!python --version
Python 3.7.9
## 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()