关于这个问题Grouping Pandas dataframe across rows,op是:
amount
clients
Comp1 16.360417
Comp2 69.697501
Comp3 85.700000
Comp4 36.666667
Comp5 44.156500
如果在输入中添加了日期列:
tdate,client1,client2,client3,client4,client5,client6,amount
12/31/2017,,,Comp1,,,4.475000
12/31/2017,,,Comp2,,,16.305584
10/31/2107,,,Comp3,,,4.050000
10/31/2017,Comp2,Comp1,,Comp4,,,21.000000
1/1/2017,,,Comp4,,,30.000000
2/2/2017,Comp1,,Comp2,,,5.137500
10/31/2017,,,Comp3,,,52.650000
12/31/2017,,,Comp1,,,2.650000
10/31/2017,Comp3,,,Comp3,,,29.000000
12/31/2017,Comp5,,,Comp2,,,20.809000
1/1/2017,Comp5,,,Comp2,,,15.100000
10/31/2017,Comp5,,,Comp2,,,52.404000
我们如何得到这个输出:
12/31/2017 Comp1 4.475+2.65
12/31/2017 Comp2 16.305584+20.809/2
10/31/2017 Comp2 21/3+5.1375/2+52.404/2
1/1/2017 Comp2 15.1/2
10/31/2017 Comp3 4.05+52.65+29
1/1/2017 Comp4 30
10/21/2017 Comp4 21/3
12/31/2017 Comp5 20.809/2
1/1/2017 Comp5 15.1/2
10/31/2017 Comp5 52.404/2
与前面的答案相比,我们需要通过设置两列作为索引来使用堆栈
输出:
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