- numpy.transpose(a, axes=None)[source]#
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.atleast2d(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.
Move axes of an array to new positions.
Return the indices that would sort an array.
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)