- numpy.transpose(a, axes=None)#
Returns an array with axes transposed.
For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g.,
np.atleast_2d(a).Tachieves this, as does
a[:, np.newaxis]. For a 2-D array, this is the 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, then
transpose(a).shape == a.shape[::-1].
- axestuple or list of ints, optional
If specified, it must be a tuple or list which contains a permutation of [0,1,…,N-1] where N is the number of axes of a. The i’th axis of the returned array will correspond to the axis numbered
axes[i]of the input. If not specified, defaults to
range(a.ndim)[::-1], which reverses the order of the axes.
a with its axes permuted. A view is returned whenever possible.
transpose(a, argsort(axes))to invert the transposition of tensors when using the axes keyword argument.
>>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> np.transpose(a) array([[1, 3], [2, 4]])
>>> a = np.array([1, 2, 3, 4]) >>> a array([1, 2, 3, 4]) >>> np.transpose(a) array([1, 2, 3, 4])
>>> a = np.ones((1, 2, 3)) >>> np.transpose(a, (1, 0, 2)).shape (2, 1, 3)
>>> a = np.ones((2, 3, 4, 5)) >>> np.transpose(a).shape (5, 4, 3, 2)