Python Tutorial (43) - random module

Time： 2024-09-19 Column：Python views：308

The Python random module is primarily used to generate random numbers. It implements various distributions for pseudorandom number generators.

To use the random module, you must first import it:

import random

View the contents of the random module:

Example:

>>> import random
>>> dir(random)
['BPF', 'LOG4', 'NV_MAGICCONST', 'RECIP_BPF', 'Random', 'SG_MAGICCONST', 'SystemRandom', 'TWOPI', '_Sequence', '_Set', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_accumulate', '_acos', '_bisect', '_ceil', '_cos', '_e', '_exp', '_floor', '_inst', '_log', '_os', '_pi', '_random', '_repeat', '_sha512', '_sin', '_sqrt', '_test', '_test_generator', '_urandom', '_warn', 'betavariate', 'choice', 'choices', 'expovariate', 'gammavariate', 'gauss', 'getrandbits', 'getstate', 'lognormvariate', 'normalvariate', 'paretovariate', 'randbytes', 'randint', 'random', 'randrange', 'sample', 'seed', 'setstate', 'shuffle', 'triangular', 'uniform', 'vonmisesvariate', 'weibullvariate']

Generating a random number using the random() method:

The random() method returns a random floating-point number in the half-open interval [0,1), meaning it includes 0 but excludes 1.

Example:

# Import the random module
import random

# Generate a random number
print(random.random())

The output of the above example could be:

0.4784904215869241

The seed() method:

The seed() method changes the seed of the random number generator. You can call this method before using other functions from the random module.

Example:

#!/usr/bin/python3
import random

random.seed()
print("Random number with default seed:", random.random())
print("Random number with default seed:", random.random())

random.seed(10)
print("Random number with seed 10:", random.random())
random.seed(10)
print("Random number with seed 10 again:", random.random())

random.seed("hello", 2)
print("Random number with string seed:", random.random())

The output will be:

Random number with default seed: 0.7908102856355441
Random number with default seed: 0.81038961519195
Random number with seed 10: 0.5714025946899135
Random number with seed 10 again: 0.5714025946899135
Random number with string seed: 0.3537754404730722

`random` Module Methods

The random module provides the following methods:

Method	Description
`seed()`	Initializes the random number generator.
`getstate()`	Returns an object capturing the current internal state of the generator.
`setstate()`	Restores the generator's internal state to what was captured by `getstate()`.
`getrandbits(k)`	Returns a Python integer with `k` random bits.
`randrange()`	Returns a randomly selected element from `range(start, stop, step)`.
`randint(a, b)`	Returns a random integer N such that `a <= N <= b`.
`choice(seq)`	Returns a random element from a non-empty sequence `seq`. Raises `IndexError` if `seq` is empty.
`choices(population, ...)`	Returns a list of `k` elements selected from the population.
`shuffle(x[, random])`	Shuffles the sequence `x` in place.
`sample(population, k, ...)`	Returns a list of `k` unique elements randomly chosen from the population.
`random()`	Returns a random floating-point number in the range [0.0, 1.0).
`uniform(a, b)`	Returns a random floating-point number `N` such that `a <= N <= b`.
`triangular(low, ...)`	Returns a random floating-point number `N` between `low` and `high` with the specified `mode`.
`betavariate(alpha, beta)`	Beta distribution. The range of returned values is between 0 and 1.
`expovariate(lambd)`	Exponential distribution. The parameter `lambd` is `1.0 / desired_mean`.
`gammavariate(alpha, beta)`	Gamma distribution (not to be confused with the Gamma function). Parameters must satisfy `alpha > 0` and `beta > 0`.
`gauss(mu, sigma)`	Gaussian (Normal) distribution. `mu` is the mean and `sigma` is the standard deviation.
`lognormvariate(mu, sigma)`	Log-normal distribution.
`normalvariate(mu, sigma)`	Normal distribution.
`vonmisesvariate(mu, kappa)`	von Mises distribution.
`paretovariate(alpha)`	Pareto distribution.
`weibullvariate(alpha, beta)`	Weibull distribution.