element wise multiplication of a vector and a matrix with numpy

Question

Given python code with numpy:

import numpy as np
a = np.arange(6).reshape(3, 2)   # a = [[0, 1], [2, 3], [4, 5]]; a.shape = (3, 2)
b = np.arange(3) + 1             # b = [1, 2, 3]               ; b.shape = (3,)

How can I multiply each value in b with each corresponding row ('vector') in a? So here, I want the result as:

result = [[0, 1], [4, 6], [12, 15]]    # result.shape = (3, 2)

I can do this with a loop, but I am wondering about a vectorized approach. I found an Octave solution here. Apart from this, I didn't find anything else. Any pointers for this? Thank you in advance.

Answer 1

Probably the simplest is to do the following.

import numpy as np
a = np.arange(6).reshape(3, 2)   # a = [[0, 1], [2, 3], [4, 5]]; a.shape = (3, 2)
b = np.arange(3) + 1  

ans = np.diag(b)@a

Here's a method that exploits numpy multiplication broadcasting:

ans = (b*a.T).T

These two solutions basically take the same approach

ans = np.tile(b,(2,1)).T*a
ans = np.vstack([b for _ in range(a.shape[1])]).T*a