python - How to fill in missing values in Pandas dataframe according to pattern in column?

Question

Suppose I have a dataframe with a column as follows:

Column
10
NaN
20
NaN
30

I want each row to be filled in with increments of 5 so that the final output would appear like:

Column
10
15
20
25
30

I've tried using np.arange and .reindex() but haven't had much luck. I'm looking for an iterative approach instead of simply manually filling in. Can anyone please help with this?

question from:https://stackoverflow.com/questions/65855938/how-to-fill-in-missing-values-in-pandas-dataframe-according-to-pattern-in-column

Answer 1

Try with interpolate

df['Column']=df.Column.interpolate()
Out[86]: 
0    10.0
1    15.0
2    20.0
3    25.0
4    30.0
Name: Column, dtype: float64