下方调用:

>>>gr = df.groupby(['EVENT_ID', 'SELECTION_ID'], as_index=False)>>>res = gr.agg({'ODDS':[np.min, np.max]})>>>资源EVENT_ID SELECTION_ID ODDS阿明最大0 100429300 5297529 18 251 100429300 5297559 30 38

返回一个带有多索引列的框架.如果您不希望列成为多索引，您可以这样做:

>>>res.columns = list(map(''.join, res.columns.values))>>>资源EVENT_ID SELECTION_ID ODDSamin ODDSamax0 100429300 5297529 18 251 100429300 5297559 30 38

I have a dataframe:

pe_odds[ [ 'EVENT_ID', 'SELECTION_ID', 'ODDS' ] ]
Out[67]: 
     EVENT_ID  SELECTION_ID   ODDS
0   100429300       5297529  18.00
1   100429300       5297529  20.00
2   100429300       5297529  21.00
3   100429300       5297529  22.00
4   100429300       5297529  23.00
5   100429300       5297529  24.00
6   100429300       5297529  25.00

When I use groupby and agg, I get results with a multi-index:

pe_odds.groupby( [ 'EVENT_ID', 'SELECTION_ID' ] )[ 'ODDS' ].agg( [ np.min, np.max ] )
Out[68]: 
                         amin   amax
EVENT_ID  SELECTION_ID              
100428417 5490293        1.71   1.71
          5881623        1.14   1.35
          5922296        2.00   2.00
          5956692        2.00   2.02
100428419 603721         2.44   2.90
          4387436        4.30   6.20
          4398859        1.23   1.35
          4574687        1.35   1.46
          4881396       14.50  19.00
          6032606        2.94   4.20
          6065580        2.70   5.80
          6065582        2.42   3.65
100428421 5911426        2.22   2.52

I have tried using as_index to return the results without the multi_index:

pe_odds.groupby( [ 'EVENT_ID', 'SELECTION_ID' ], as_index=False )[ 'ODDS' ].agg( [ np.min, np.max ], as_index=False )

But it still gives me a multi-index.

I can use .reset_index(), but it is very slow:

pe_odds.groupby( [ 'EVENT_ID', 'SELECTION_ID' ] )[ 'ODDS' ].agg( [ np.min, np.max ] ).reset_index()

pe_odds.groupby( [ 'EVENT_ID', 'SELECTION_ID' ] )[ 'ODDS' ].agg( [ np.min, np.max ] ).reset_index()
Out[69]: 
     EVENT_ID  SELECTION_ID   amin   amax
0   100428417       5490293   1.71   1.71
1   100428417       5881623   1.14   1.35
2   100428417       5922296   2.00   2.00
3   100428417       5956692   2.00   2.02
4   100428419        603721   2.44   2.90
5   100428419       4387436   4.30   6.20

How can I return the results, without the Multi-index, using parameters of the groupby and/or agg function. And without having to resort to using reset_index() ?

Below call:

>>> gr = df.groupby(['EVENT_ID', 'SELECTION_ID'], as_index=False)
>>> res = gr.agg({'ODDS':[np.min, np.max]})
>>> res
    EVENT_ID SELECTION_ID ODDS     
                          amin amax
0  100429300      5297529   18   25
1  100429300      5297559   30   38

returns a frame with mulit-index columns. If you do not want columns to be multi-index either you may do:

>>> res.columns = list(map(''.join, res.columns.values))
>>> res
    EVENT_ID  SELECTION_ID  ODDSamin  ODDSamax
0  100429300       5297529        18        25
1  100429300       5297559        30        38