Closed joschw01 closed 1 year ago
df['date2']=df['date'].apply(lambda x: datetime.datetime.strptime(x, "%m-%d-%Y")) df = df.iloc[1:] df["month"]=df["date2"].apply(lambda x: x.strftime('%B')) df.set_index('date') df.drop(columns=['date2'])
that should work to fix the date hard code
for month, csv_date in months.items(): df = pd.read_csv('data/Merge/vaccinations-and-deaths-' + csv_date +'.csv', converters={'FIPS' : str}) df['Deaths_Per_1e5'] = df['Deaths'] / df['Census2019_18PlusPop'] * 1e5 df['Month'] = month file_list.append(df)
initial pull request made. need to still clean a bit and add more comments and doc strings
This looks to have been fixed. Thanks!
Requires removing the hardcoded dates, utilize seaborn to simplify the graphing functions, updating/improving the labels on the graph for both the axis and legend, and make sure all labels are suitable for the expanded data