Gives a new shape to an array without changing its data.
Parameters :  a : array_like
newshape : int or tuple of ints
order : {‘C’, ‘F’}, optional


Returns :  reshaped_array : ndarray

See also
Notes
It is not always possible to change the shape of an array without copying the data. If you want an error to be raise if the data is copied, you should assign the new shape to the shape attribute of the array:
>>> a = np.zeros((10, 2))
# A transpose make the array noncontiguous
>>> b = a.T
# Taking a view makes it possible to modify the shape without modiying the
# initial object.
>>> c = b.view()
>>> c.shape = (20)
AttributeError: incompatible shape for a noncontiguous array
Examples
>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])
>>> np.reshape(a, (3,1)) # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])