Pandas Dataframe replace Nan from a row when a column value matches
Use forward and back filling missing values per groups with lambda function
in columns specified in list with DataFrame.groupby
- is necessary for each combination same values per groups:
cols = ['school','city']
df[cols] = df.groupby(['class','section'])[cols].apply(lambda x: x.ffill().bfill())
print (df)
class section sub marks school city
0 I A Eng 80 jghss salem
1 I A Mat 90 jghss salem
2 I A Eng 50 jghss salem
3 III A Eng 80 gphss salem
4 III A Mat 45 gphss salem
5 III A Eng 40 gphss salem
6 III A Eng 20 gphss salem
7 III A Mat 55 gphss salem