pandas: Filling missing values within a group

Question

I have some data from an experiment, and within each trial there are some single values, surrounded by NA\'s, that I want to fill out to the entire trial:

Answer 1

An alternative approach is to use first_valid_index and a transform:

In [11]: g = df.groupby('trial')

In [12]: g['cs_name'].transform(lambda s: s.loc[s.first_valid_index()])
Out[12]: 
0     A1
1     A1
2     A1
3     A1
4     B2
5     B2
6     B2
7     B2
8     A1
9     A1
10    A1
11    A1
Name: cs_name, dtype: object

This ought to be more efficient then using ffill followed by a bfill...

And use this to change the cs_name column:

df['cs_name'] = g['cs_name'].transform(lambda s: s.loc[s.first_valid_index()])

Note: I think it would be nice enhancement to have a method to grab the first non-null object in the pandas, in numpy it's an open request, I don't think there is currently a method (I could be wrong!)...