<jupyter_start><jupyter_text>Data Source: [European CDPC](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide) Lab 13: Analyzing COVID Data<jupyter_code>%matplotlib inline
import pandas<jupyter_output><empty_output><jupyter_text>Importing the Data<jupyter_code>df = pandas.read_excel("s3://ia241sp22sliman/covid_data.xls")
df[:10]<jupyter_output><empty_output><jupyter_text>Sum of Case Numbers per Month<jupyter_code>sum_death_by_date = df.groupby("month").sum()["cases"]
sum_death_by_date.plot()<jupyter_output><empty_output><jupyter_text>Top 10 Countries With the Highest Death Count<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>List of Unique Countries and Territories<jupyter_code>pandas.unique(df["countriesAndTerritories"])<jupyter_output><empty_output><jupyter_text>Select the US Data<jupyter_code>usa_data = df.loc[df["countriesAndTerritories"] == "United_States_of_America"]
usa_data[:10]<jupyter_output><empty_output><jupyter_text>How the Number of Cases is Related to the Number of Deaths in the USA<jupyter_code>usa_data.plot.scatter(x = "cases", y = "deaths", c = "month")<jupyter_output><empty_output>
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