如何用一维数组中的值替换 3D numpy 数组的第一维？

Question

I've got a 3D and a 1D numpy array- A sized (3750, 17, 1000) and B sized (3750). I want to replace the values in the 1st dimension of A with the values from array B, so that the resulting array C is still sized (3750, 17, 1000), but the values in the first dimension are different.

>>> A.shape
(3750, 17, 1000)

>>> B.shape (3750,)

>>> C.shape(3750, 17, 1000)

I've tried:

>>> C = np.concatenate((A, np.broadcast_to(np.array(B)[:, None, None],A.shape)), axis = 0)

But the output is:

>>> C.shape (7500, 17, 1000)

So basically if

A =

1 [x, y ... 1000]

  [x, y ... 1000]

  ...17

2 [x, y ... 1000]

  [x, y ... 1000]

  ...17

3 [x, y ... 1000]

  [x, y ... 1000]

  ...17
.
.
.
3750

and B =

22
43
11
.
.
n=3750

Then C should look like

22 [x, y ... 1000]
   [x, y ... 1000]
    ...17

43 [x, y ... 1000]
   [x, y ... 1000]
    ...17

11 [x, y ... 1000]
   [x, y ... 1000]
    ...17
.
.
.
n=3750

Answer 1

Do you mean:

A[:,0,0] = B

Is it correct?