# Numpy ndarray.transpose() function | Python

• Last Updated : 22 Apr, 2020

`numpy.ndarray.transpose()` function returns a view of the array with axes transposed.
For a 1-D array this has no effect, as a transposed vector is simply the same vector. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted. If axes are not provided and arr.shape = (i[0], i[1], … i[n-2], i[n-1]), then arr.transpose().shape = (i[n-1], i[n-2], … i[1], i[0]).

Syntax : numpy.ndarray.transpose(*axes)

Parameters :
axes : [None, tuple of ints, or n ints] None or no argument: reverses the order of the axes.
tuple of ints: i in the j-th place in the tuple means arr’s i-th axis becomes arr.transpose()’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)

Return : [ndarray] View of arr, with axes suitably permuted.

Code #1 :

 `# Python program explaining``# numpy.ndarray.transpose() function`` ` `# importing numpy as geek ``import` `numpy as geek`` ` `arr ``=` `geek.array([[``5``, ``6``], [``7``, ``8``]])`` ` `gfg ``=` `arr.transpose()`` ` `print``( gfg )`

Output :

```[[5 7]
[6 8]]
```

Code #2 :

 `# Python program explaining``# numpy.ndarray.transpose() function`` ` `# importing numpy as geek ``import` `numpy as geek`` ` `arr ``=` `geek.array([[``5``, ``6``], [``7``, ``8``]])`` ` `gfg ``=` `arr.transpose((``1``, ``0``))`` ` `print``( gfg )`

Output :

```[[5 7]
[6 8]]
```
My Personal Notes arrow_drop_up