numpy.matrix.transpose¶
method

matrix.
transpose
(*axes)¶ Returns a view of the array with axes transposed.
For a 1D array this has no effect, as a transposed vector is simply the same vector. To convert a 1D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2D array, this is a standard matrix transpose. For an nD 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[n2], i[n1])
, thena.transpose().shape = (i[n1], i[n2], ... i[1], i[0])
. Parameters
 axesNone, tuple of ints, or n ints
None or no argument: reverses the order of the axes.
tuple of ints: i in the jth place in the tuple means a’s ith axis becomes a.transpose()’s jth axis.
n ints: same as an ntuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
 Returns
 outndarray
View of a, with axes suitably permuted.
See also
ndarray.T
Array property returning the array transposed.
ndarray.reshape
Give a new shape to an array without changing its data.
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]])