Pandas groupby applies to multiple columns

Question

I am trying to apply the same function to multiple columns of a groupby object, for example:

In [51]: df Out[51]: ab group 0 0.738628 0.242605 grp1 1 0.411315 0.340703 grp1 2 0.328785 0.780767 grp1 3 0.059992 0.853132 grp1 4 0.041380 0.368674 grp1 5 0.181592 0.632006 grp1 6 0.427660 0.292086 grp1 7 0.582361 0.239835 grp1 8 0.158401 0.328503 grp2 9 0.430513 0.540628 grp2 10 0.436652 0.085609 grp2 11 0.164037 0.381844 grp2 12 0.560781 0.098178 grp2 In [52]: df.groupby('group')['a'].apply(pd.rolling_mean, 2, min_periods = 2) Out[52]: 0 NaN 1 0.574971 2 0.370050 3 0.194389 4 0.050686 5 0.111486 6 0.304626 7 0.505011 8 NaN 9 0.294457 10 0.433582 11 0.300345 12 0.362409 dtype: float64 In [53]: 

However, if I try df.groupby('group')['a', 'b'].apply(pd.rolling_mean, 2, min_periods = 2) or df.groupby('group')[['a', 'b']].apply(pd.rolling_mean, 2, min_periods = 2) , both will give me ValueError: could not convert string to float: grp1 . What is the correct way to apply a function to multiple columns at the same time?

Answer 1

I think you are looking for transform - it applies a function to each group.

 >>> df.groupby('group').transform(pd.rolling_mean, 2, min_periods=2) ab 0 NaN NaN 1 0.574971 0.291654 2 0.370050 0.560735 3 0.194388 0.816950 4 0.050686 0.610903 5 0.111486 0.500340 6 0.304626 0.462046 7 0.505010 0.265961 8 NaN NaN 9 0.294457 0.434566 10 0.433582 0.313119 11 0.300344 0.233727 12 0.362409 0.240011