将代码从python 3.6复制到Mark Dickinson的建议

from itertools import accumulate as _accumulate,repeat as _repeat
from bisect import bisect as _bisect
import random
def choices(population,weights=None,*,cum_weights=None,k=1):
    """Return a k sized list of population elements chosen with replacement.
    If the relative weights or cumulative weights are not specified,the selections are made with equal probability.
    """
    n = len(population)
    if cum_weights is None:
        if weights is None:
            _int = int
            n += 0.0    # convert to float for a small speed improvement
            return [population[_int(random.random() * n)] for i in _repeat(None,k)]
        cum_weights = list(_accumulate(weights))
    elif weights is not None:
        raise TypeError('Cannot specify both weights and cumulative weights')
    if len(cum_weights) != n:
        raise ValueError('The number of weights does not match the population')
    bisect = _bisect
    total = cum_weights[-1] + 0.0   # convert to float
    hi = n - 1
    return [population[bisect(cum_weights,random.random() * total,hi)]
            for i in _repeat(None,k)]

现在您可以安心使用choices函数了！