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# NumPy Random Generator | permuted method

schedule Aug 10, 2023
Last updated
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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. axislink | 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 np
rng = 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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