numpy.transpose() ⚬ Entries

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numpy.transpose() ⚬｜Documentation｜1st｜20251021130108-00-⌔

numpy.transpose — NumPy v2.4 Manual#numpy.transpose

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).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.

See also:

ndarray.transpose

Equivalent method.

moveaxis

Move axes of an array to new positions.

argsort

Return the indices that would sort an array.

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

Printed 2026-06-28.

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