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# NumPy Random Generator | permuted method

NumPy
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NumPy Random Generator
schedule Jul 1, 2022
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NumPy Random's `permuted(~)` method returns a new NumPy array with the values shuffled.

NOTE

To shuffle values in-place, use `shuffle(~)`.

Also, the difference between `permutation(~)` and `permuted(~)` is that the former shuffles rows or columns for two-dimensional arrays, but `permuted(~)` shuffles values independent of the other rows or columns. Consult examples below for clarification.

# Parameters

1. `x` | `NumPy array` or `MutableSequence`

The array to shuffle.

2. `axis`link | `int` | `optional`

The axis to shuffle. By default, `axis=None`, which means that all the values in the array are shuffled.

3. `out` | `NumPy array` | `optional`

If given, then the result is stored in `out`. By default, a new NumPy array is created and returned.

A NumPy array.

# Examples

## Shuffling a one-dimensional array

To shuffle a one-dimensional array:

``` import numpy as nprng = np.random.default_rng(seed=42)rng.permuted([5,2,6,1]) array([1, 6, 2, 5]) ```

Note that when shuffling one-dimensional arrays, the behaviour is exactly the same as `permutation(~)`.

## Shuffling two-dimensional array

Consider the following two-dimensional array:

``` x = np.arange(12).reshape((3,4))x array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) ```

### Shuffling all values

By default, `axis=None`, which means that all the values in the array are shuffled:

``` rng = np.random.default_rng(seed=42)rng.permuted(x) # axis=None array([[ 0, 7, 6, 9], [11, 3, 5, 2], [ 4, 10, 1, 8]]) ```

### Shuffling values of each column

To shuffle the values in each column, set `axis=0`:

``` rng = np.random.default_rng(seed=42)rng.permuted(x, axis=0) array([[ 8, 1, 6, 11], [ 4, 9, 10, 3], [ 0, 5, 2, 7]]) ```

Note that each row is shuffled independently on how the values in the other columns are sorted. This is the main difference between `permuted(~)` and `permutation(~)`.

### Shuffling values of each row

To shuffle the values in each row, set `axis=1`:

``` rng = np.random.default_rng(seed=42)rng.permuted(x, axis=1) array([[ 3, 2, 1, 0], [ 7, 6, 4, 5], [ 8, 11, 10, 9]]) ```
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