Module « random » Python 3.11.3

Classe « Random »

Informations générales

Héritage

builtins.object
    Random
        Random

Définition

class Random(Random):

help(Random)

Random number generator base class used by bound module functions.

    Used to instantiate instances of Random to get generators that don't
    share state.

    Class Random can also be subclassed if you want to use a different basic
    generator of your own devising: in that case, override the following
    methods:  random(), seed(), getstate(), and setstate().
    Optionally, implement a getrandbits() method so that randrange()
    can cover arbitrarily large ranges.

Constructeur(s)

Signature du constructeur	Description
__new__(args, *kwargs)	Create and return a new object. See help(type) for accurate signature. _{[extrait de __new__.__doc__]}
__init__(self, x=None)	Initialize an instance. _{[extrait de __init__.__doc__]}

Liste des attributs statiques

Nom de l'attribut	Valeur
VERSION	3

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Liste des méthodes

Toutes les méthodes Méthodes d'instance Méthodes statiques

Signature de la méthode	Description
__getstate__(self)
__reduce__(self)
__setstate__(self, state)
betavariate(self, alpha, beta)	Beta distribution. _{[extrait de betavariate.__doc__]}
choice(self, seq)	Choose a random element from a non-empty sequence. _{[extrait de choice.__doc__]}
choices(self, population, weights=None, *, cum_weights=None, k=1)	Return a k sized list of population elements chosen with replacement. _{[extrait de choices.__doc__]}
expovariate(self, lambd)	Exponential distribution. _{[extrait de expovariate.__doc__]}
gammavariate(self, alpha, beta)	Gamma distribution. Not the gamma function! _{[extrait de gammavariate.__doc__]}
gauss(self, mu=0.0, sigma=1.0)	Gaussian distribution. _{[extrait de gauss.__doc__]}
getrandbits(self, k)	getrandbits(k) -> x. Generates an int with k random bits. _{[extrait de getrandbits.__doc__]}
getstate(self)	Return internal state; can be passed to setstate() later. _{[extrait de getstate.__doc__]}
lognormvariate(self, mu, sigma)	Log normal distribution. _{[extrait de lognormvariate.__doc__]}
normalvariate(self, mu=0.0, sigma=1.0)	Normal distribution. _{[extrait de normalvariate.__doc__]}
paretovariate(self, alpha)	Pareto distribution. alpha is the shape parameter. _{[extrait de paretovariate.__doc__]}
randbytes(self, n)	Generate n random bytes. _{[extrait de randbytes.__doc__]}
randint(self, a, b)	Return random integer in range [a, b], including both end points. _{[extrait de randint.__doc__]}
random(self)	random() -> x in the interval [0, 1). _{[extrait de random.__doc__]}
randrange(self, start, stop=None, step=1)	Choose a random item from range(stop) or range(start, stop[, step]). _{[extrait de randrange.__doc__]}
sample(self, population, k, *, counts=None)	Chooses k unique random elements from a population sequence. _{[extrait de sample.__doc__]}
seed(self, a=None, version=2)	Initialize internal state from a seed. _{[extrait de seed.__doc__]}
setstate(self, state)	Restore internal state from object returned by getstate(). _{[extrait de setstate.__doc__]}
shuffle(self, x)	Shuffle list x in place, and return None. _{[extrait de shuffle.__doc__]}
triangular(self, low=0.0, high=1.0, mode=None)	Triangular distribution. _{[extrait de triangular.__doc__]}
uniform(self, a, b)	Get a random number in the range [a, b) or [a, b] depending on rounding. _{[extrait de uniform.__doc__]}
vonmisesvariate(self, mu, kappa)	Circular data distribution. _{[extrait de vonmisesvariate.__doc__]}
weibullvariate(self, alpha, beta)	Weibull distribution. _{[extrait de weibullvariate.__doc__]}