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?

a[:, :, -2:] = b

user3483203
– user3483203

2019-05-08 14:03:44 +00:00
Commented May 8, 2019 at 14:03 — user3483203
– user3483203, Commented May 8, 2019 at 14:03
Or simply use the ellipsis notation, a[..., -2:] = b

kmario23
– kmario23

2019-05-08 14:37:48 +00:00
Commented May 8, 2019 at 14:37 — kmario23
– kmario23, Commented May 8, 2019 at 14:37

MuadDev · Accepted Answer · 2019-05-08 14:05:03Z

3

You could use the direct indices like so:

a[:, :, 3:] = b

answered May 8, 2019 at 14:05

MuadDev

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Comments

meowgoesthedog · Accepted Answer · 2019-05-08 14:06:10Z

1

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]]])

answered May 8, 2019 at 14:06

meowgoesthedog

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1 Comment

VnC Over a year ago

ah it's the second axis!! I was trying with 0 and 1 :facepalm:

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