python - 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:

df = pd.DataFrame({'trial': [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], 
    'cs_name': [np.nan, 'A1', np.nan, np.nan, np.nan, np.nan, 'B2', 
                np.nan, 'A1', np.nan, np.nan, np.nan]})
Out[177]: 
   cs_name  trial
0      NaN      1
1       A1      1
2      NaN      1
3      NaN      1
4      NaN      2
5      NaN      2
6       B2      2
7      NaN      2
8       A1      3
9      NaN      3
10     NaN      3
11     NaN      3

I'm able to fill these values within the whole trial by using both bfill() and ffill(), but I'm wondering if there is a better way to achieve this.

df['cs_name'] = df.groupby('trial')['cs_name'].ffill()
df['cs_name'] = df.groupby('trial')['cs_name'].bfill()

Expected output:

   cs_name  trial
0       A1      1
1       A1      1
2       A1      1
3       A1      1
4       B2      2
5       B2      2
6       B2      2
7       B2      2
8       A1      3
9       A1      3
10      A1      3
11      A1      3

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!)...