You could create broadcast object, and call np.column_stack on that:

In [175]: np.column_stack(np.broadcast([1, 2, 3], 0, [3, 2, 1]))
Out[175]: 
array([[1, 2, 3],
       [0, 0, 0],
       [3, 2, 1]])

Alternatively, you could ask NumPy to literally broadcast the items to compatibly-shaped arrays:

In [158]: np.broadcast_arrays([1, 2, 3], [3, 2, 1], 0)
Out[158]: [array([1, 2, 3]), array([3, 2, 1]), array([0, 0, 0])]

and then call vstack or row_stack on that:

In [176]: np.row_stack(np.broadcast_arrays([1, 2, 3], 0, [3, 2, 1]))
Out[176]: 
array([[1, 2, 3],
       [0, 0, 0],
       [3, 2, 1]])

Of these two options (using np.broadcast or np.broadcast_arrays), np.broadcast is quicker since you don't actually need to instantiate the broadcasted sub-arrays.

One limitation of np.broadcast, however, is that it can accept at most 32 arguments. In that case, use np.broadcast_arrays.

You can get the same with:

import numpy as np
a = np.arange(7, 13).reshape(2, 3)
b = np.arange(1, 7).reshape(2, 3)
z = np.stack((a, b), axis=1)
print(z)
# [[[ 7  8  9]
#   [ 1  2  3]]
# 
#  [[10 11 12]
#   [ 4  5  6]]]