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

NaNs have the interesting property of comparing different from themselves, thus we can quickly find the indexes of the non-nan elements:

idx = np.nonzero(a==a)[0]

it's now easy to replace the nans with the desired value:

for i in range(0, idx[0]):
    a[i]=a[idx[0]]
for i in range(idx[-1]+1, a.size)
    a[i]=a[idx[-1]]

Finally, we can put this in a function:

import numpy as np

def FixNaNs(arr):
    if len(arr.shape)>1:
        raise Exception("Only 1D arrays are supported.")
    idxs=np.nonzero(arr==arr)[0]

    if len(idxs)==0:
        return None

    ret=arr

    for i in range(0, idxs[0]):
        ret[i]=ret[idxs[0]]

    for i in range(idxs[-1]+1, ret.size):
        ret[i]=ret[idxs[-1]]

    return ret

edit

Ouch, coming from C++ I always forget about list ranges... @aix's solution is way more elegant and efficient than my C++ish loops, use that instead of mine.