How to replace NaNs by preceding values plus some constant , in pandas DataFrame?

Question

Pandas example data frame

    id   age   
0   1     2   
1   4     NaN 
2 NaN     NaN   
3   5     16
4   6     21
5   7     NaN

expected output

id   age   
0   1     2   
1   4     7 
2 NaN     11   
3   5     16
4   6     21
5   7     26

here if i want fill missing age , with previous age + 5 , how to do that ?

Answer 1

Is this what you need ?

df.age.ffill()+df.age.groupby(df.age.notnull().cumsum()).cumcount()*5
Out[539]: 
0     2.0
1     7.0
2    12.0
3    16.0
4    21.0
5    26.0
dtype: float64