In This Article, You Will Learn About Python Numpy Random Permutations
Python Numpy Random permutations – Before moving ahead, let’s know a little bit about Python Numpy Random Data Distribution
Random Permutations of Elements
A Permutation is defined as arrangements of elements, e.g., [7,9,8] is a Permutation of [7,8,9] and vice-versa.
The NumPy Random module has two methods for Permutation of elements: shuffle() and permutation().
Shuffling Arrays
Shuffling Arrays means changing arranged elements of Array in Array itself.
Example – Returning randomly shuffling integers of the array.
from numpy import random import numpy as np Array = np.array([11, 12, 13, 14, 15]) random.shuffle(Array) print(Array)
Output - [13 14 11 12 15]
As shown above, it returned an array with randomly shuffling elements.
Example – Returning randomly shuffling strings of the array.
from numpy import random import numpy as np Array = np.array(['c', 'b', 'a', 'e', 'd']) random.shuffle(Array) print(Array)
Output - ['a' 'c' 'd' 'e' 'b']
As shown above, it returned an array with randomly shuffling elements.
The shuffle() method makes changes to the original array.
Generating Permutation of Arrays
Example – Generate a random permutation of elements of the array.
from numpy import random import numpy as np Array = np.array([11, 12, 13, 14, 15]) print(random.permutation(Array))
As a result, it returned an array with arrangement of random number.
Note: Permutation() method returns re-arranged array with no changing in original array.
If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.
Like us on
