I have a pandas DataFrame with a MultiIndex of columns:

columns=pd.MultiIndex.from_tuples([(c, i) for c in ['a', 'b'] for i in range(3)])
df = pd.DataFrame(np.random.randn(4, 6),index=[0, 0, 1, 1],columns=columns)
print(df)a                             b                    0         1         2         0         1         2
0  0.582804  0.753118 -0.900950 -0.914657 -0.333091 -0.965912
0  0.498002 -0.842624  0.155783  0.559730 -0.300136 -1.211412
1  0.727019  1.522160  1.679025  1.738350  0.593361  0.411907
1  1.253759 -0.806279 -2.177582 -0.099210 -0.839822 -0.211349

I want to group by the index, and use the 'min' aggregation on the a columns, and the 'sum' aggregation on the b columns.

I know I can do this by creating a dict that specifies the agg function for each column:

agg_dict = {'a': 'min', 'b': 'sum'}
full_agg_dict = {(c, i): agg_dict[c] for c in ['a', 'b'] for i in range(3)}
print(df.groupby(level=0).agg(full_agg_dict))a                             b                    0         1         2         0         1         2
0  0.498002 -0.842624 -0.900950 -0.354927 -0.633227 -2.177324
1  0.727019 -0.806279 -2.177582  1.639140 -0.246461  0.200558

Is there a simpler way? It seems like there should be a way to do this with agg_dict without using full_agg_dict.

I would use your approach as well. But here's another way that (should) work:

(df.stack(level=1).groupby(level=[0,1]).agg({'a':'min','b':'sum'}).unstack(-1)
)

For some reason groupby(level=[0,1] doesn't work for me, so I came up with:

(df.stack(level=1).reset_index().groupby(['level_0','level_1']).agg({'a':'min','b':'sum'}).unstack('level_1')
)