# `tensorly`.transpose

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.atleast2d(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. 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.

`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

```>>> 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)
```