Zero Pad Ndarray Along Axis

I want to pad: [[1, 2] [3, 4]] to [[0, 0, 0] [0, 1, 2] [0, 3, 4]] I have no problem in doing so when input is 2D by vstack & hsrack However, when I add 1 dim to represent

Solution 1:

You can use np.pad, which allows you to specify the number of values padded to the edges of each axis.

So for the first example 2D array you could do:

a = np.array([[1, 2],[3, 4]])
np.pad(a,((1, 0), (1, 0)), mode = 'constant')

array([[0, 0, 0],
       [0, 1, 2],
       [0, 3, 4]])

So here each tuple is representing the side which to pad with zeros along each axis, i.e. ((before_1, after_1), … (before_N, after_N)).

---

And for a 3D array the same applies, but in this case we must specify that we only want to zero pad the two last dimensions:

img = np.random.randint(-10, 10, size=(2, 2, 2))
np.pad(img, ((0,0), (1,0), (1,0)), 'constant')

array([[[ 0,  0,  0],
        [ 0, -3, -2],
        [ 0,  9, -5]],

       [[ 0,  0,  0],
        [ 0,  1, -9],
        [ 0, -1, -3]]])

Solution 2:

You can use np.pad and remove the last row/column:

import numpy as np

a = np.array([[1, 2], [3, 4]])
result = np.pad(a, 1, mode='constant', constant_values=0)[:-1, :-1]
print(result)

Output:

[[0 0 0]
 [0 1 2]
 [0 3 4]]