How to stack arrays and scalars in numpy?

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 thebroadcasted sub-arrays.

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

The problem here is to fill the gap between the readable python world, and the efficient numpy world.

Experimentally, python is paradoxically often better that numpy for this task. With l=[ randint(10) if n%2 else randint(0,10,100) for n in range(32)] :

In [11]: %timeit array([x if type(x) is ndarray else [x]*100 for x in l])1000 loops, best of 3: 655 µs per loopIn [12]: %timeit column_stack(broadcast(*l))100 loops, best of 3: 3.77 ms per loop

Furthermore broadcast is limited to 32 elements.

Not a one liner, but you can fill an empty array with your scalar.

>>> a = np.empty(4, dtype=int )>>> a.fill(2)>>> print(a)[2 2 2 2]