pandas Groupby累积总和 [英] Pandas groupby cumulative sum

问题描述

我想在我的Pandas数据框中添加一个累加的总和列，以便:

I would like to add a cumulative sum column to my Pandas dataframe so that:

name | day       | no
-----|-----------|----
Jack | Monday    | 10
Jack | Tuesday   | 20
Jack | Tuesday   | 10
Jack | Wednesday | 50
Jill | Monday    | 40
Jill | Wednesday | 110

成为:

Jack | Monday     | 10  | 10
Jack | Tuesday    | 30  | 40
Jack | Wednesday  | 50  | 90
Jill | Monday     | 40  | 40
Jill | Wednesday  | 110 | 150

我尝试了df.groupby和df.agg(lambda x: cumsum(x))的各种组合都无济于事.

I tried various combos of df.groupby and df.agg(lambda x: cumsum(x)) to no avail.

推荐答案

这应该做到，需要两次groupby():

This should do it, need groupby() twice:

df.groupby(['name', 'day']).sum() \
  .groupby(level=0).cumsum().reset_index()

说明:

print(df)
   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   20
2  Jack    Tuesday   10
3  Jack  Wednesday   50
4  Jill     Monday   40
5  Jill  Wednesday  110

# sum per name/day
print( df.groupby(['name', 'day']).sum() )
                 no
name day           
Jack Monday      10
     Tuesday     30
     Wednesday   50
Jill Monday      40
      Wednesday  110

# cumulative sum per name/day
print( df.groupby(['name', 'day']).sum() \
         .groupby(level=0).cumsum() )
                 no
name day           
Jack Monday      10
     Tuesday     40
     Wednesday   90
Jill Monday      40
     Wednesday  150

由第一个和得出的数据帧由'name'和'day'索引.您可以通过打印

The dataframe resulting from the first sum is indexed by 'name' and by 'day'. You can see it by printing

df.groupby(['name', 'day']).sum().index 

在计算累积总和时，您要通过'name'进行计算，该值对应于第一个索引(级别0).

When computing the cumulative sum, you want to do so by 'name', corresponding to the first index (level 0).

最后，使用reset_index重复命名.

df.groupby(['name', 'day']).sum().groupby(level=0).cumsum().reset_index()

   name        day   no
0  Jack     Monday   10
1  Jack    Tuesday   40
2  Jack  Wednesday   90
3  Jill     Monday   40
4  Jill  Wednesday  150

这篇关于 pandas Groupby累积总和的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！