/NumPy 1.17

# numpy.char.chararray.transpose

method

`chararray.transpose(*axes)`

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. To convert a 1-D array into a 2D column vector, an additional dimension must be added. `np.atleast2d(a).T` achieves this, as does `a[:, np.newaxis]`. 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 (see Examples). If axes are not provided and `a.shape = (i, i, ... i[n-2], i[n-1])`, then `a.transpose().shape = (i[n-1], i[n-2], ... i, i)`.

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 `a`’s `i`-th axis becomes `a.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) `out : ndarray` View of `a`, with axes suitably permuted.

`ndarray.T`
Array property returning the array transposed.
`ndarray.reshape`
Give a new shape to an array without changing its data.

#### Examples

```>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
```