!python --version

## Import Libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from pandas.plotting import register_matplotlib_converters register_matplotlib_converters() #%matplotlib inline import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots plt.rcParams['figure.figsize'] = [15, 5] from IPython import display from ipywidgets import interact, widgets ## Read Data for Cases, Deaths and Recoveries ConfirmedCases_raw=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv') Deaths_raw=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')

### Melt the dateframe into the right shape and set index def cleandata(df_raw): df_cleaned=df_raw.melt(id_vars=['Province/State','Country/Region','Lat','Long'],value_name='Cases',var_name='Date') df_cleaned=df_cleaned.set_index(['Country/Region','Province/State','Date']) return df_cleaned # Clean all datasets ConfirmedCases=cleandata(ConfirmedCases_raw) Deaths=cleandata(Deaths_raw) #Recoveries=cleandata(Recoveries_raw)

### Get Countrywise Data def countrydata(df_cleaned,oldname,newname): df_country=df_cleaned.groupby(['Country/Region','Date'])['Cases'].sum().reset_index() df_country=df_country.set_index(['Country/Region','Date']) df_country.index=df_country.index.set_levels([df_country.index.levels[0], pd.to_datetime(df_country.index.levels[1])]) df_country=df_country.sort_values(['Country/Region','Date'],ascending=True) df_country=df_country.rename(columns={oldname:newname}) return df_country ConfirmedCasesCountry=countrydata(ConfirmedCases,'Cases','Total Confirmed Cases') DeathsCountry=countrydata(Deaths,'Cases','Total Deaths') #RecoveriesCountry=countrydata(Recoveries,'Cases','Total Recoveries') ### Get DailyData from Cumulative sum def dailydata(dfcountry,oldname,newname): dfcountrydaily=dfcountry.groupby(level=0).diff().fillna(0) dfcountrydaily=dfcountrydaily.rename(columns={oldname:newname}) return dfcountrydaily NewCasesCountry=dailydata(ConfirmedCasesCountry,'Total Confirmed Cases','Daily New Cases') NewDeathsCountry=dailydata(DeathsCountry,'Total Deaths','Daily New Deaths') #NewRecoveriesCountry=dailydata(RecoveriesCountry,'Total Recoveries','Daily New Recoveries') CountryConsolidated=pd.merge(ConfirmedCasesCountry,NewCasesCountry,how='left',left_index=True,right_index=True) CountryConsolidated=pd.merge(CountryConsolidated,NewDeathsCountry,how='left',left_index=True,right_index=True) CountryConsolidated=pd.merge(CountryConsolidated,DeathsCountry,how='left',left_index=True,right_index=True) #CountryConsolidated=pd.merge(CountryConsolidated,RecoveriesCountry,how='left',left_index=True,right_index=True) #CountryConsolidated=pd.merge(CountryConsolidated,NewRecoveriesCountry,how='left',left_index=True,right_index=True) #CountryConsolidated['Active Cases']=CountryConsolidated['Total Confirmed Cases']-CountryConsolidated['Total Deaths']-CountryConsolidated['Total Recoveries'] #CountryConsolidated['Share of Recoveries - Closed Cases']=np.round(CountryConsolidated['Total Recoveries']/(CountryConsolidated['Total Recoveries']+CountryConsolidated['Total Deaths']),2) CountryConsolidated['Death to Cases Ratio']=np.round(CountryConsolidated['Total Deaths']/CountryConsolidated['Total Confirmed Cases'],3)

## Get totals for all metrics GlobalTotals=CountryConsolidated.reset_index().groupby('Date').sum() #GlobalTotals['Share of Recoveries - Closed Cases']=np.round(GlobalTotals['Total Recoveries']/(GlobalTotals['Total Recoveries']+GlobalTotals['Total Deaths']),2) GlobalTotals['Death to Cases Ratio']=np.round(GlobalTotals['Total Deaths']/GlobalTotals['Total Confirmed Cases'],3) GlobalTotals.tail(2) # Create Plots that show Key Metrics For the Covid-19 chartcol='red' fig = make_subplots(rows=2, cols=2,shared_xaxes=True, specs=[[{}, {}],[{"colspan": 2}, None]], subplot_titles=('Total Confirmed Cases','Deaths','Death to Cases Ratio')) fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Total Confirmed Cases'], mode='lines+markers', name='Confirmed Cases', line=dict(color=chartcol,width=2)), row=1,col=1) fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Total Deaths'], mode='lines+markers', name='Deaths', line=dict(color=chartcol,width=2)), row=1,col=2) fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Death to Cases Ratio'], mode='lines+markers', line=dict(color=chartcol,width=2)), row=2,col=1) fig.update_layout(showlegend=False)

TotalCasesCountry=CountryConsolidated.max(level=0)['Total Confirmed Cases'].reset_index().set_index('Country/Region') TotalCasesCountry=TotalCasesCountry.sort_values(by='Total Confirmed Cases',ascending=False) TotalCasesCountryexclChina=TotalCasesCountry[~TotalCasesCountry.index.isin(['Others'])] Top10countriesbycasesexclChina=TotalCasesCountryexclChina.head(10) TotalCasesCountrytop10=TotalCasesCountry.head(10) fig = go.Figure(go.Bar(x=Top10countriesbycasesexclChina.index, y=Top10countriesbycasesexclChina['Total Confirmed Cases'], text=Top10countriesbycasesexclChina['Total Confirmed Cases'], textposition='outside')) fig.update_layout(title_text='Top 10 Countries by Total Confirmed Cases') fig.update_yaxes(showticklabels=False) fig.show()

ChinaFirstCase=CountryConsolidated.loc['China']['Total Confirmed Cases'].reset_index().set_index('Date') SpainFirstCase=CountryConsolidated.loc['Spain']['Total Confirmed Cases'].reset_index().set_index('Date') ItalyFirstCase=CountryConsolidated.loc['Italy']['Total Confirmed Cases'].reset_index().set_index('Date') SKFirstCase=CountryConsolidated.loc['Korea, South']['Total Confirmed Cases'].reset_index().set_index('Date') IranFirstCase=CountryConsolidated.loc['Iran']['Total Confirmed Cases'].reset_index().set_index('Date') GermanyFirstCase=CountryConsolidated.loc['Germany']['Total Confirmed Cases'].reset_index().set_index('Date') SingaporeFirstCase=CountryConsolidated.loc['Singapore']['Total Confirmed Cases'].reset_index().set_index('Date') USFirstCase=CountryConsolidated.loc['US']['Total Confirmed Cases'].reset_index().set_index('Date') CanadaFirstCase=CountryConsolidated.loc['Canada']['Total Confirmed Cases'].reset_index().set_index('Date') ChileFirstCase=CountryConsolidated.loc['Chile']['Total Confirmed Cases'].reset_index().set_index('Date') BrazilFirstCase=CountryConsolidated.loc['Brazil']['Total Confirmed Cases'].reset_index().set_index('Date') IndiaFirstCase=CountryConsolidated.loc['India']['Total Confirmed Cases'].reset_index().set_index('Date') ChinaGrowth=ChinaFirstCase[ChinaFirstCase.ne(0)].dropna().reset_index() SpainGrowth=SpainFirstCase[SpainFirstCase.ne(0)].dropna().reset_index() ItalyGrowth=ItalyFirstCase[ItalyFirstCase.ne(0)].dropna().reset_index() SKGrowth=SKFirstCase[SKFirstCase.ne(0)].dropna().reset_index() IranGrowth=IranFirstCase[IranFirstCase.ne(0)].dropna().reset_index() GermanyGrowth=GermanyFirstCase[GermanyFirstCase.ne(0)].dropna().reset_index() SingaporeGrowth=SingaporeFirstCase[SingaporeFirstCase.ne(0)].dropna().reset_index() USGrowth=USFirstCase[USFirstCase.ne(0)].dropna().reset_index() CanadaGrowth=CanadaFirstCase[CanadaFirstCase.ne(0)].dropna().reset_index() ChileGrowth=ChileFirstCase[ChileFirstCase.ne(0)].dropna().reset_index() BrazilGrowth=BrazilFirstCase[BrazilFirstCase.ne(0)].dropna().reset_index() IndiaGrowth=IndiaFirstCase[IndiaFirstCase.ne(0)].dropna().reset_index()

fig = make_subplots(rows=4, cols=2,shared_xaxes=True, subplot_titles=('US','Canada','Spain','Italy','Chile','Brazil','China','India',)) fig.update_xaxes(title_text="Number of Days since Outbreak", row=4, col=1) fig.update_xaxes(title_text="Number of Days since Outbreak", row=4, col=2) fig.add_trace(go.Scatter(x=USGrowth.index,y=USGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=1,col=1) fig.add_trace(go.Scatter(x=CanadaGrowth.index,y=CanadaGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=1,col=2) fig.add_trace(go.Scatter(x=SpainGrowth.index,y=SpainGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=2,col=1) fig.add_trace(go.Scatter(x=ItalyGrowth.index,y=ItalyGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=2,col=2) fig.add_trace(go.Scatter(x=ChileGrowth.index,y=ChileGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=3,col=1) fig.add_trace(go.Scatter(x=BrazilGrowth.index,y=BrazilGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=3,col=2) fig.add_trace(go.Scatter(x=ChinaGrowth.index,y=ChinaGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=4,col=1) fig.add_trace(go.Scatter(x=IndiaGrowth.index,y=IndiaGrowth['Total Confirmed Cases'], mode='lines+markers', name='Active Cases', line=dict(color=chartcol,width=2)), row=4,col=2) fig.update_layout(showlegend=False)

def plotcountry(Country): fig = make_subplots(rows=3, cols=2,shared_xaxes=True, specs=[[{}, {}],[{"colspan": 2}, None],[{"colspan": 2}, None]], subplot_titles=('Total Confirmed Cases','Deaths','Death to Cases Ratio', 'Daily New Cases')) fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Total Confirmed Cases'], mode='lines+markers', name='Confirmed Cases', line=dict(color=chartcol,width=2)), row=1,col=1) fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Total Deaths'], mode='lines+markers', name='Total Deaths', line=dict(color=chartcol,width=2)), row=1,col=2) fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Death to Cases Ratio'], mode='lines+markers', name='Death to Cases Ratio', line=dict(color=chartcol,width=2)), row=2,col=1) CountryNewCases = CountryConsolidated.loc[Country]['Daily New Cases'].reset_index().set_index('Date') fig.add_trace(go.Bar(x=CountryNewCases.index,y=CountryNewCases['Daily New Cases'], name='Daily New Cases', ), row=3,col=1) fig.update_layout(showlegend=False, title_text="Data for %s" % Country) return fig plotcountry('US')

plotcountry('Sweden')

plotcountry('United Kingdom')

# A list with countries of interest countries = ['US','Canada', 'Spain', 'Italy', 'Korea, South', 'Sweden', 'Denmark', 'Chile', 'Iran', 'India', 'United Kingdom', 'Japan'] rowsTot = int(len(countries)/2) fig = make_subplots(rows=rowsTot, cols=2,shared_xaxes=True, subplot_titles=(countries)) fig.update_xaxes(title_text="Daily New Cases", row=rowsTot, col=1) fig.update_xaxes(title_text="Daily New Cases", row=rowsTot, col=2) # Where we start rowNum=1 colNum=1 for country in countries: CountryNewCases = CountryConsolidated.loc[country]['Daily New Cases'].reset_index().set_index('Date') fig.add_trace(go.Bar(x=CountryNewCases.index,y=CountryNewCases['Daily New Cases'], name='Daily New Cases', ), row=rowNum,col=colNum) # Col numbers flips between 1 and 2, Rows increase by one if colNum % 2 == 1: colNum += 1 else: colNum = 1 rowNum += 1 fig.update_layout(showlegend=False)

# A list with countries of interest countries = ['US','Canada', 'Spain', 'Italy', 'Korea, South', 'Sweden', 'Denmark', 'Chile', 'Iran', 'India', 'United Kingdom'] # Initialize figTrend = go.Figure() for country in countries: CountryNewCases = CountryConsolidated.loc[country]['Daily New Cases'].reset_index().set_index('Date') CountryConfirmedCases = CountryConsolidated.loc[country,'Total Confirmed Cases'] figTrend.add_trace(go.Scatter(x=CountryConfirmedCases, y=CountryNewCases['Daily New Cases'], mode='markers', marker=dict(size=3), name=country)) figTrend.update_layout(xaxis_type='log', yaxis_type='log') figTrend.update_xaxes(title_text="Confirmed Cases") figTrend.update_yaxes(title_text="New Cases") figTrend.show()