71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
import pandas as pd
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import argparse
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# parse input arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('path_to_csv', help = 'path to the csv file')
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args = parser.parse_args()
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all_covid_data = pd.read_csv('data/owid-covid-data.csv', \
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encoding = 'ISO-8859-1')
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# filter out data past 2020
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all_covid_data = all_covid_data[(all_covid_data['date'] >= '2020-01-01') \
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& (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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# create groupby objects and sum new cases/deaths by month
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new_cases = all_covid_data.loc[:, ['location', 'date', 'new_cases']]
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new_cases_grouped = new_cases.groupby([new_cases.date.dt.month, \
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new_cases.location]).new_cases.sum()
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new_deaths = all_covid_data.loc[:, ['location', 'date', 'new_deaths']]
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new_deaths_grouped = new_deaths.groupby([new_deaths.date.dt.month, \
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new_deaths.location]).new_deaths.sum()
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# convert multi-indexed series to dataframe
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new_cases_grouped = new_cases_grouped.to_frame()
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new_cases_grouped = pd.DataFrame(new_cases_grouped.to_records())
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new_deaths_grouped = new_deaths_grouped.to_frame()
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new_deaths_grouped = pd.DataFrame(new_deaths_grouped.to_records())
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# sort by location, then date
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new_cases_grouped.sort_values(by = ['location', 'date'], inplace = True)
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new_deaths_grouped.sort_values(by = ['location', 'date'], inplace = True)
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# merge new_deaths_grouped and new_cases_grouped
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aggregated_data = new_cases_grouped.merge(new_deaths_grouped, \
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how = 'outer', left_on = ['location', 'date'], \
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right_on = ['location', 'date'])
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# filter out all entries that aren't at the end of the month
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all_covid_data['end_of_month'] = \
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pd.to_datetime(all_covid_data['date']).dt.is_month_end
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all_covid_data = all_covid_data.loc[all_covid_data.end_of_month, :]
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# extract monthly total cases and total deaths
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total_cases = all_covid_data.loc[:, ['location', 'date', 'total_cases']]
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total_cases.date = total_cases.date.dt.month
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total_deaths = all_covid_data.loc[:, ['location', 'date', 'total_deaths']]
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total_deaths.date = total_deaths.date.dt.month
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# merge total_deaths and total_cases into aggregated_data
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aggregated_data = aggregated_data.merge(total_cases, how = 'outer', \
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left_on = ['location', 'date'], right_on = ['location', 'date'])
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aggregated_data = aggregated_data.merge(total_deaths, how = 'outer', \
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left_on = ['location', 'date'], right_on = ['location', 'date'])
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# compute case fatality rate for each month
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aggregated_data['case_fatality_rate'] = \
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(aggregated_data['new_deaths'] / aggregated_data['new_cases'])
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# format aggregated_data and output results
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aggregated_data = aggregated_data.reindex(columns = ['location', 'date', \
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'case_fatality_rate', 'total_cases', 'new_cases', 'total_deaths', \
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'new_deaths'])
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aggregated_data.rename(columns = {'date': 'month'}, inplace = True)
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aggregated_data.set_index(['location', 'month'], inplace = True)
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print(aggregated_data.head(5))
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aggregated_data.to_csv(args.path_to_csv)
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