combining 2D arrays to 3D arrays

Question

Hello I have 3 numpy arrays as given below.

>>> print A
[[ 1.  0.  0.]
 [ 3.  0.  0.]
 [ 5.  2.  0.]
 [ 2.  0.  0.]
 [ 1.  2.  1.]]
>>> pri         


        

Answer 1

>>> import numpy as np
>>> A = np.array([[1,0,0],[3,0,0],[5,2,0],[2,0,0],[1,2,1]])
>>> B = np.array([[5,9,9],[37,8,9],[49,8,3],[3,3,1],[4,4,5]])
>>> C = np.array([[0,0,0],[0,6,0],[1,4,6],[6,2,0],[0,5,4]])
>>> np.array([A,B,C]).swapaxes(1,0)

array([[[ 1,  0,  0],
    [ 5,  9,  9],
    [ 0,  0,  0]],

   [[ 3,  0,  0],
    [37,  8,  9],
    [ 0,  6,  0]],

   [[ 5,  2,  0],
    [49,  8,  3],
    [ 1,  4,  6]],

   [[ 2,  0,  0],
    [ 3,  3,  1],
    [ 6,  2,  0]],

   [[ 1,  2,  1],
    [ 4,  4,  5],
    [ 0,  5,  4]]])

I did a comparison of the answers using Ipython %%timeit:

np.array([A,B,C]).swapaxes(1,0)
100000 loops, best of 3: 18.2 us per loop

np.dstack((A,B,C)).swapaxes(1,2)
100000 loops, best of 3: 19.8 us per loop

np.hstack([A,B,C]).reshape((5,3,3))
100000 loops, best of 3: 14.8 us per loop

np.hstack([A[:, None, :], B[:, None, :], C[:, None, :]])
100000 loops, best of 3: 17.2 us per loop

It looks like @Viktor Kerkez's answer is fastest.