np.concatenate a ND tensor/array with a 1D array

Question

I have two arrays a & b

a.shape
(5, 4, 3)
array([[[ 0.        ,  0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        ],
        [ 0.          


        

Answer 1

Here are some simple timings based on cᴏʟᴅsᴘᴇᴇᴅ's and Divakar's solutions:

%timeit np.concatenate((a, b.reshape(1, 1, -1).repeat(a.shape[0], axis=0)), axis=1)

Output: The slowest run took 6.44 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: 3.68 µs per loop

%timeit np.concatenate((a, np.broadcast_to(b[None,None], (a.shape[0], 1, len(b)))), axis=1)

Output: The slowest run took 4.12 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: 10.7 µs per loop

Now here is the timing based on your original code:

%timeit original_func(a, b)

Output: The slowest run took 4.62 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: 4.69 µs per loop

Since the question asked for faster ways to come up with the same result, I would go for cᴏʟᴅsᴘᴇᴇᴅ's solution based on these problem calculations.