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# NumPy Random Generator | permutation method

schedule Aug 12, 2023
Last updated
local_offer
PythonNumPy
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NumPy Random Generator's `permutation(~)` method return a new 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` | `int` or `array-like`

• If `x` is an `int`, then a `np.arange(x)` is randomly shuffled and returned.

• If `x` is `array-like`, then a new array with randomly shuffled values is returned.

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

The axis by which to perform the shuffling. By default, `axis=0`.

A NumPy array.

# Examples

## Passing an integer

To get a shuffled array of `[0,1,2,3,4]`:

``` import numpy as nprng = np.random.default_rng(seed=42)rng.permutation(5) array([4, 2, 3, 1, 0]) ```

Note that this is equivalent to `rng.permutation(np.arange(5))`.

## Passing in an array

To randomly shuffle an array of numbers:

``` rng = np.random.default_rng(seed=42)rng.permutation([5,2,6,1]) array([1, 6, 2, 5]) ```

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

## Setting axis

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 the rows

By default, `axis=0`, which means that the rows are randomly shuffled (in a two-dimensional array):

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

### Shuffling the columns

To randomly shuffle the columns of a two-dimensional array:

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