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

Here is a solution using simple python iterators. They are actually more efficient here than numpy.where, especially with big arrays! See comparison of similar code here.

import numpy as np

a = np.array([np.NAN, np.NAN, np.NAN, 1.44955726, 1.44628034, 1.44409573, 1.4408188, 1.43657094, 1.43171624,  1.42649744, 1.42200684, 1.42117704, 1.42040255, 1.41922908, np.NAN, np.NAN, np.NAN, np.NAN, np.NAN, np.NAN])

mask = np.isfinite(a)

# get first value in list
for i in range(len(mask)):
    if mask[i]:
        first = i
        break

# get last vaue in list
for i in range(len(mask)-1, -1, -1):
    if mask[i]:
        last = i
        break

# fill NaN with near known value on the edges
a = np.copy(a)
a[:first] = a[first]
a[last + 1:] = a[last]

print(a)

Output:

[1.44955726 1.44955726 1.44955726 1.44955726 1.44628034 1.44409573
 1.4408188  1.43657094 1.43171624 1.42649744 1.42200684 1.42117704
 1.42040255 1.41922908 1.41922908 1.41922908 1.41922908 1.41922908
 1.41922908 1.41922908]

It replaces only the first and last NaNs like requested here.