Broadcasting involves 2 steps

• give all arrays the same number of dimensions

• expand the 1 dimensions to match the other arrays

With your inputs

(41,6) (41,)

one is 2d, the other 1d; broadcasting can change the 1d to (1, 41), but it does not automatically expand in the other direction (41,1).

(41,6) (1,41) 

Neither (41,41) or (6,41) matches the other.

So you need to change your y to (41,1) or the x to (6,41)

x.T*y
x*y[:,None]

I'm assuming, of course, that you want element by element multiplication, not the np.dot matrix product.

Looks like the outer product.

>>> np.outer(a, b)
array([[ 10,  20,  30,  40,  50,  60,  70,  80],
       [ 20,  40,  60,  80, 100, 120, 140, 160],
       [ 30,  60,  90, 120, 150, 180, 210, 240]])

You could use None (or np.newaxis) to expand A to match B:

>>> A = np.arange(10)
>>> B = np.random.random((10,3,5))
>>> C0 = np.array([A[i]*B[i,:,:] for i in range(len(A))])
>>> C1 = A[:,None,None] * B
>>> np.allclose(C0, C1)
True

But this will only work for the 2 case. Borrowing from @ajcr, with enough transposes we can get implicit broadcasting to work for the general case:

>>> C3 = (A * B.T).T
>>> np.allclose(C0, C3)
True

Alternatively, you could use einsum to provide the generality. In retrospect it's probably overkill here compared with the transpose route, but it's handy when the multiplications are more complicated.

>>> C2 = np.einsum('i,i...->i...', A, B)
>>> np.allclose(C0, C2)
True

and

>>> B = np.random.random((10,4))
>>> D0 = np.array([A[i]*B[i,:] for i in range(len(A))])
>>> D2 = np.einsum('i,i...->i...', A, B)
>>> np.allclose(D0, D2)
True