Replace values in a 3d numpy array

Question

There are those two numpy arrays:

a = np.array([
    [
        [1,2,3,0,0],
        [4,5,6,0,0],
        [7,8,9,0,0]
    ],
    [
        [1,3,5,0,0],
        [2,4,6,0,0],
        [1,1,1,0,0]
    ]
])

b = np.array([
    [
        [1,2],
        [2,3],
        [3,4]
    ],
    [
        [4,1],
        [5,2],
        [6,3]
    ]
])

with shapes:

"a" shape: (2, 3, 5), "b" shape: (2, 3, 2)

I want to replace the last two elements from array a with those from array b, e.g.

c = np.array([
    [
        [1,2,3,1,2],
        [4,5,6,2,3],
        [7,8,9,3,4]
    ],
    [
        [1,3,5,4,1],
        [2,4,6,5,2],
        [1,1,1,6,3]
    ]
])

However, np.hstack((a[:,:,:-2], b)) throws a Value Error:

all the input array dimensions except for the concatenation axis must match exactly

and in general doesn't look like it's the correct function to use. Append doesn't work either.

Is there a method in numpy that can do that or do I need to iterate over the arrays with a for loop and manipulate them manually?

Or simply use the ellipsis notation, `a[..., -2:] = b` – kmario23 May 08 '19 at 14:37 — kmario23, May 08 '19 at 14:37

score 3 · Answer 1 · answered May 08 '19 at 14:05

3

You could use the direct indices like so:

a[:, :, 3:] = b

answered May 08 '19 at 14:05

MuadDev

392
3
12

score 1 · Accepted Answer · answered May 08 '19 at 14:06

Non-overwriting method:

a[:,:,-2:] fetches the zeros at the end; use a[:,:,:3].
According to the documentation, np.hstack(x) is equivalent to np.concatenate(x, axis=1). Since you want to join the matrices on their innermost rows, you should use axis=2.

Code:

>>> np.concatenate((a[:,:,:3], b), axis=2)
array([[[1, 2, 3, 1, 2],
        [4, 5, 6, 2, 3],
        [7, 8, 9, 3, 4]],

       [[1, 3, 5, 4, 1],
        [2, 4, 6, 5, 2],
        [1, 1, 1, 6, 3]]])

ah it's the second axis!! I was trying with 0 and 1 :facepalm: — VnC, May 08 '19 at 14:07

Replace values in a 3d numpy array

2 Answers2