# numpy.random.Generator.permuted#

method

random.Generator.permuted(x, axis=None, out=None)#

Randomly permute x along axis axis.

Unlike `shuffle`, each slice along the given axis is shuffled independently of the others.

Parameters
xarray_like, at least one-dimensional

Array to be shuffled.

axisint, optional

Slices of x in this axis are shuffled. Each slice is shuffled independently of the others. If axis is None, the flattened array is shuffled.

outndarray, optional

If given, this is the destinaton of the shuffled array. If out is None, a shuffled copy of the array is returned.

Returns
ndarray

If out is None, a shuffled copy of x is returned. Otherwise, the shuffled array is stored in out, and out is returned

Notes

An important distinction between methods `shuffle` and `permuted` is how they both treat the `axis` parameter which can be found at Handling the axis parameter.

Examples

Create a `numpy.random.Generator` instance:

```>>> rng = np.random.default_rng()
```

Create a test array:

```>>> x = np.arange(24).reshape(3, 8)
>>> x
array([[ 0,  1,  2,  3,  4,  5,  6,  7],
[ 8,  9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]])
```

Shuffle the rows of x:

```>>> y = rng.permuted(x, axis=1)
>>> y
array([[ 4,  3,  6,  7,  1,  2,  5,  0],  # random
[15, 10, 14,  9, 12, 11,  8, 13],
[17, 16, 20, 21, 18, 22, 23, 19]])
```

x has not been modified:

```>>> x
array([[ 0,  1,  2,  3,  4,  5,  6,  7],
[ 8,  9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]])
```

To shuffle the rows of x in-place, pass x as the out parameter:

```>>> y = rng.permuted(x, axis=1, out=x)
>>> x
array([[ 3,  0,  4,  7,  1,  6,  2,  5],  # random
[ 8, 14, 13,  9, 12, 11, 15, 10],
[17, 18, 16, 22, 19, 23, 20, 21]])
```

Note that when the `out` parameter is given, the return value is `out`:

```>>> y is x
True
```