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):
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