Python random Module

Python randomThe module is mainly used to generate random numbers.

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

To userandomthe function must be imported first:

import random

View the contents of the random module:

Examples

>>> 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']

Next we userandom()method returns a random number, which is withinhalf-open interval [0,1)range, including 0 but not including 1.

Examples

# Import the random package
import random

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

The output of the above example is:

0.4784904215869241

seed()The method changes the seed of the random number generator, and you can call this function before calling other random module functions.

Examples

#!/usr/bin/python3
import random

random.seed()
print (Generate a random number using the default seed:, random.random())
print (Generate a random number using the default seed:, random.random())

random.seed(10)
print (Generate a random number using the integer 10 as the seed:, random.random())
random.seed(10)
print (Generate a random number using the integer 10 as the seed:, random.random())

random.seed("hello",2)
print (Generate a random number using a string seed:, random.random())

The output after running the above example is:

使用默认种子生成随机数： 0.7908102856355441
使用默认种子生成随机数： 0.81038961519195
使用整数 10 种子生成随机数： 0.5714025946899135
使用整数 10 种子生成随机数： 0.5714025946899135
使用字符串种子生成随机数： 0.3537754404730722

random Module Methods

The random module methods are as follows:

Method Description
seed() Initialize the random number generator
getstate() Return an object capturing the current internal state of the generator.
setstate() state should have been obtained from a previous call to getstate(), and setstate() restores the generator's internal state to what it was when getstate() was called.
getrandbits(k) Return a non-negative Python integer with k random bits. This method is provided with the MersenneTwister generator, and some other generators may also provide it as an optional part of the API. When possible, getrandbits() enables randrange() to handle arbitrarily large ranges.
randrange() Return a randomly selected element from range(start, stop, step).
randint(a, b) Return a random integer N such that a <= N <= b.
choice(seq) Return a random element from the non-empty sequence seq. If seq is empty, raise IndexError.
choices(population, weights=None, *, cum_weights=None, k=1) Return a list of k elements chosen with replacement from population. If population is empty, raise IndexError.
shuffle(x[, random]) Shuffle the sequence x randomly.
sample(population, k, *, counts=None) Return a k-length list of unique elements chosen from the population sequence or set. Used for random sampling without replacement.
random() Return the next random floating-point number in the range [0.0, 1.0).
uniform() Return a random floating-point number N such that a <= N <= b when a <= b, and b <= N <= a when b < a.
triangular(low, high, mode) Return a random floating-point number N such that low <= N <= high and with a specified mode between these bounds. The low and high bounds default to zero and one. The mode parameter defaults to the midpoint between the boundaries, giving a symmetric distribution.
betavariate(alpha, beta) Beta distribution. The conditions on the parameters are alpha > 0 and beta > 0. The returned value ranges between 0 and 1.
expovariate(lambd) Exponential distribution. lambd is 1.0 divided by the desired mean, and it should be non-zero.
gammavariate() Gamma distribution (not the gamma function). The conditions on the parameters are alpha > 0 and beta > 0.
gauss(mu, sigma) Normal distribution, also known as Gaussian distribution. mu is the mean, and sigma is the standard deviation. This function is slightly faster than the normalvariate() function defined below.
lognormvariate(mu, sigma) Log-normal distribution. If you take the natural logarithm of this distribution, you will get a normal distribution with mean mu and standard deviation sigma. mu can be any value, and sigma must be greater than zero.
normalvariate(mu, sigma) Normal distribution. mu is the mean, and sigma is the standard deviation.
vonmisesvariate(mu, kappa) Von Mises distribution. mu is the average angle, expressed in radians, between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.
paretovariate(alpha) Pareto distribution. alpha is the shape parameter.
weibullvariate(alpha, beta) Weibull distribution. alpha is the scale parameter, and beta is the shape parameter.
Other extensions