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What's the difference between these two? And because of this difference I'm unable to stack them using

np.vstack((array2, array1))
What changes should I make to array1 shape so that I can stack them up?
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3/dist-packages/numpy/core/shape_base.py", line 230, in vstack
    return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
ValueError: all the input array dimensions except for the concatenation axis must match exactly
However, with a simple change, they will stack:
>>> np.vstack((x, y[:, None]))
array([[ 0.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.]])
Alternatively:
>>> np.vstack((x[:, 0], y))
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])
One array is 1d, the other 2d.  vertical stack requires 2 dimensions, vertical and horizontal.  So np.vstack passes each input through np.atleast_2d:
In [82]: np.atleast_2d(x1).shape
Out[82]: (1, 10)
But now we have a (1,10) array and a (10,1) - they can't be joined in either axis.
But if we reshape x1 so it is (10,1), then we can join it with x2 in either direction:
In [83]: np.concatenate((x1[:,None],x2), axis=0).shape
Out[83]: (20, 1)
In [84]: np.concatenate((x1[:,None],x2), axis=1).shape
Out[84]: (10, 2)
Print out the two arrays:
In [86]: x1
Out[86]: array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])
In [87]: x2
Out[87]: 
array([[ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.]])
How can you concatenate those two shapes without some sort of adjustment?
        
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