Multiplying Numpy 3D arrays by 1D arrays

Question

I am trying to multiply a 3D array by a 1D array, such that each 2D array along the 3rd (depth: d) dimension is calculated like:

1D_array[d]*2D_array

And I end up with an array that looks like, say:

[[
[1,1]
[1,1]]
[
[2,2]
[2,2]]
[
[3,3]
[3,3]]]

Which would be the result of correctly multiplying np.ones((3,2,2)) with [1,2,3].

I've been trying for some time now and whatever I seem to do I can't end up with this result, just variations on the theme. How do I correctly go about doing this?

Thanks for any help.

Homicide

If your arrays are a and b, are you looking for b[:, None] * a? – Damselfish

I just tried b=np.ones((3,2,2)) a=np.array(range(-1,2)) ans=b[:, None]*a But it threw an error: ValueError: operands could not be broadcast together with shapes (1,3) (3,2,2) I'm sure I'm just doing something foolish? – Homicide

in this case I think you want a[:, None, None] * b. – Damselfish

Thankyou, that appears to be exactly what I want! – Homicide

You ask for "2D arrays stacked along the 3rd dimension", but your example has them stacked along the 1st dimension. – Tobolsk

Good point Jaime, I made a mistake when I posed the question, so thankyou for the clarification. Hopefully other users that find this post will still be able to work with Alok's replies. – Homicide

@Alok, you could post your comment as an answer since it solved the problem... – Hammertoe

Answer 1

Let's assume b=np.ones((3,2,2)) and a=np.array([1,2,3]). I really do like the answer of @Alok which uses the simple a[:, None, None] * b which surely works with your problem. What I dislike with this formulation is that it's quite dimension specific. What I mean is that it can only be used with 3 dimensional arrays, which was not true in my problem, where b could be a 1D or a 3D array with the exact same length for axis 0. I hence found a way to accommodate it to my problem :

broad_a = np.broadcast_to(a, b.T.shape).T
result = broad_a * b
print(result)
[[
[1,1]
[1,1]]
[
[2,2]
[2,2]]
[
[3,3]
[3,3]]]

Giving also the intended result for your case.

Polemic