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## random

 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