what does the np.stack() function do eg:x=np.stack((x1,x2),axis=1)
About functioning of the function np.stack()
numpy.stack(arrays, axis=0, out=None)
Join a sequence of arrays along a new axis.
The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.
Parameters:
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
out : ndarray, optional
If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.
Returns:
stacked : ndarray
The stacked array has one more dimension than the input arrays.
Examples
arrays = [np.random.randn(3, 4) for _ in range(10)]
np.stack(arrays, axis=0).shape
>>>(10, 3, 4)
np.stack(arrays, axis=1).shape
>>>(3, 10, 4)
np.stack(arrays, axis=2).shape
>>>(3, 4, 10)
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
np.stack((a, b))
>>>array([[1, 2, 3],
[2, 3, 4]])
np.stack((a, b), axis=-1)
>>>array([[1, 2],
[2, 3],
[3, 4]])
To give you a better understanding of axis.
Say you have an arr of shape (1,2,3,4) then axis = 0 is stack in rows, axis = 1 is stack in columns and axis = 2 is the third dimension and axis = 3 is the fourth dimension.
Happy Learning 
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