Using random

https://docs.python.org/3/library/random.html

INTRODUCTION

This module implements pseudo-random number generators for various distributions.

import random

# Bookkeeping functions

# random.seed(a=None) - initialize the random number generator
# random.seed() - the current system time is used
# random.getstate()
# random.setstate(state)

# Functions for integers

# random.randrange(stop)
# random.randrange(start, stop[, step])
# Choose a random item from range(start, stop[, step]).

# random.randint(a, b) - alias for randrange(a, b+1), b is included
random.randint(1, 6)   # in games, choose from range(1, 7)

# Functions for sequences

# random.choice(sequence) - return a random element from sequence
random.choice([0, 1])   # heads or tails, orzeł czy reszka, random.randint(0, 1)
random.choice(["A", "B", "C", "D"])   # during exams ...
random.choice(['win', 'lose', 'draw'])

# random.choices(population, weights=None, *, cum_weights=None, k=1) (Py3.6)
# Return a k sized list of elements chosen from the population with replacement.
random.choices(range(10), k=5)   # [2, 7, 5, 8, 5]

# Six roulette wheel spins (weighted sampling with replacement)
random.choices(['red', 'black', 'green'], [18, 18, 2], k=6)
# ['red', 'green', 'black', 'black', 'red', 'black']

# random.sample(population, k)
# random.sample(population, k, *, counts=None) (Py3.9)
# Return a k length list of unique elements chosen from the population sequence.
# Members of the population need not be hashable or unique.
random.sample(['red', 'blue'], counts=[4, 2], k=5)
random.sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) # the same
random.sample(range(10000000), k=60)   # fast and space efficient

# random.shuffle(sequence)
# Shuffle the sequence in place (Fisher-Yates shuffle).
# To shuffle an immutable sequence and return a new shuffled list,
# use random.sample(L, k=len(L)) instead.

# Real-valued distributions

# random.random() - return a random float from [0.0, 1.0)
# random.uniform(a, b) - return a random float from [a, b]
# random.triangular(low, high, mode)
# random.gauss(mu, sigma) - Gaussian distribution