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random module is used to generate pseudo-random numbers. Source code location: Lib/random.py
The true random number (or random event) behaves according to the experimental process during a certain generation process. Distribution probabilities are generated randomly, and the results are unpredictable and invisible. The random function in the computer is simulated according to a certain algorithm, and the result is certain and visible. We can assume that the probability of this foreseeable outcome is 100%. Therefore, the "random numbers" generated by the computer random function are not random, but pseudo-random numbers.
#The pseudo-random number of the computer is a value calculated by a random seed according to a certain calculation method. Therefore, as long as the calculation method is certain and the random seed is certain, the random numbers generated are fixed.
As long as the user or third party does not set the random seed, the random seed comes from the system clock by default.
This library of Python uses a common algorithm at the bottom. After long-term testing, its reliability cannot be said, but it must not be used for password-related functions.
1. Basic method
random.seed(a=None, version=2)
Initialize the pseudo-random number generator. If a is not provided or a=None, the system time is used as the seed. If a is an integer, it is used as the seed.
random.getstate()
Returns an object of the internal state of the current generator
random.setstate(state)
Pass in a previous exploit The state object obtained by the getstate method restores the generator to this state.
random.getrandbits(k)
Returns a Python integer (decimal) not larger than K bits. For example, k=10, the result is an integer between 0~2^10.
2. Methods for integers
random.randrange(stop) random.randrange(start, stop[, step])
is equivalent to choice(range(start, stop, step)), but does not actually create a range object.
random.randint(a, b)
Returns a random integer N where a d2dff0ecb45a7a4c0307a7bc6fe845fdb, it is a floating point number between b and a. Both a and b here may appear in the result.
random.triangular(low, high, mode)
Returns a triangular distributed random number with low <= N <=high. The parameter mode specifies the position where the mode appears.
random.betavariate(alpha, beta)
Beta distribution. The returned result is between 0 and 1
random.expovariate(lambd)
Exponential distribution
random.gammavariate(alpha, beta)
GA Horse distribution
random.gauss(mu, sigma)
Gaussian distribution
random.lognormvariate(mu, sigma)
Lognormal distribution
random.normalvariate(mu, sigma)
Normal distribution
random.vonmisesvariate(mu, kappa)
Kappa distribution
random.paretovariate(alpha)
Pareto distribution
random.weibullvariate(alpha, beta)
5. Optional generator
class random.SystemRandom([seed])
A class that uses the os.urandom() method to generate random numbers. The source code is provided by the operating system. Not all systems may support it
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