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Home Interviews Random Permutations using Numpy. – MindStick
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ICSM Computer
ICSM Computer 26 Nov 2025
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Random Permutations in NumPy allow you to shuffle data or generate a random ordering of elements.

NumPy provides two important functions for this:

1. numpy.random.permutation()

Creates a new shuffled copy of a sequence or array.

Example 1: Shuffle numbers 0 to 9

import numpy as np

print(np.random.permutation(10))

Output example:

[3 7 1 0 6 9 5 4 2 8]

Example 2: Shuffle an existing array

arr = np.array([10, 20, 30, 40])
print(np.random.permutation(arr))

Output:

[30 10 40 20]

2. numpy.random.shuffle()

Shuffles the original array in-place (modifies the existing one).

Example:

arr = np.array([1, 2, 3, 4])
np.random.shuffle(arr)
print(arr)

Output (original array is changed):

[3 1 4 2]

Difference Between permutation() and shuffle()

Function Returns New Array? Modifies Original?
np.random.permutation() Yes No
np.random.shuffle() No Yes

Permuting Multi-Dimensional Arrays

permutation() always shuffles rows:

arr = np.array([[1,2],[3,4],[5,6]])
print(np.random.permutation(arr))

Possible output:

[[5 6]
 [1 2]
 [3 4]]

Randomly Shuffle List (Python list)

NumPy also works with Python lists:

print(np.random.permutation([1, 2, 3, 4, 5]))

Summary

  • Random permutation = random ordering of elements
  • Use np.random.permutation() when you want a new shuffled copy
  • Use np.random.shuffle() when you want to shuffle in-place
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