在熊猫中返回组/多重索引的前n个值

您可以使用SeriesGroupBy.nlargest：

print (grouped.groupby(level='yearmonth').nlargest(3).reset_index(level=0, drop=True))
yearmonth  product
201601     E          180
           A          100
           B           90
201602     F          220
           A          200
           C          120
201603     B          120
           C          110
           D          110
Name: val, dtype: int64

您也可以将to_datetimewith与to_period转换为year-month期间：

print (df)
        date product  Volume
0   20160101       A      10
1   20160101       B       5
2   20160101       C      10
3   20160101       D       5
4   20160102       A      20
5   20160102       A      10
6   20160102       B       5
7   20160102       C      10
8   20160102       D       5
9   20160328       A      20
10  20160328       C     100
11  20160328       B      20
12  20160328       D      20
13  20160330       D      20

grouped = df.groupby([pd.to_datetime(df.date, format='%Y%m%d').dt.to_period('M'),
                     'product'])['Volume'].sum()
print (grouped)
date     product
2016-01  A           40
         B           10
         C           20
         D           10
2016-03  A           20
         B           20
         C          100
         D           40
Name: Volume, dtype: int64

print (grouped.groupby(level='date').nlargest(3).reset_index(level=0, drop=True))
date     product
2016-01  A           40
         C           20
         B           10
2016-03  C          100
         D           40
         A           20
Name: Volume, dtype: int64