<jupyter_start><jupyter_text>A simple example of analyzing COVID-19 Data in Python  This data is downloaded from the European Center for Disease Prevention snd Control.<jupyter_code>%matplotlib inline
import pandas<jupyter_output><empty_output><jupyter_text>Import Data from S3 market<jupyter_code>df = pandas.read_excel('s3://ia241-spring2022-bell/covid_data.xls')
df[:10] #the top 10 rows<jupyter_output><empty_output><jupyter_text>The Sum of Case Numbers per Month<jupyter_code>sum_cases_by_date = df.groupby('month').sum()['cases']

sum_cases_by_date.plot()<jupyter_output><empty_output><jupyter_text>The Top 10 Countries with the Highest Deaths<jupyter_code>sum_death_by_country = df.groupby('countriesAndTerritories').sum()['deaths']
sum_death_by_country.nlargest(10).plot.bar()<jupyter_output><empty_output><jupyter_text>The List of the Unique Countries and Territories<jupyter_code>pandas.unique(df['countriesAndTerritories'])<jupyter_output><empty_output><jupyter_text>Select the USA Data<jupyter_code>usa_data = df.loc[df['countriesAndTerritories'] == 'United_States_of_America']
usa_data[:10] #the top 10 rows<jupyter_output><empty_output><jupyter_text>How the Number of Cases is Related to the Number of Deaths<jupyter_code>usa_data.plot.scatter(x='cases',y='deaths', c='month')<jupyter_output><empty_output>
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