`numpy:transpose`

source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/_core/fromnumeric.py :604

Signature

def transpose ( a , axes = None )

Summary

Returns an array with axes transposed.

Extended Summary

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 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].

Parameters

a : array_like: Input array.
axes : tuple 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

p : ndarray: 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
np.transpose(a)

✓

a = np.array([1, 2, 3, 4])
a
np.transpose(a)

✓

a = np.ones((1, 2, 3))
np.transpose(a, (1, 0, 2)).shape

✓

a = np.ones((2, 3, 4, 5))
np.transpose(a).shape

✓

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

✓

Aliases

numpy.permute_dims