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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 b0e2df88c81c22b68aa55687803d59bbb, 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 <= N <=high. The mode parameter specifies the position where the mode appears. <br>
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(['win', 'lose', 'draw']) # 从序列随机选择一个元素 'draw' >>> deck = 'ace two three four'.split() >>> shuffle(deck) # 对序列进行洗牌，改变原序列 >>> deck ['four', 'two', 'ace', 'three'] >>> 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(['red', 'black', 'green'], [18, 18, 2], k=6) ['red', 'green', 'black', 'black', 'red', 'black'] >>> # 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('tens') / 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('HT', cum_weights=(0.60, 1.00), k=7).count('H') >= 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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