Propagate/forward-fill nan values in numpy array
No inbuilt function in numpy to do this. Below simple code will generate desired result using numpy array only.
row,col = arr.shape
mask = np.isnan(arr)
for i in range(1,row):
for j in range(col):
if mask[i][j]:
arr[i][j] =arr[i-1][j]