transpose
numpy.ndarray.
transpose
ndarray.
transpose(*axes)
Returns
a view
of the array
with axes transposed.
For
a 1-D array, this has no effect. (To change between column
and row vectors,
first cast
the 1-D array
into a matrix object.)
For
a 2-D array, this is
the usual matrix
transpose. For
an n-D array,
if axes are given, their order indicates how
the axes are permuted (see Examples). If axes are
not provided
and a.shape = (i[
0], i[
1], ... i[n-
2], i[n-
1]),
then a.
transpose().shape = (i[n-
1], i[n-
2], ... i[
1], i[
0]).
Parameters:
axes : None, tuple
of ints,
or n ints
None
or no argument: reverses
the order
of the axes.
tuple
of ints: i
in the j-th place
in the tuple means
a‘s i-th axis becomes
a.
transpose()‘s j-th axis.
n ints: same
as an n-tuple
of the same ints (this form is intended simply
as a “convenience” alternative
to the tuple form)
Returns:
out : ndarray
View
of a,
with axes suitably permuted.
See also
ndarray.T
Array property returning
the array transposed.
Examples
>>>
>>>
a = np.array([[
1,
2], [
3,
4]])
>>>
a
array([[
1,
2],
[
3,
4]])
>>>
a.
transpose()
array([[
1,
3],
[
2,
4]])
>>>
a.
transpose((
1,
0))
array([[
1,
3],
[
2,
4]])
>>>
a.
transpose(
1,
0)
array([[
1,
3],
[
2,
4]])
对于三维数组难理解一点：假设 shape(z, x, y)
shape 的 x轴 与 y 轴的转换比较简单， 跟二维数组一样
In [
27]: arr.transpose((
0,
2,
1))
Out[
27]:
array(
[[[ 0, 4],
[ 1, 5],
[ 2, 6],
[ 3, 7]],
[[ 8, 12],
[ 9, 13],
[10, 14],
[11, 15]]])
对于 z 轴 与 x 轴的变换
In [
40]: arr = np.arange(
16).reshape((
2,
2,
4))
In [
41]: arr
Out[
41]:
array(
[[[ 0, 1, 2, 3],
[ 4, 5, 6, 7]],
[[ 8, 9, 10, 11],
[12, 13, 14, 15]]])
In [
42]: arr.transpose((
1,
0,
2))
Out[
42]:
array(
[[[ 0, 1, 2, 3],
[ 8, 9, 10, 11]],
[[ 4, 5, 6, 7],
[12, 13, 14, 15]]])
transpose 的变换是根据 shape 进行的
转换前 shape 是(0, 1, 2)
[[(0,0,0), (0,0,1), (0,0,2), (0,0,3)] ／／ [[[ 0, 1, 2, 3], [(0,1,0), (0,1,1), (0,1,2), (0,1,3)], ／／ [ 4, 5, 6, 7]], [(1,0,0), (1,0,1), (1,0,2), (1,0,3)] ／／ [[ 8, 9, 10, 11], [(1,1,0), (1,1,1), (1,1,2), (1,1,3)]]. ／／[12, 13, 14, 15]]]
转换后 shape 是(1, 0, 2), 也就是调换位于 z 轴 和 x 轴的shape
[[(0,0,0), (0,0,1), (0,0,2), (0,0,3)] (1,0,0), (1,0,1), (1,0,2), (1,0,3)], [(0,1,0), (0,1,1), (0,1,2), (0,1,3)] [(1,1,0), (1,1,1), (1,1,2), (1,1,3)]]
将转换前 shape 对应的值填进去 得到
[1,2,3,4]
[8,9,10,11]
[4,5,6,7]
[12,13,14,15]
so perfect 刚好对应输出
