Pandas Want combine Statistics data using groupby

    data = {'ITEM': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'z', 'z', 'z', 'z'],
    'col1': [37, 84, 54, 72, 49, 20, 64, 75, 90, 71, 84, 46, 78, 24, 34],
    'col2': [27, 44, 43, 12, 92, 10, 24, 55, 50, 37, 44, 57, 38, 64, 17],
    'col3': [17, 54, 64, 68, 39, 50, 34, 65, 40, 72, 44, 56, 48, 94, 37], }

want = data.groupby('ITEM').agg({'col1' : ['median', 'std','rabbit'],
                                 'col2' : ['median', 'std','rabbit'],
                                 'col3' : ['median', 'std','rabbit']
                       })

I want to combine ‘col1’s’ average with ‘col1’s std times 4. And I call it as "rabbit"

and want to make new column [‘average’, ‘std’, ‘rabbit’]

>Solution :

Are you looking for:

>>> (df.groupby('ITEM', as_index=False)[['col1', 'col2', 'col3']]
       .agg(lambda x: x.mean() + 4 * x.std()))

  ITEM        col1        col2        col3
0    a  134.107944  163.886325  131.663437
1    b  169.413058  121.782536  111.677418
2    c  134.240115   80.269553  146.509668
3    d         NaN         NaN         NaN
4    z  139.336738  128.348483  157.778615

Or maybe:

>>> pd.concat([df,
         df.groupby('ITEM')[['col1', 'col2', 'col3']]
           .transform(lambda x: x.mean() + 4 * x.std()).add_prefix('rabbit_')],
              axis=1)

   ITEM  col1  col2  col3  rabbit_col1  rabbit_col2  rabbit_col3
0     a    37    27    17   134.107944   163.886325   131.663437
1     a    84    44    54   134.107944   163.886325   131.663437
2     a    54    43    64   134.107944   163.886325   131.663437
3     a    72    12    68   134.107944   163.886325   131.663437
4     a    49    92    39   134.107944   163.886325   131.663437
5     b    20    10    50   169.413058   121.782536   111.677418
6     b    64    24    34   169.413058   121.782536   111.677418
7     b    75    55    65   169.413058   121.782536   111.677418
8     c    90    50    40   134.240115    80.269553   146.509668
9     c    71    37    72   134.240115    80.269553   146.509668
10    d    84    44    44          NaN          NaN          NaN
11    z    46    57    56   139.336738   128.348483   157.778615
12    z    78    38    48   139.336738   128.348483   157.778615
13    z    24    64    94   139.336738   128.348483   157.778615
14    z    34    17    37   139.336738   128.348483   157.778615