Numpy vstack vs. column_stack

Question

What precisely is the contrast between numpy vstack and column_stack. Perusing the documentation, it looks as though column_stack is an execution of vstack for 1D arrays. Is it a more productive execution? Else, I can't discover an explanation behind having vstack. 

Answer 1

Look at the following code:

>>> np.vstack(([1,2,3],[4,5,6]))

array([[1, 2, 3],

       [4, 5, 6]])

>>> np.column_stack(([1,2,3],[4,5,6]))

array([[1, 4],

       [2, 5],

       [3, 6]])

>>> np.hstack(([1,2,3],[4,5,6]))

array([1, 2, 3, 4, 5, 6])

I've included hstack for examination too. Notice how column_stack stacks along the subsequent measurement though vstack stacks along the primary measurement. The identical to column_stack is the accompanying hstack order:

>>> np.hstack(([[1],[2],[3]],[[4],[5],[6]]))

array([[1, 4],

       [2, 5],

       [3, 6]])

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