numpy.transpose — NumPy v2.2 Manual (original) (raw)

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.atleast_2d(a).T achieves this, as doesa[:, 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, thentranspose(a).shape == a.shape[::-1].

Parameters:

aarray_like

Input array.

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. Negative indices can also be used to specify axes. 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.

Returns:

pndarray

a with its axes permuted. A view is returned whenever possible.

Notes

Use transpose(a, argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.

Examples

import numpy as np 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)

a = np.arange(345).reshape((3, 4, 5)) np.transpose(a, (-1, 0, -2)).shape (5, 3, 4)