applying several functions in transform in pandas

Question

After a groupby, when using agg, if a dict of columns:functions is passed, the functions will be applied in the corresponding columns.

Answer 1

I think now (pandas 0.20.2) function transform is not implemented with dict - columns names with functions like agg.

If functions return Series with same lenght:

df1 = df_test.set_index('a').groupby('a').agg({'b':np.cumsum,'c':np.cumprod}).reset_index()
print (df1)
   a     c   b
0  1     3   2
1  1    90  22
2  2    50  30
3  1  2970  24
4  2  2500  34

But if aggreagte different length need join:

df2 = df_test[['a']].join(df_test.groupby('a').agg({'b':my_fct1,'c':my_fct2}), on='a')
print (df2)
   a          c   b
0  1  16.522712   8
1  1  16.522712   8
2  2   0.000000  17
3  1  16.522712   8
4  2   0.000000  17

Answer 2

With the updates to Pandas, you can use the assign method, along with transform to either append new columns, or replace existing columns with new values :

grouper = df_test.groupby("a")

df_test.assign(b=grouper["b"].transform("cumsum"), 
               c=grouper["c"].transform("cumprod"))

    a   b   c
0   1   2   3
1   1   22  90
2   2   30  50
3   1   24  2970
4   2   34  2500

Answer 3

You can still use a dict but with a bit of hack:

df_test.groupby('a').transform(lambda x: {'b': x.cumsum(), 'c': x.cumprod()}[x.name])
Out[427]: 
    b     c
0   2     3
1  22    90
2  30    50
3  24  2970
4  34  2500

If you need to keep column a, you can do:

df_test.set_index('a')\
       .groupby('a')\
       .transform(lambda x: {'b': x.cumsum(), 'c': x.cumprod()}[x.name])\
       .reset_index()
Out[429]: 
   a   b     c
0  1   2     3
1  1  22    90
2  2  30    50
3  1  24  2970
4  2  34  2500

Another way is to use an if else to check column names:

df_test.set_index('a')\
       .groupby('a')\
       .transform(lambda x: x.cumsum() if x.name=='b' else x.cumprod())\
       .reset_index()