假设我有以下熊猫数据框：
Date Region Country Cases Deaths Lat Long
2020-03-08 Northern Territory Australia 27 49 -12.4634 130.8456
2020-03-09 Northern Territory Australia 80 85 -12.4634 130.8456
2020-03-12 Northern Territory Australia 35 73 -12.4634 130.8456
2020-03-08 Western Australia Australia 48 20 -31.9505 115.8605
2020-03-09 Western Australia Australia 70 12 -31.9505 115.8605
2020-03-10 Western Australia Australia 66 95 -31.9505 115.8605
2020-03-11 Western Australia Australia 31 38 -31.9505 115.8605
2020-03-12 Western Australia Australia 40 83 -31.9505 115.8605
我需要更新缺少北领地2020-3-10和2020-3-11的数据框。但是，我想使用除案件和死亡之外的所有信息。像这样：
Date Region Country Cases Deaths Lat Long
2020-03-08 Northern Territory Australia 27 49 -12.4634 130.8456
2020-03-09 Northern Territory Australia 80 85 -12.4634 130.8456
2020-03-10 Northern Territory Australia 0 0 -12.4634 130.8456
2020-03-11 Northern Territory Australia 0 0 -12.4634 130.8456
2020-03-12 Northern Territory Australia 35 73 -12.4634 130.8456
2020-03-08 Western Australia Australia 48 20 -31.9505 115.8605
2020-03-09 Western Australia Australia 70 12 -31.9505 115.8605
2020-03-10 Western Australia Australia 66 95 -31.9505 115.8605
2020-03-11 Western Australia Australia 31 38 -31.9505 115.8605
2020-03-12 Western Australia Australia 40 83 -31.9505 115.8605
我能想到的唯一方法是遍历日期和国家/地区的所有组合。
dates = my_df['Date'].unique()
countries = my_df['Country']
regions = my_df['Region']
##yuck
for d in dates:
for r in regions:
for c in countries:
if my_df[(my_df['country']== c) & (my_df['Date']==d) & (my_df['region']==c)].empty:
my_df.append({'Date':'d, 'Country':c,'Region':r,'cases':0,'deaths':0},ignore_index=True)
