Q:

Flatten or group array in blocks of columns - NumPy / Python

Is there any easy way to flatten
import numpy    
np.arange(12).reshape(3,4)
Out[]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

into
array([ 0,  1,  4,  5, 8, 9, 2, 3, 6, 7, 10, 11])

A:

It seems like you are looking to consider a specific number of cols to form blocks and then getting the elements in each block and then moving onto the next ones.  So, with that in mind, here's one way -
In [148]: a
Out[148]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [149]: ncols = 2 # no.  of cols to be considered for each block

In [150]: a.reshape(a.shape[0],-1,ncols).swapaxes(0,1).ravel()
Out[150]: array([ 0,  1,  4,  5,  8,  9,  2,  3,  6,  7, 10, 11])

The motivation behind is discussed in detail in this post. 
Additionally, to keep the 2D format -
In [27]: a.reshape(a.shape[0],-1,ncols).swapaxes(0,1).reshape(-1,ncols)
Out[27]: 
array([[ 0,  1],
       [ 4,  5],
       [ 8,  9],
       [ 2,  3],
       [ 6,  7],
       [10, 11]])

And to have it in a intuitive 3D array format -
In [28]: a.reshape(a.shape[0],-1,ncols).swapaxes(0,1)
Out[28]: 
array([[[ 0,  1],
        [ 4,  5],
        [ 8,  9]],

       [[ 2,  3],
        [ 6,  7],
        [10, 11]]])

