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