Detailed explanation of Python's random module

This article mainly introduces the relevant content of Python's random module, which has certain reference value. Friends who need it can refer to it. I hope it can help everyone.

random module<br>

is used to generate pseudo-random numbers<br>

Truly random numbers (or random events) are randomly generated in a certain generation process according to the distribution probability shown in the experimental process, 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. <br>

As long as the user or third party does not set the random seed, the random seed comes from the system clock by default. <br>

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<br>

random.seed(a=None, version=2)<br>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()<br>Returns an object of the internal state of the current generator

random.setstate(state)<br>Pass in a state object previously obtained using the getstate method to restore the generator to this state.

random.getrandbits(k)<br>Returns a Python integer (decimal) not larger than K bits. For example, k=10, the result is between 0~2^10 Integer.

2. Methods for integers<br>

##random.randrange(stop)<br>

random.randrange(start, stop[, step])Equivalent to choice(range(start, stop, step)), but does not actually create a range object. <br>

random.randint(a, b)Returns a random integer N where a

3. Methods for sequence class structures<br>

random. choice(seq)Randomly select an element from the non-empty sequence seq. If seq is empty, an IndexError exception will pop up. <br>

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. <br>

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. <br>

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. <br>

4. True value distribution<br>

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)<br>

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. <br>

random.triangular(low, high, mode)Returns a random number from a triangular distribution with low

random.betavariate(alpha, beta)Beta distribution. The returned result is between 0 and 1<br>

random.expovariate(lambd)Exponential distribution<br>

random.gammavariate(alpha, beta)Gamma distribution<br>

random.gauss(mu, sigma)<br>Gaussian distribution

random.lognormvariate(mu, sigma) Lognormal distribution<br>

random.normalvariate(mu, sigma)Normal distribution<br>

random.vonmisesvariate( mu, kappa)Kappa distribution<br>

random.paretovariate(alpha)<br>Pareto distribution

random.weibullvariate (alpha, beta)

5. Optional generator<br>

class random.SystemRandom( [seed])Use the os.urandom() method to generate random numbers. The source code is provided by the operating system. Not all systems may support it<br>

6. Typical example of

>>> random()               # 随机浮点数: 0.0 <= x < 1.0
0.37444887175646646

>>> uniform(2.5, 10.0)          # 随机浮点数: 2.5 <= x < 10.0
3.1800146073117523

>>> randrange(10)            # 0-9的整数：
7

>>> randrange(0, 101, 2)         # 0-100的偶数
26

>>> choice([&#39;win&#39;, &#39;lose&#39;, &#39;draw&#39;])   # 从序列随机选择一个元素
&#39;draw&#39;

>>> deck = &#39;ace two three four&#39;.split()
>>> shuffle(deck)            # 对序列进行洗牌，改变原序列
>>> deck
[&#39;four&#39;, &#39;two&#39;, &#39;ace&#39;, &#39;three&#39;]

>>> sample([10, 20, 30, 40, 50], k=4)  # 不改变原序列的抽取指定数目样本，并生成新序列
[40, 10, 50, 30]

>>> # 6次旋转红黑绿*(带权重可重复的取样)，不破坏原序列
>>> choices([&#39;red&#39;, &#39;black&#39;, &#39;green&#39;], [18, 18, 2], k=6)
[&#39;red&#39;, &#39;green&#39;, &#39;black&#39;, &#39;black&#39;, &#39;red&#39;, &#39;black&#39;]

>>> # 德州扑克计算概率Deal 20 cards without replacement from a deck of 52 playing cards
>>> # and determine the proportion of cards with a ten-value
>>> # (a ten, jack, queen, or king).
>>> deck = collections.Counter(tens=16, low_cards=36)
>>> seen = sample(list(deck.elements()), k=20)
>>> seen.count(&#39;tens&#39;) / 20
0.15

>>> # 模拟概率Estimate the probability of getting 5 or more heads from 7 spins
>>> # of a biased coin that settles on heads 60% of the time.
>>> trial = lambda: choices(&#39;HT&#39;, cum_weights=(0.60, 1.00), k=7).count(&#39;H&#39;) >= 5
>>> sum(trial() for i in range(10000)) / 10000
0.4169

>>> # Probability of the median of 5 samples being in middle two quartiles
>>> trial = lambda : 2500 <= sorted(choices(range(10000), k=5))[2] < 7500
>>> sum(trial() for i in range(10000)) / 10000
0.7958

The following is a program to generate a random 4-digit verification code containing the uppercase letters A-Z and the numbers 0-9

import random
 
checkcode = ''
for i in range(4):
  current = random.randrange(0,4)
  if current != i:
    temp = chr(random.randint(65,90))
  else:
    temp = random.randint(0,9)
  checkcode += str(temp)
print(checkcode)

The following is the code to generate a random sequence of letters and numbers of a specified length:

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import random, string

def gen_random_string(length):
  # 数字的个数随机产生
  num_of_numeric = random.randint(1,length-1)
  # 剩下的都是字母
  num_of_letter = length - num_of_numeric
  # 随机生成数字
  numerics = [random.choice(string.digits) for i in range(num_of_numeric)]
  # 随机生成字母
  letters = [random.choice(string.ascii_letters) for i in range(num_of_letter)]
  # 结合两者
  all_chars = numerics + letters
  # 洗牌
  random.shuffle(all_chars)
  # 生成最终字符串
  result = ''.join([i for i in all_chars])
  return result

if __name__ == '__main__':
  print(gen_random_string(64))

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