Q:

Numpy: Subtract 2 numpy arrays row wise

I have 2 numpy arrays a and b as below:
a = np.random.randint(0,10,(3,2))
Out[124]: 
array([[0, 2],
       [6, 8],
       [0, 4]])
b = np.random.randint(0,10,(2,2))
Out[125]: 
array([[5, 9],
       [2, 4]])

I want to subtract each row in b from each row in a and the desired output is of shape(3,2,2):
array([[[-5, -7],        [-2, -2]],

       [[ 1, -1],        [ 4,  4]],

       [[-5, -5],        [-2,  0]]])

I can do this using: 
print(np.c_[(a - b[0]),(a - b[1])].reshape(3,2,2))

But I need a fully vectorized solution or a built in numpy function to do this.

A:

Just use np.newaxis (which is just an alias for None) to add a singleton dimension to a, and let broadcasting do the rest:
In [45]: a[:, np.newaxis] - b
Out[45]: 
array([[[-5, -7],
        [-2, -2]],

       [[ 1, -1],
        [ 4,  4]],

       [[-5, -5],
        [-2,  0]]])

