python - pandas: Filling missing values within a group -


i have data experiment, , within each trial there single values, surrounded na's, want fill out 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 fill these values within whole trial using both bfill() , ffill(), i'm wondering if there better way 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

an alternative approach use first_valid_index , 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 more efficient using ffill followed bfill...

and use change cs_name column:

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

note: think nice enhancement have method grab first non-null object in pandas, in numpy it's an open request, don't think there method (i wrong!)...


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