numpy.transpose#

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 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:

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.

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

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>>> 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)

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