问题
In R data.table it is possible and easy to aggregate on multiple columns using argmin or argmax functions in one aggregate. For example for DT:
> DT = data.table(id=c(1,1,1,2,2,2,2,3,3,3), col1=c(1,3,5,2,5,3,6,3,67,7), col2=c(4,6,8,3,65,3,5,4,4,7), col3=c(34,64,53,5,6,2,4,6,4,67))
> DT
id col1 col2 col3
1: 1 1 4 34
2: 1 3 6 64
3: 1 5 8 53
4: 2 2 3 5
5: 2 5 65 6
6: 2 3 3 2
7: 2 6 5 4
8: 3 3 4 6
9: 3 67 4 4
10: 3 7 7 67
> DT_agg = DT[, .(agg1 = col1[which.max(col2)]
, agg2 = col2[which.min(col3)]
, agg3 = col1[which.max(col3)])
, by= id]
> DT_agg
id agg1 agg2 agg3
1: 1 5 4 3
2: 2 5 3 5
3: 3 7 4 7
agg1 is value of col1 where value of col2 is maximum, grouped by id.
agg2 is value of col2 where value of col3 is minimum, grouped by id.
agg3 is value of col1 where value of col3 is maximum, grouped by id.
how is this possible in Pandas, doing all three aggregates in one aggregate operation using groupby and agg? I can't figure out how to incorporate three different indexing in one agg function in Python. here's the dataframe in Python:
DF =pd.DataFrame({'id':[1,1,1,2,2,2,2,3,3,3], 'col1':[1,3,5,2,5,3,6,3,67,7], 'col2':[4,6,8,3,65,3,5,4,4,7], 'col3':[34,64,53,5,6,2,4,6,4,67]})
DF
Out[70]:
id col1 col2 col3
0 1 1 4 34
1 1 3 6 64
2 1 5 8 53
3 2 2 3 5
4 2 5 65 6
5 2 3 3 2
6 2 6 5 4
7 3 3 4 6
8 3 67 4 4
9 3 7 7 67
回答1:
You can try this,
DF.groupby('id').agg(agg1=('col1',lambda x:x[DF.loc[x.index,'col2'].idxmax()]),
agg2 = ('col2',lambda x:x[DF.loc[x.index,'col3'].idxmin()]),
agg3 = ('col1',lambda x:x[DF.loc[x.index,'col3'].idxmax()]))
agg1 agg2 agg3
id
1 5 4 3
2 5 3 5
3 7 4 7
回答2:
Toyed with this question, primarily to see if I could get an improved speed on the original solution. Anonymous functions have a way of eating into the speed.
grp = df.groupby("id")
pd.DataFrame({ "col1": df.col1[grp.col2.idxmax()].array,
"col2": df.col2[grp.col3.idxmin()].array,
"col3": df.col1[grp.col3.idxmax()].array},
index=grp.indices)
col1 col2 col3
1 5 4 3
2 5 3 5
3 7 4 7
Speedup ~3x. Your mileage may vary. Also, for brevity, no, I think the original solution is probably the best in brevity. rdatatable excels at being concise and quite fast.
来源：https://stackoverflow.com/questions/62295617/multiple-indexing-with-multiple-idxmin-and-idmax-in-one-aggregate-in-pandas