Python: Add a column to numpy 2d array

Using numpy index trick to append a 1D vector to a 2D array

a = np.zeros((6,2))
# array([[ 0.,  0.],
#        [ 0.,  0.],
#        [ 0.,  0.],
#        [ 0.,  0.],
#        [ 0.,  0.],
#        [ 0.,  0.]])
b = np.ones(6) # or np.ones((6,1))
#array([1., 1., 1., 1., 1., 1.])
np.c_[a,b]
# array([[0., 0., 1.],
#        [0., 0., 1.],
#        [0., 0., 1.],
#        [0., 0., 1.],
#        [0., 0., 1.],
#        [0., 0., 1.]])

Under cover all the stack variants (including append and insert) end up doing a concatenate. They just precede it with some sort of array reshape.

In [60]: A = np.arange(12).reshape(3,4)

In [61]: np.concatenate([A, np.ones((A.shape[0],1),dtype=A.dtype)], axis=1)
Out[61]: 
array([[ 0,  1,  2,  3,  1],
       [ 4,  5,  6,  7,  1],
       [ 8,  9, 10, 11,  1]])

Here I made a (3,1) array of 1s, to match the (3,4) array. If I wanted to add a new row, I'd make a (1,4) array.

While the variations are handy, if you are learning, you should become familiar with concatenate and the various ways of constructing arrays that match in number of dimensions and necessary shapes.

Let me just throw in a very simple example with much smaller size. The principle should be the same.

a = np.zeros((6,2))
    array([[ 0.,  0.],
           [ 0.,  0.],
           [ 0.,  0.],
           [ 0.,  0.],
           [ 0.,  0.],
           [ 0.,  0.]])
b = np.ones((6,1))
    array([[ 1.],
           [ 1.],
           [ 1.],
           [ 1.],
           [ 1.],
           [ 1.]])

np.hstack((a,b))
array([[ 0.,  0.,  1.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  1.]])