使用Pandas到CSV，如何在多级索引中组织时间和数值数据

Question

Using pandas to write to a csv, I want Monthly Income sums for each unique Source. Month is in datetime format.

I have tried resampling and groupby methods, but groupby neglects month and resampling neglects source. I currently have a multi-level index with Month and Source as indexes.

   Month        Source   Income
2019-03-01        A        100
2019-03-05        B        50
2019-03-06        A        4
2019-03-22        C        60
2019-04-23        A        40
2019-04-24        A        100
2019-04-24        C        30
2019-06-1         C        100
2019-06-1         B        90
2019-06-8         B        20
2019-06-12        A        50
2019-06-27        C        50

I can groupby Source which neglects date, or I can resample for date which neglects source. I want monthly sums for each unique source.

Answer 1

What you have in the Month column is a Timestamp . So you can separate the month attribute of this Timestamp and afterward apply the groupby method, like this:

df.columns = ['Timestamp', 'Source', 'Income']

month_list = []
for i in range(len(df)):
    month_list.append(df.loc[i,'Timestamp'].month)

df['Month'] = month_list

df1 = df.groupby(['Month', 'Source']).sum()

The output should be like this:

               Income
Month   Source  
3          A    104
           B    50
           C    60
4          A    140
           C    30
6          A    50
           B    110
           C    150