode='hide',
                      title=f"<b>Cumulative Total Coronavirus Cases in {continent}<br>between {df_continent.date.min().strftime('%Y-%m-%d')} and {df_continent.date.max().strftime('%Y-%m-%d')}</b>",
                      yaxis={'title':'Coronavirus Confirmed Cases'},
                      xaxis={'title':''}
                     )
    fig.show()


plot_continent("Asia")


plot_continent("Europe")


plot_continent("Africa")


plot_continent("North America")


plot_continent("South America")


plot_continent("Australia/Oceania")


sorted_by_cases_per_1m = df_summary.sort_values(['total_cases_per_1m_population'])
sorted_by_cases_per_1m['% of Population with Confirmed Cases'] = sorted_by_cases_per_1m['total_cases_per_1m_population']/1_000_000
mean = sorted_by_cases_per_1m['% of Population with Confirmed Cases'].mean()
sorted_by_cases_per_1m['color'] = sorted_by_cases_per_1m.apply(lambda row: "Red" if row['% of Population with Confirmed Cases'] > mean else "Blue", axis=1)
fig = px.scatter(sorted_by_cases_per_1m, x='country', y='% of Population with Confirmed Cases',
                 size='% of Population with Confirmed Cases',
                 color='color',
                 title=f"<b>Coronavirus Infection-Rate by Country as of {df.date.max().strftime('%Y-%m-%d')}</b>",
                 height=650)
fig.update_traces(marker_line_color='rgb(75,75,75)',
                  marker_line_width=1.5, opacity=0.8,
                  hovertemplate="<b>%{x}</b><br>%{y} of Population with Confirmed Cases<extra></extra>",)
fig.update_layout(showlegend=False,
                 yaxis={"tickformat":".3%", "range":[0,sorted_by_cases_per_1m['% of Population with Confirmed Cases'].max() * 1.1]},
                 xaxis={"title": ""},
                 title_font_size=20)


to_mention = ["China", "Australia", "India", "South Africa", "Russia", "Italy","Brazil", "UK", "France", "USA",  "Montenegro"]

for i, country in enumerate(to_mention):
    ay = 30 if i%2 else -30
    ax = 20
    if country == "USA": ay, ax = -30, -20
    if country == "UK": ax = -20
    if country == "France": ay, ax = -60, -40
    if country == "Russia": ax = -20
    if country == "Australia": ay = -30
    if country == "Brazil": ax = -20
    fig.add_annotation(
            x=country,
            y=sorted_by_cases_per_1m['% of Population with Confirmed Cases'][sorted_by_cases_per_1m.index[sorted_by_cases_per_1m.country==country][0]],
            xref="x",
            yref="y",
            text=country,
            showarrow=True,
            font=dict(
                family="Courier New, monospace",
                size=14,
                color="#ffffff"
                ),
            align="center",
            arrowhead=2,
            arrowsize=1,
            arrowwidth=2,
            arrowcolor="#636363",
            ax=ax,
            ay=ay,
            bordercolor="#c7c7c7",
            borderwidth=2,
            borderpad=4,
            bgcolor=sorted_by_cases_per_1m['color'][sorted_by_cases_per_1m.index[sorted_by_cases_per_1m.country==country][0]],
            opacity=0.6
            )

fig.add_shape(type='line',
              x0=sorted_by_cases_per_1m['country'].iloc[0], y0=mean,
              x1=sorted_by_cases_per_1m['country'].iloc[-1], y1=mean,
              line=dict(color='Green',width=1),
              xref='x', yref='y'
             )
fig.add_annotation(x=sorted_by_cases_per_1m['country'].iloc[0], y=mean,
                   text=f"mean = {mean*100:.2f}%",
                   showarrow=False,
                   xanchor="left",
                   yanchor="bottom",
                   font={"color":"Green", "size":14}
                  )
fig.show()


sorted_by_deaths_per_1m = df_summary.sort_values(['total_deaths_per_1m_population'])
sorted_by_deaths_per_1m = sorted_by_deaths_per_1m[sorted_by_deaths_per_1m['total_deaths_per_1m_population'].notna()]
sorted_by_deaths_per_1m['% of Population with Coronavirus Death Cases'] = sorted_by_deaths_per_1m['total_deaths_per_1m_population']/1_000_000
mean = sorted_by_deaths_per_1m['% of Population with Coronavirus Death Cases'].mean()
sorted_by_deaths_per_1m['color'] = sorted_by_deaths_per_1m.apply(lambda row: "Red" if row['% of Population with Coronavirus Death Cases'] > mean else "Blue", axis=1)
#sorted_by_deaths_per_1m.dropna(inplace=True)
fig = px.scatter(sorted_by_deaths_per_1m, x='country', y='% of Population with Coronavirus Death Cases',
                 size='% of Population with Coronavirus Death Cases',
                 color='color',
                 title=f"<b>Coronavirus Death-Rate by Country as of {df.date.max().strftime('%Y-%m-%d')}</b>",
                 height=650)

fig.update_traces(marker_line_color='rgb(75,75,75)',
                  marker_line_width=1.5, opacity=0.8,
                  hovertemplate="<b>%{x}</b><br>%{y} of Population with Death Cases<extra></extra>",)
fig.update_layout(showlegend=False,
                 yaxis={"tickformat":".3%", "range":[0,sorted_by_deaths_per_1m['% of Population with Coronavirus Death Cases'].max() * 1.1]},
                 xaxis={"title": ""},
                 title_font_size=20)


to_mention = ["China", "Australia", "India", "South Africa", "Russia", "Italy","Brazil", "UK", "France", "USA",  "Bulgaria", "Peru"]

for i, country in enumerate(to_mention):
    print
    ay = 30 if i%2 else -30
    ax = 20
    if country == "Russia": ax = -20
    if country == "Czech Republic": ay, ax = -30, -60
    if country == "USA": ay = 50
    if country == "Italy": ay, ax = 30, -20
    if country == "UK": ay, ax = -30, 40
    if country == "Australia": ay = -30
    if country == "France": ay, ax = -60, -40
    if country == "Brazil": ax = -20
    if country == "Peru": ay = -30
    fig.add_annotation(
            x=country,
            y=sorted_by_deaths_per_1m['% of Population with Coronavirus Death Cases'][sorted_by_deaths_per_1m.index[sorted_by_deaths_per_1m.country==country][0]],
            xref="x",
            yref="y",
            text=country,
            showarrow=True,
            font=dict(
                family="Courier New, monospace",
                size=14,
                color="#ffffff"
                ),
            align="center",
            arrowhead=2,
            arrowsize=1,
            arrowwidth=2,
            arrowcolor="#636363",
            ax=ax,
            ay=ay,
            bordercolor="#c7c7c7",
            borderwidth=2,
            borderpad=4,
            bgcolor=sorted_by_deaths_per_1m['color'][sorted_by_deaths_per_1m.index[sorted_by_deaths_per_1m.country==country][0]],
            opacity=0.6
            )

fig.add_shape(type='line',
              x0=sorted_by_deaths_per_1m['country'].iloc[0], y0=mean,
              x1=sorted_by_deaths_per_1m['country'].iloc[-1], y1=mean,
              line=dict(color='Green',width=1),
              xref='x', yref='y'
             )
fig.add_annotation(x=sorted_by_deaths_per_1m['country'].iloc[0], y=mean,
                   text=f"mean = {mean*100:.2f}%",
                   showarrow=False,
                   xanchor="left",
                   yanchor="bottom",
                   font={"color":"Green", "size":14}
                  )

fig.show()


df_summary["Coronavirus Deaths/Confirmed Cases"] = df_summary["total_deaths"] / df_summary["total_confirmed"]
sorted_by_deaths_per_confirmed = df_summary.sort_values(['Coronavirus Deaths/Confirmed Cases'])
sorted_by_deaths_per_confirmed = sorted_by_deaths_per_confirmed[sorted_by_deaths_per_confirmed['Coronavirus Deaths/Confirmed Cases'].notna()]
mean = sorted_by_deaths_per_confirmed['Coronavirus Deaths/Confirmed Cases'].mean()
sorted_by_deaths_per_confirmed['color'] = sorted_by_deaths_per_confirmed.apply(lambda row: "Red" if row['Coronavirus Deaths/Confirmed Cases'] > mean else "Blue", axis=1)
fig = px.scatter(sorted_by_deaths_per_confirmed, x='country', y='Coronavirus Deaths/Confirmed Cases',
                 size='Coronavirus Deaths/Confirmed Cases',
                 color='color',
                 title=f"<b>Coronavirus severity by Country as of {df.date.max().strftime('%Y-%m-%d')}</b>",
                 height=650)

fig.update_traces(marker_line_color='rgb(75,75,75)',
                  marker_line_width=1.5, opacity=0.8,
                  hovertemplate="<b>%{x}</b><br>%{y} of Cases Leading to Death Cases<extra></extra>",)
fig.update_layout(showlegend=False,
                 yaxis={"tickformat":".3%", "range":[0,sorted_by_deaths_per_confirmed['Coronavirus Deaths/Confirmed Cases'].max() * 1.1]},
                 xaxis={"title": ""},
                 title_font_size=20)


to_mention = ["China", "Australia", "India", "South Africa", "Russia", "Italy","Brazil", "UK", "France", "USA", "Yemen", "Vanuatu"]

for i, country in enumerate(to_mention):
    print
    ay = 30 if i%2 else -30
    ax = 20
    if country in ["India", "USA", "Russia"]: ax = -20
    if country == "Yemen": ay = 30
    if country == "UK": ay, ax = -60, -40
    if country == "Belgium": ay, ax = -30, -60
    if country == "USA": ay, ax = -30, 40
    if country == "Italy": ax = -40
    if country == "Australia": ay = -30
    if country == "France": ay, ax = -60, 40
    if country == "Brazil": ay, ax = -60, -20
    fig.add_annotation(
            x=country,
            y=sorted_by_deaths_per_confirmed['Coronavirus Deaths/Confirmed Cases'][sorted_by_deaths_per_confirmed.index[sorted_by_deaths_per_confirmed.country==country][0]],
            xref="x",
            yref="y",
            text=country,
            showarrow=True,
            font=dict(
                family="Courier New, monospace",
                size=14,
                color="#ffffff"
                ),
            align="center",
            arrowhead=2,
            arrowsize=1,
            arrowwidth=2,
            arrowcolor="#636363",
            ax=ax,
            ay=ay,
            bordercolor="#c7c7c7",
            borderwidth=2,
            borderpad=4,
            bgcolor=sorted_by_deaths_per_confirmed['color'][sorted_by_deaths_per_confirmed.index[sorted_by_deaths_per_confirmed.country==country][0]],
            opacity=0.6
            )

fig.add_shape(type='line',
              x0=sorted_by_deaths_per_confirmed['country'].iloc[0], y0=mean,
              x1=sorted_by_deaths_per_confirmed['country'].iloc[-1], y1=mean,
              line=dict(color='Green',width=1),
              xref='x', yref='y'
             )
fig.add_annotation(x=sorted_by_deaths_per_confirmed['country'].iloc[0], y=mean,
                   text=f"mean = {mean*100:.2f}%",
                   showarrow=False,
                   xanchor="left",
                   yanchor="bottom",
                   font={"color":"Green", "size":14}
                  )

fig.show()


active_cases_df = df[['date', 'country', 'active_cases']].dropna().sort_values('date')
active_cases_df = active_cases_df[active_cases_df.active_cases > 0]
active_cases_df['log2(active_cases)'] = np.log2(active_cases_df['active_cases'])
active_cases_df['date'] = active_cases_df['date'].dt.strftime('%m/%d/%Y')

fig = px.choropleth(active_cases_df, locations="country", locationmode='country names',
                    color="log2(active_cases)", hover_name="country", hover_data=['active_cases'],
                    projection="natural earth", animation_frame="date",
                    title='<b>Coronavirus Global Active Cases Over Time</b>',
                    color_continuous_scale="reds",
                   )

fig.update_layout(coloraxis={"colorbar": {"title":"<b>Active Cases</b><br>",
                                          "titleside":"top",
                                          "tickmode":"array",
                                          "tickvals":log_scale_vals,
                                          "ticktext":scale_vals}
                            }
                 )

fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 10
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 2

fig.show()


fig = px.area(df[df.country.isin(df[df.date == df.date.max()].sort_values("active_cases", ascending=False).iloc[:20].country)].sort_values("active_cases", ascending=False),
              x="date", y="active_cases", color="country", template="plotly_dark")#, groupnorm='percent')

fig.update_traces(line={"width":1.25})
fig.update_layout(title = f"Top 20 Countries with Most Active Cases on {df.date.max().strftime('%Y-%m-%d')}",
                  xaxis={"title": ""},
                  yaxis={"title":"Active Cases"})


fig = px.treemap(df_summary, path=["country"], values="active_cases", height = 750,
                 title=f"<b>Active Cases Breakdown on {df.date.max().strftime('%Y-%m-%d')}</b>",
                 color_discrete_sequence = px.colors.qualitative.Set3)

fig.update_traces(textinfo = "label+text+value")
fig.show()

	date	country	cumulative_total_cases	daily_new_cases	active_cases	cumulative_total_deaths	daily_new_deaths
0	2020-02-15	Afghanistan	0.0	NaN	0.0	0.0	NaN
1	2020-02-16	Afghanistan	0.0	NaN	0.0	0.0	NaN
2	2020-02-17	Afghanistan	0.0	NaN	0.0	0.0	NaN
3	2020-02-18	Afghanistan	0.0	NaN	0.0	0.0	NaN
4	2020-02-19	Afghanistan	0.0	NaN	0.0	0.0	NaN
...	...	...	...	...	...	...	...
184782	2022-05-10	Zimbabwe	248642.0	106.0	963.0	5481.0	2.0
184783	2022-05-11	Zimbabwe	248778.0	136.0	1039.0	5481.0	0.0
184784	2022-05-12	Zimbabwe	248943.0	165.0	1158.0	5481.0	0.0
184785	2022-05-13	Zimbabwe	249131.0	188.0	1283.0	5482.0	1.0
184786	2022-05-14	Zimbabwe	249206.0	75.0	1307.0	5482.0	0.0

	country	continent	total_confirmed	total_deaths	total_recovered	active_cases	serious_or_critical	total_cases_per_1m_population	total_deaths_per_1m_population	total_tests	total_tests_per_1m_population	population
0	Afghanistan	Asia	179267	7690.0	162202.0	9375.0	1124.0	4420	190.0	951337.0	23455.0	40560636
1	Albania	Europe	275574	3497.0	271826.0	251.0	2.0	95954	1218.0	1817530.0	632857.0	2871945
2	Algeria	Africa	265816	6875.0	178371.0	80570.0	6.0	5865	152.0	230861.0	5093.0	45325517
3	Andorra	Europe	42156	153.0	41021.0	982.0	14.0	543983	1974.0	249838.0	3223924.0	77495
4	Angola	Africa	99194	1900.0	97149.0	145.0	NaN	2853	55.0	1499795.0	43136.0	34769277
...	...	...	...	...	...	...	...	...	...	...	...	...
221	Wallis And Futuna Islands	Australia/Oceania	454	7.0	438.0	9.0	NaN	41755	644.0	20508.0	1886140.0	10873
222	Western Sahara	Africa	10	1.0	9.0	0.0	NaN	16	2.0	NaN	NaN	624681
223	Yemen	Asia	11819	2149.0	9009.0	661.0	23.0	381	69.0	265253.0	8543.0	31049015
224	Zambia	Africa	320591	3983.0	315997.0	611.0	NaN	16575	206.0	3452554.0	178497.0	19342381
225	Zimbabwe	Africa	249206	5482.0	242417.0	1307.0	12.0	16324	359.0	2287793.0	149863.0	15265849

	date	country	cumulative_total_cases	daily_new_cases	active_cases	cumulative_total_deaths	daily_new_deaths	continent
0	2020-02-15	Afghanistan	0.0	NaN	0.0	0.0	NaN	Asia
1	2020-02-16	Afghanistan	0.0	NaN	0.0	0.0	NaN	Asia
2	2020-02-17	Afghanistan	0.0	NaN	0.0	0.0	NaN	Asia
3	2020-02-18	Afghanistan	0.0	NaN	0.0	0.0	NaN	Asia
4	2020-02-19	Afghanistan	0.0	NaN	0.0	0.0	NaN	Asia
...	...	...	...	...	...	...	...	...
184782	2022-05-10	Zimbabwe	248642.0	106.0	963.0	5481.0	2.0	Africa
184783	2022-05-11	Zimbabwe	248778.0	136.0	1039.0	5481.0	0.0	Africa
184784	2022-05-12	Zimbabwe	248943.0	165.0	1158.0	5481.0	0.0	Africa
184785	2022-05-13	Zimbabwe	249131.0	188.0	1283.0	5482.0	1.0	Africa
184786	2022-05-14	Zimbabwe	249206.0	75.0	1307.0	5482.0	0.0	Africa

Table of content:¶

1. Global Statistics ¶

2. Visualize Data by Country ¶

2.1. USA (The Leader) ¶

2.2. China (The Origin) ¶

2.3. UK (The Mutant) ¶

2.4. Italy (The Early Chaos) ¶

2.5. India (The Midway Chaos) ¶

2.6. Australia (The Latest Chaos) ¶

2.7. France ¶

3. Visualize Data by Continent ¶

3.1. Asia ¶

3.2. Europe ¶

3.3. Africa ¶

3.4. North America ¶

3.5. South America ¶

3.6. Australia/Oceania ¶

4. Most Affected Countries ¶

5. Current and History of Distribution of Active Cases ¶