Python中打乱列表顺序 random.shuffle()的使用方法

港控/mmm° 2022-05-04 15:00 554阅读 0赞

之前自己一直使用random中 randint生成随机数以及使用for将列表中的数据遍历一次。  
现在有个需求需要将列表的次序打乱，或者也可以这样理解：  
`【需求】将一个容器中的数据每次随机逐个遍历一遍。`  
random.shuffle()方法提供了完美的解决方案。  
**不会生成新的列表，只是将原列表的次序打乱**

# shuffle()使用样例
    import random
    
    x = [i for i in range(10)]
    print(x)
    random.shuffle(x)
    print(x)

源码及注释（个人翻译注释）

def shuffle(self, x, random=None):
        """Shuffle list x in place, and return None. 原位打乱列表，不生成新的列表。 Optional argument random is a 0-argument function returning a random float in [0.0, 1.0); if it is the default None, the standard random.random will be used. 可选参数random是一个从0到参数的函数，返回[0.0,1.0)中的随机浮点； 如果random是缺省值None，则将使用标准的random.random()。 """
    
        if random is None:
            randbelow = self._randbelow
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = randbelow(i + 1)
                x[i], x[j] = x[j], x[i]
        else:
            _int = int
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = _int(random() * (i + 1))
                x[i], x[j] = x[j], x[i]

random 中其他的方法

class Random(_random.Random):
    
        ## -------------------- integer methods -------------------
        def randrange(self, start, stop=None, step=1, _int=int):
    
        def randint(self, a, b):
    
        def _randbelow(self, n, int=int, maxsize=1 << BPF, type=type,
                       Method=_MethodType, BuiltinMethod=_BuiltinMethodType):
    
        ## -------------------- sequence methods -------------------
        def choice(self, seq):
    
        def shuffle(self, x, random=None):
    
        def sample(self, population, k):
    
        def choices(self, population, weights=None, *, cum_weights=None, k=1):
    
        ## -------------------- uniform distribution -------------------
        def uniform(self, a, b):
    
        ## -------------------- triangular --------------------
        def triangular(self, low=0.0, high=1.0, mode=None):
    
        ## -------------------- normal distribution --------------------
        def normalvariate(self, mu, sigma):
    
        ## -------------------- lognormal distribution --------------------
        def lognormvariate(self, mu, sigma):
    
        ## -------------------- exponential distribution --------------------
        def expovariate(self, lambd):
    
        ## -------------------- von Mises distribution --------------------
        def vonmisesvariate(self, mu, kappa):
    
        ## -------------------- gamma distribution --------------------
        def gammavariate(self, alpha, beta):
    
        ## -------------------- Gauss (faster alternative) --------------------
        def gauss(self, mu, sigma):
    
        def betavariate(self, alpha, beta):
    
        ## -------------------- Pareto --------------------
        def paretovariate(self, alpha):
    
        ## -------------------- Weibull --------------------
        def weibullvariate(self, alpha, beta):