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random.betavariate() Beta distribution
random.choice() Return a random element from the non-empty sequence seq
random.expovariate() Exponential distribution
random.gammavariate() Gamma distribution
random.gauss() Gaussian distribution
random.getrandbits() Returns a Python integer with k random bits
random.getstate() Return an object capturing the current internal state of the generator
random.lognormvariate() Log normal distribution
random.normalvariate() Normal distribution
random.paretovariate() Pareto distribution
random.randint() Return a random integer N such that a <= N <= b
random.random() Return the next random floating point number in the range [0
random.randrange() Return a randomly selected element from range(start, stop, step)
random.sample() Return a k length list of unique elements chosen from the population sequence or set
random.seed() Initialize the random number generator
random.setstate() state should have been obtained from a previous call to getstate(), and setstate() restores the internal state of the generator to what it was at the time getstate() was called
random.shuffle() Shuffle the sequence x in place
random.triangular() Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds
random.uniform() Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a
random.vonmisesvariate() mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero
random.weibullvariate() Weibull distribution