我可以在 python 中对不同时间的值求和吗？

Question

我有一个看起来像这样的数据框

WEEK	DELIVERY_BOY_ID	TOTAL_GMV
2022-04-04	999999999.0	470510.11
2022-04-11	999999999.0	557351.02
2022-04-18	999999999.0	454225.78
2022-04-25	999999999.0	527932.46
2022-05-02	999999999.0	556741.18
2022-05-09	999999999.0	524571.93
2022-05-16	999999999.0	547195.66
2022-05-23	999999999.0	112423.49

我想做的是每 4 周对 TOTAL_GMV 求和（从 2022-05-02 到 2022-05-23 周的总和，从 2022-04-25 到 2022-每周 05-16 等等），并向我展示最后一周日期的结果

所以，最终结果应该是这样的：

WEEK	DELIVERY_BOY_ID	TOTAL_GMV	EXPLANATION
2022-04-04	999999999.0	*********	Sum from 2022-03-14 to 2022-04-04
2022-04-11	999999999.0	*********	Sum from 2022-03-21 to 2022-04-11
2022-04-18	999999999.0	*********	Sum from 2022-03-28 to 2022-04-18
2022-04-25	999999999.0	2.010.018,91	Sum from 2022-04-04 to 2022-04-25
2022-05-02	999999999.0	2.096.250,44	Sum from 2022-04-11 to 2022-05-02
2022-05-09	999999999.0	2.063.469,15	Sum from 2022-04-18 to 2022-05-09
2022-05-16	999999999.0	2.156.441,23	Sum from 2022-04-25 to 2022-05-16
2022-05-23	999999999.0	1.639.932,26	Sum from 2022-05-02 to 2022-05-23

知道怎么做吗？

谢谢！！

Answer 1

假设WEEK是索引，你可以这样做：

>>> df.TOTAL_GMV.rolling(4).sum()
WEEK
2022-04-04           NaN
2022-04-11           NaN
2022-04-18           NaN
2022-04-25    2010019.37
2022-05-02    2096250.44
2022-05-09    2063471.35
2022-05-16    2156441.23
2022-05-23    1740932.26
Name: TOTAL_GMV, dtype: float64

要将其添加到 df，

df['TOTAL_GMV'] = df.TOTAL_GMV.rolling(4).sum()

（如果不是索引，请将其更改为 df.set_index('WEEK').TOTAL_GMV.rolling(4).sum()。）

我可以在 python 中对不同时间的值求和吗？

Can I sum a value over different times in python?

python

group-by

date

dataframe

pandas