You can simply use this line of code.
cols = ["workclass", "native-country"]
df[cols]=df[cols].fillna(df.mode().iloc[0])
or instead of fillna(df.mode().iloc[0]), you can use fillna(mode.iloc[0])
Example:
import pandas as pn
df={
'P3': [7,9,9,9,3],
'P2': [8,8,9],
'P1': [8,9,9],
}
df=pn.DataFrame.from_dict(d,orient='index').transpose()
Then df is
P3 P2 P1
0 7 8 8
1 9 8 9
2 9 9 9
3 9 NaN NaN
4 3 NaN NaN
After this,
l=df.filter(["P1", "P2"]).mode()
df[["P1", "P2"]]=df[["P1", "P2"]].fillna(value=l.iloc[0])
we get that df is
P3 P2 P1
0 7 8 8
1 9 8 9
2 9 9 9
3 9 8 9
4 3 8 9
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