Pandas - 用另一列的字符串填充 NaN

Question

I have 2 columns ('Surname' and 'PostCode'). The dataframe is already filtered to include only duplicated surnames:

Surname | PostCode
Adams   | NaN
Adams   | NaN
Bryan   | NX203
Bryan   | NaN
Cormack | NaN
Cormack | NaN
Cormack | NZ233
Dylan   | NaN
Dylan   | NaN
Dylan   | NaN

Some of them do not have post codes at all. For those that have, however, I'd like to fill in the missing ones with whatever there is. For example the second row containing 'Bryan' should be filled with NX203 (just like the row above). Similarly, the other two instances of Cormack should be filled with NZ233.

I have no idea where to start. I assume it'd have to be a python function applied to each row but not sure how to start/what to do.

Answer 1

Let's try groupby().transform() :

df['PostCode'] = df.groupby('Surname').PostCode.transform('first')

Output:

   Surname PostCode
0    Adams      NaN
1    Adams      NaN
2    Bryan    NX203
3    Bryan    NX203
4  Cormack    NZ233
5  Cormack    NZ233
6  Cormack    NZ233
7    Dylan      NaN
8    Dylan      NaN
9    Dylan      NaN

Answer 2

Another way, groupby(), ffill and then bfill

df['PostCode'] =df.groupby('Surname').PostCode.apply(lambda x:x.ffill().bfill())



 Surname PostCode
0    Adams      NaN
1    Adams      NaN
2    Bryan    NX203
3    Bryan    NX203
4  Cormack    NZ233
5  Cormack    NZ233
6  Cormack    NZ233
7    Dylan      NaN
8    Dylan      NaN
9    Dylan      NaN