Returns a view of the array with axes transposed.
For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D 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 2-D array, this is a standard 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, i, ... i[n-2], i[n-1]), then
a.transpose().shape = (i[n-1], i[n-2], ... i, i).
- 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)
- out : ndarray
View of a, with axes suitably permuted.
>>> 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]])