Random Permutations using Numpy.

Question

ICSM Computer · Answer

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 elementsUse np.random.permutation() when you want a new shuffled copyUse np.random.shuffle() when you want to shuffle in-place

interview

Random Permutations using Numpy.

ICSM Computer

Can you answer this question?

1 Answers

1. `numpy.random.permutation()`

Example 1: Shuffle numbers 0 to 9

Example 2: Shuffle an existing array

2. `numpy.random.shuffle()`

Example:

Difference Between permutation() and shuffle()

Permuting Multi-Dimensional Arrays

permutation() always shuffles rows:

Randomly Shuffle List (Python list)

Summary

Liked By