ndarray.reshape(shape, order='C')与numpy.reshape(a, newshape, order='C')基本是通用的。
仅有的区别是ndarray.reshape的shape可以写成ndarray.reshape((10, 11))这种tuple型，也可以写成ndarray.reshape(10, 11)这种独立的形式，都表示10行11列，代码中的介绍如下：
def reshape(self, shape, order='C'): # real signature unknown; restored from __doc__
"""
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to `numpy.reshape` for full documentation.
See Also
--------
numpy.reshape : equivalent function
Notes
-----
Unlike the free function `numpy.reshape`, this method on `ndarray` allows
the elements of the shape parameter to be passed in as separate arguments.
For example, ``a.reshape(10, 11)`` is equivalent to
``a.reshape((10, 11))``.
"""
pass
def reshape(a, newshape, order='C'):
"""
Gives a new shape to an array without changing its data.
Parameters
----------
a : array_like
Array to be reshaped.
newshape : int or tuple of ints
The new shape should be compatible with the original shape. If
an integer, then the result will be a 1-D array of that length.
One shape dimension can be -1. In this case, the value is
inferred from the length of the array and remaining dimensions.
order : {'C', 'F', 'A'}, optional
Read the elements of `a` using this index order, and place the
elements into the reshaped array using this index order. 'C'
means to read / write the elements using C-like index order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to read / write the
elements using Fortran-like index order, with the first index
changing fastest, and the last index changing slowest. Note that
the 'C' and 'F' options take no account of the memory layout of
the underlying array, and only refer to the order of indexing.
'A' means to read / write the elements in Fortran-like index
order if `a` is Fortran *contiguous* in memory, C-like order
otherwise.
Returns
-------
reshaped_array : ndarray
This will be a new view object if possible; otherwise, it will
be a copy. Note there is no guarantee of the *memory layout* (C- or
Fortran- contiguous) of the returned array.
See Also
--------
ndarray.reshape : Equivalent method.
从下面的测试可以看出一些设置reshape形状的套路：
l1 = [[1,3,-1],[5,7,-3],[11,13,-5]]
l2 = [[2,4,-2],[6,8,-4],[10,12,-6]]
>>>np.array((l1,l2)).shape
(2, 3, 3)
>>>np.array((l1,l2))
array([[[ 1, 3, -1],
[ 5, 7, -3],
[11, 13, -5]],
[[ 2, 4, -2],
[ 6, 8, -4],
[10, 12, -6]]])
>>>np.array((l1,l2)).reshape((-1,2))
array([[ 1, 3],
[-1, 5],
[ 7, -3],
[11, 13],
[-5, 2],
[ 4, -2],
[ 6, 8],
[-4, 10],
[12, -6]])
>>>np.array((l1,l2)).reshape((-1,3))
array([[ 1, 3, -1],
[ 5, 7, -3],
[11, 13, -5],
[ 2, 4, -2],
[ 6, 8, -4],
[10, 12, -6]])
>>>np.array((l1,l2)).reshape((2,-1))
array([[ 1, 3, -1, 5, 7, -3, 11, 13, -5],
[ 2, 4, -2, 6, 8, -4, 10, 12, -6]])
对（2,3,3）的数据做reshape，看起来reshape是把数据先展成1行，然后按照reshape方法中设定的尺寸去转换：
例如，
reshape((-1,2))，意为不论行数，只考虑列=2，由于原数据是（2,3,3）=18个元素，那么新shape的行数=18/2=9