Styjun

I have dataframe i.e.,

Input Dataframe

 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 Nan salem 
3 III A Eng 80 gphss Nan
4 III A Mat 45 Nan salem
5 III A Eng 40 gphss Nan
6 III A Eng 20 gphss salem
7 III A Mat 55 gphss Nan

I need to replace the "Nan" in "school" and "city" when a value in "class" and "section" column matches. The resultant outcome suppose to be,
Input Dataframe

 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

Can anyone help me out in this?

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

Assuming that each pair of class and section corresponds to a unique pair of school and city, we can use groupby:

# create a dictionary of class and section with school and city
# here we assume that for each pair and class there's a row with both school and city
# if that's not the case, we can separate the two series 
school_city_dict = df[['class', 'section','school','city']].dropna().
 groupby(['class', 'section'])[['school','city']].
 max().to_dict()
# school_city_dict = 'school': ('I', 'A'): 'jghss', ('III', 'A'): 'gphss',
# 'city': ('I', 'A'): 'salem', ('III', 'A'): 'salem'

# set index, prepare for map function
df.set_index(['class','section'], inplace=True)

df.loc[:,'school'] = df.index.map(school_city_dict['school'])
df.loc[:,'city'] = df.index.map(school_city_dict['city'])

# reset index to the original
df.reset_index()