Permuting a list

>>> from cryptorandom.cryptorandom import SHA256
>>> from cryptorandom.sample import random_permutation
>>> import numpy as np

The sample module contains methods for generating random permutations, compatible with any pseudorandom number generator that has randint and random methods. The module includes several algorithms to permute lists or arrays.

The main workhorse is the random_permutation function. The default algorithm is Fisher-Yates, a shuffling method.

>>> fruit = ['apple', 'banana', 'cherry', 'pear', 'plum']
>>> s = SHA256(1234567890)
>>> random_permutation(fruit, prng=s)
    array(['plum', 'apple', 'banana', 'pear', 'cherry'], dtype='<U6')

Numpy and the base random module offer methods for drawing simple random samples with and without replacement, but don’t allow you to choose the pseudorandom number generator. Numpy’s choice method also uses the Fisher-Yates method.

>>> np.random.permutation(fruit) # Returns permuted list
array(['apple', 'banana', 'plum', 'cherry', 'pear'], dtype='<U6')
>>> np.random.shuffle(fruit) # Permutes the list in place, returns None
>>> fruit
['cherry', 'plum', 'pear', 'banana', 'apple']

The permutation algorithms available are:

Method	description
Fisher-Yates	a shuffling algorithm
random_sort	generate random floats and sort
permute_by_index	sample integer indices without replacement

>>> %timeit random_permutation(fruit, method="Fisher-Yates", prng=s)
10000 loops, best of 3: 53.3 µs per loop
>>> %timeit random_permutation(fruit, method="random_sort", prng=s)
10000 loops, best of 3: 37.5 µs per loop
>>> %timeit random_permutation(fruit, method="permute_by_index", prng=s)
10000 loops, best of 3: 22 µs per loop