Question

I have a NumPy array a like the following:

>>> str(a)
'[        nan         nan         nan  1.44955726  1.44628034  1.44409573\n  1.4408188   1.43657094  1.43171624  1.42649744  1.42200684  1.42117704\n  1.42040255  1.41922908         nan         nan         nan         nan\n         nan         nan]'

I want to replace each NaN with the closest non-NaN value, so that all of the NaN’s at the beginning get set to 1.449... and all of the NaN’s at the end get set to 1.419....

I can see how to do this for specific cases like this, but I need to be able to do it generally for any length of array, with any length of NaN’s at the beginning and end of the array (there will be no NaN’s in the middle of the numbers). Any ideas?

I can find the NaN’s easily enough with np.isnan(), but I can’t work out how to get the closest value to each NaN.

Answer 1

I want to replace each NaN with the closest non-NaN value… there will be no NaN’s in the middle of the numbers

The following will do it:

ind = np.where(~np.isnan(a))[0]
first, last = ind[0], ind[-1]
a[:first] = a[first]
a[last + 1:] = a[last]

This is a straight numpy solution requiring no Python loops, no recursion, no list comprehensions etc.