详解Python中打乱列表顺序random.shuffle()如何使用

# shuffle()使用样例
import random

x = [i for i in range(10)]
print(x)
random.shuffle(x)
print(x)

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]

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):