Which version of Python is the random module in?

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

3. Methods for sequence class structures

random.choice(seq)

Never empty Randomly select an element from the sequence seq. If seq is empty, an IndexError exception will pop up.

random.choices(population, weights=None, *, cum_weights=None, k=1)

New in version 3.6. K elements are randomly selected from the population cluster. Weights is a relative weight list, cum_weights is the cumulative weight, and the two parameters cannot exist at the same time.

random.shuffle(x[, random])

Randomly shuffle the order of elements in sequence x. It can only be used for mutable sequences. For immutable sequences, please use the sample() method below.

random.sample(population, k)

Randomly extract K non-repeating elements from the population sample or set to form a new sequence. Often used for random sampling without repetition. What is returned is a new sequence without destroying the original sequence. To randomly draw a certain number of integers from an integer range, use a method like sample(range(10000000), k=60), which is very efficient and space-saving. If k is greater than the length of population, a ValueError exception will pop up.

4. True Value Distribution

The most high-end function of the random module is actually here.

random.random()

Returns a floating point number between left closed and right open [0.0, 1.0)

random.uniform(a, b)

Returns a floating point number between a and b. If a>b, 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

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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