Replace NaN's in an array in a specific way

Question

I have an array of the form (simplified here): [1,NaN,NaN,7,NaN,27]. I want to replace the NaN's with values equally spaced between the known values, so the above array would become [1,3,5,7,17,27]. Is there a fast way to do this (without using some for loops)? Thank you!

Answer 1

Pandas dataframe.interpolate() function is basically used to fill NA values in the dataframe or series

import pandas as pd
import numpy as np
arr = [ 1, np.NaN, np.NaN, 7, np.NaN, 27]
//converting array in series 
print(pd.Series(arr).interpolate(method = 'linear', limit_direction = 'forward'))

Parameters
method = 'linear': Ignore the index and treat the values as equally spaced.
limit_direction : {‘forward’, ‘backward’, ‘both’}, default ‘forward’ If limit is specified, consecutive NaNs will be filled in this direction.
limit : int, optional Maximum number of consecutive NaNs to fill. Must be greater than 0.

print(pd.Series(arr).interpolate(method = 'linear', limit_direction = 'forward', limit = 1))
#5 won't get printed
print(pd.Series(arr).interpolate(method = 'linear', limit_direction = 'backward', limit = 1))
#3 won't get printed

You can try different variations based on your requirement.

Answer 2

If possible use pandas create Series and then use Series.interpolate:

import pandas as pd
import numpy as np
arr = [1,np.NaN,np.NaN,7,np.NaN,27]
print (pd.Series(arr).interpolate().values)
[ 1. 3. 5. 7. 17. 27.]