ndarray.reshape(shape, order='C') and numpy.reshape(a, newshape, order='C') are basically common.
The only difference is that the shape of ndarray.reshape can be written as a tuple type of ndarray.reshape((10, 11)) or an independent form of ndarray.reshape(10, 11), both representing 10 rows and 11 columns. , The introduction in the code is as follows:
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.
From the following test, we can see some routines for setting the shape of reshape:
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]])
Reshape the data of (2,3,3), it seems that reshape is to expand the data into 1 row first, and then convert it according to the size set in the reshape method:
for example,
reshape((-1,2)), meaning Regardless of the number of rows, only column=2 is considered. Since the original data is (2,3,3)=18 elements, the number of rows in the new shape=18/2=9