scatter plots created

parta2.py

@@ -1,3 +1,50 @@
 import pandas as pd
 import argparse
 import matplotlib.pyplot as plt
+import numpy as np
+
+# parse input arguments
+parser = argparse.ArgumentParser()
+parser.add_argument('scatter_a', help = 'output path of figure 1')
+parser.add_argument('scatter_b', help = 'output path of figure 2')
+args = parser.parse_args()
+
+all_covid_data = pd.read_csv('data/owid-covid-data.csv', encoding = 'ISO-8859-1')
+
+# filter out data - only need 2020-12-31
+all_covid_data = all_covid_data[(all_covid_data['date'] == '2020-12-31')]
+all_covid_data.date = pd.to_datetime(all_covid_data.date)
+
+# extract total cases and deaths for 2020 per country
+total_cases = all_covid_data.loc[:, ['location', 'total_cases']]
+total_cases.set_index(['location'], inplace = True)
+total_deaths = all_covid_data.loc[:, ['location', 'total_deaths']]
+
+# merge total_cases and total_deaths
+aggregated_data = total_cases.merge(total_deaths, how = 'inner', on = 'location')
+
+# compute case fatality rate for each country
+aggregated_data['case_fatality_rate'] = (aggregated_data['total_deaths'] / aggregated_data['total_cases'])
+
+# format aggregated_data
+aggregated_data = aggregated_data.reindex(columns = ['location', 'case_fatality_rate', 'total_deaths', 'total_cases'])
+
+# extract case fatality rate from aggregated data
+case_fatality_rate = aggregated_data.loc[:, ['case_fatality_rate']]
+
+# plot case-fatality-rate against number of cases
+# this excludes some locations as they are outliers
+plt.scatter(total_cases, case_fatality_rate)
+plt.xlim(0, 3000000)
+plt.ylim(0, 0.075)
+plt.ylabel('Case fatality rate')
+plt.xlabel('Total confirmed cases in 2020')
+plt.grid(True)
+plt.savefig(args.scatter_a, dpi = 250.0)
+
+# plot case-fatality-rate against the log of the number of cases
+total_cases = total_cases.apply(np.log10, axis = 1)
+plt.scatter(total_cases, case_fatality_rate)
+plt.xlim(0, 9)
+plt.xlabel('Log of total confirmed cases in 2020')
+plt.savefig(args.scatter_b, dpi = 250.0)