Using broadcasting, we can work with arrays that don't have exactly the same shape. Here is an example:

    In [357]: ar=np.ones([3,2]); ar
    Out[357]: array([[ 1.,  1.],
                     [ 1.,  1.],
                     [ 1.,  1.]])
    
    In [358]: ar2=np.array([2,3]); ar2
    Out[358]: array([2, 3])
    
    In [359]: ar+ar2
    Out[359]: array([[ 3.,  4.],
                     [ 3.,  4.],
                     [ 3.,  4.]])

Thus, we can see that ar2 is broadcast across the rows of ar by adding it to each row of ar, producing the preceding result. Here is another example, showing that broadcasting works across dimensions:

    In [369]: ar=np.array([[23,24,25]]); ar
    Out[369]: array([[23, 24, 25]])
    In [368]: ar.T
    Out[368]: array([[23],
                     [24],
                     [25]])
    In [370]: ar.T+ar
    Out[370]: array([[46, 47, 48],
                     [47, 48, 49],
                     [48, 49, 50]])

Here, both row and column arrays were broadcast and we ended up with a 3 × 3 array.