Most efficient way to forward-fill NaN values in numpy array

Question

Example Problem

As a simple example, consider the numpy array arr as defined below:

import numpy as np
arr = np.array([[5, np.nan, np.         


        

Answer 1

Here's one approach -

mask = np.isnan(arr)
idx = np.where(~mask,np.arange(mask.shape[1]),0)
np.maximum.accumulate(idx,axis=1, out=idx)
out = arr[np.arange(idx.shape[0])[:,None], idx]

If you don't want to create another array and just fill the NaNs in arr itself, replace the last step with this -

arr[mask] = arr[np.nonzero(mask)[0], idx[mask]]

Sample input, output -

In [179]: arr
Out[179]: 
array([[  5.,  nan,  nan,   7.,   2.,   6.,   5.],
       [  3.,  nan,   1.,   8.,  nan,   5.,  nan],
       [  4.,   9.,   6.,  nan,  nan,  nan,   7.]])

In [180]: out
Out[180]: 
array([[ 5.,  5.,  5.,  7.,  2.,  6.,  5.],
       [ 3.,  3.,  1.,  8.,  8.,  5.,  5.],
       [ 4.,  9.,  6.,  6.,  6.,  6.,  7.]])