Dataframe conditional cumulative sum-CodePudding

I am trying to sum a value column based on 2 categorical columns values.

dict1 = {'A': {0: 'A0', 1: 'A0', 2: 'A0', 3: 'A0', 4: 'A1', 5: 'A1', 6: 'A1', 7: 'A1', 8: 'A1', 9: 'A1'}, 'B': {0: 'B0', 1: 'B1', 2: 'B2', 3: 'B3', 4: 'B4', 5: 'B5', 6: 'B6', 7: 'B7', 8: 'B8', 9: 'B9'}, 'C': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}, 'D': {0: 10, 1: 11, 2: 12, 3: 13, 4: 14, 5: 15, 6: 16, 7: 17, 8: 18, 9: 19}, 'E': {0: 'E0', 1: 'E1', 2: 'E0', 3: 'E1', 4: 'E0', 5: 'E1', 6: 'E0', 7: 'E1', 8: 'E0', 9: 'E1'}}

df2 = pd.DataFrame(dict1)

I am now trying to get a cumsum of column D based on column A & E, i.e. A0E0, A0E1, A1E0 & so on should have a cumulative sum like this in column F.

    A   B   C   D   E   F
0   A0  B0  0   10  E0  10.0
1   A0  B1  1   11  E1  11.0
2   A0  B2  2   12  E0  22.0
3   A0  B3  3   13  E1  24.0
4   A1  B4  4   14  E0  14.0
5   A1  B5  5   15  E1  15.0
6   A1  B6  6   16  E0  30.0
7   A1  B7  7   17  E1  32.0
8   A1  B8  8   18  E0  48.0
9   A1  B9  9   19  E1  51.0

CodePudding user response：

df2['F'] = df2.groupby(['A', 'E'])['D'].cumsum()

df2

    A   B   C   D   E   F
0   A0  B0  0   10  E0  10
1   A0  B1  1   11  E1  11
2   A0  B2  2   12  E0  22
3   A0  B3  3   13  E1  24
4   A1  B4  4   14  E0  14
5   A1  B5  5   15  E1  15
6   A1  B6  6   16  E0  30
7   A1  B7  7   17  E1  32
8   A1  B8  8   18  E0  48
9   A1  B9  9   19  E1  51