Replace NaN's in NumPy array with closest non-NaN value

Question

I have a NumPy array a like the following:

>>> str(a)
\'[        nan         nan         nan  1.44955726  1.44628034  1.44409573\\n  1.4408         


        

Answer 1

I came across the problem and had to find a custom solution for scattered NaNs. The function below replaces any NaN by the first number occurrence to the right, if none exists, it replaces it by the first number occurrence to the left. Further manipulation can be done to replace it with the mean of boundary occurrences.

import numpy as np

Data = np.array([np.nan,1.3,np.nan,1.4,np.nan,np.nan])

nansIndx = np.where(np.isnan(Data))[0]
isanIndx = np.where(~np.isnan(Data))[0]
for nan in nansIndx:
    replacementCandidates = np.where(isanIndx>nan)[0]
    if replacementCandidates.size != 0:
        replacement = Data[isanIndx[replacementCandidates[0]]]
    else:
        replacement = Data[isanIndx[np.where(isanIndx

Result is:

>>> Data
array([ 1.3,  1.3,  1.4,  1.4,  1.4,  1.4])