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Rory Healy 2021-04-10 14:47:26 +10:00
parent fba77047d6
commit 8f7e856039
1 changed files with 38 additions and 7 deletions
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parta1.py
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@ -3,20 +3,51 @@ import argparse
all_covid_data = pd.read_csv('data/owid-covid-data.csv', encoding = 'ISO-8859-1')
reduced_data = all_covid_data.loc[:,['location', 'date', 'total_cases', 'new_cases', 'total_deaths', 'new_deaths']]
# reduced_data_grouped = reduced_data.groupby(['location', 'date'], as_index = False)
# filter out data past 2020
all_covid_data = all_covid_data[(all_covid_data['date'] >= '2020-01-01') & (all_covid_data['date'] <= '2020-12-31')]
all_covid_data.date = pd.to_datetime(all_covid_data.date)
# create groupby objects and sum new cases/deaths by month
new_cases = all_covid_data.loc[:, ['location', 'date', 'new_cases']]
new_cases.date = pd.to_datetime(new_cases.date)
new_cases_grouped = new_cases.groupby([new_cases.date.dt.month, new_cases.location]).new_cases.sum()
new_deaths = all_covid_data.loc[:, ['location', 'date', 'new_deaths']]
new_deaths.date = pd.to_datetime(new_deaths.date)
new_deaths_grouped = new_deaths.groupby([new_deaths.date.dt.month, new_deaths.location]).new_deaths.sum()
# convert multi-indexed series to dataframe
new_cases_grouped = new_cases_grouped.to_frame()
new_cases_grouped = pd.DataFrame(new_cases_grouped.to_records())
new_deaths_grouped = new_deaths_grouped.to_frame()
new_deaths_grouped = pd.DataFrame(new_deaths_grouped.to_records())
print(new_cases_grouped)
# sort by location, then date
new_cases_grouped.sort_values(by = ['location', 'date'], inplace = True)
new_deaths_grouped.sort_values(by = ['location', 'date'], inplace = True)
print("\n\n")
# rename columns
new_cases_grouped.rename(columns = {'date': 'month'}, inplace = True)
new_deaths_grouped.rename(columns = {'date': 'month'}, inplace = True)
print(new_deaths_grouped)
# merge new_deaths and new_cases
new_cases_grouped = new_cases_grouped.reindex(columns = ['location', 'month', 'new_cases'])
aggregated_data = new_cases_grouped.merge(new_deaths_grouped, how = 'outer', left_on = ['location', 'month'], right_on = ['location', 'month'])
# filter out all entries that aren't at the end of the month
all_covid_data['end_of_month'] = pd.to_datetime(all_covid_data['date']).dt.is_month_end
all_covid_data = all_covid_data.loc[all_covid_data.end_of_month, :]
# extract monthly total cases and total deaths
total_cases = all_covid_data.loc[:, ['location', 'date', 'total_cases']]
total_cases.date = total_cases.date.dt.month
total_cases.rename(columns = {'date': 'month'}, inplace = True)
total_deaths = all_covid_data.loc[:, ['location', 'date', 'total_deaths']]
total_deaths.date = total_deaths.date.dt.month
total_deaths.rename(columns = {'date': 'month'}, inplace = True)
# merge total_deaths and total_cases into aggregated_data
aggregated_data = aggregated_data.merge(total_cases, how = 'outer', left_on = ['location', 'month'], right_on = ['location', 'month'])
aggregated_data = aggregated_data.merge(total_deaths, how = 'outer', left_on = ['location', 'month'], right_on = ['location', 'month'])
aggregated_data = aggregated_data.reindex(columns = ['location', 'month', 'total_cases', 'new_cases', 'total_deaths', 'new_deaths'])
print(aggregated_data.head(25))