<jupyter_start><jupyter_text>Visualize Covid19 Data in Python  data source The data is from the [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide). ![covid image](https://leecountync.gov/portals/0/Content/Departments/uploads/covid-19_1.jpg)<jupyter_code>%matplotlib inline
import pandas<jupyter_output><empty_output><jupyter_text>a quick view of the data<jupyter_code>df = pandas.read_excel('s3://ia241-spring2022-lipold/covid_data.xls')
df[:10]<jupyter_output><empty_output><jupyter_text>trend of the number of cases<jupyter_code>sum_cases_per_day=df.groupby('dateRep').sum()['cases']
sum_cases_per_day.plot()<jupyter_output><empty_output><jupyter_text>top 10 countries with the highest deaths<jupyter_code>sum_death_per_country=df.groupby('countriesAndTerritories').sum()['deaths']
sum_death_per_country.nlargest(10).plot.bar()<jupyter_output><empty_output><jupyter_text>list of all countries<jupyter_code>pandas.unique(df['countriesAndTerritories'])<jupyter_output><empty_output><jupyter_text>The USA Data<jupyter_code>usa_data = df.loc[ df ['countriesAndTerritories']=='United_States_of_America' ]
usa_data[:10]<jupyter_output><empty_output><jupyter_text>how number of deaths is related to number of cases in the USA<jupyter_code>usa_data.plot.scatter(x='cases',y='deaths',c='month')<jupyter_output><empty_output>
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