简单的生成器版本，在内存中保存尽可能少的值，并且只调用pred一次:

from collections import deque
from typing import Callable, TypeVar, Iterable
_T = TypeVar('_T')

def iter_split(pred: Callable[[_T], bool],
               iterable: Iterable[_T]) -> tuple[Iterable[_T], Iterable[_T]]:
    """Split an iterable into two iterables based on a predicate.
    
    The predicate will only be called once per element.
    
    Returns:
        A tuple of two iterables, the first containing all elements for which
        the predicate returned True, the second containing all elements for
        which the predicate returned False.
    """
    iterator = iter(iterable)
    true_values: deque[_T] = deque()
    false_values: deque[_T] = deque()
    
    def true_generator():
        while True:
            while true_values:
                yield true_values.popleft()
            
            for item in iterator:
                if pred(item):
                    yield item
                    break
                false_values.append(item)
            else:
                break
            
    def false_generator():
        while True:
            while false_values:
                yield false_values.popleft()
            
            for item in iterator:
                if not pred(item):
                    yield item
                    break
                true_values.append(item)
            else:
                break

    return true_generator(), false_generator()

就我个人而言，我喜欢你引用的版本，假设你已经有了一个好的列表。如果没有，就像这样:

good = filter(lambda x: is_good(x), mylist)
bad = filter(lambda x: not is_good(x), mylist)

当然，这真的非常类似于使用列表理解，就像你最初做的，但用一个函数而不是一个查找:

good = [x for x in mylist if is_good(x)]
bad  = [x for x in mylist if not is_good(x)]

总的来说，我发现列表推导式的美学非常令人满意。当然，如果您实际上不需要保留顺序，也不需要重复，那么在集合上使用交集和差分方法也会很好。

还有另一个答案，简短但“邪恶”(用于理解列表的副作用)。

digits = list(range(10))
odd = [x.pop(i) for i, x in enumerate(digits) if x % 2]

>>> odd
[1, 3, 5, 7, 9]

>>> digits
[0, 2, 4, 6, 8]

例如，按偶数和奇数拆分列表

arr = range(20)
even, odd = reduce(lambda res, next: res[next % 2].append(next) or res, arr, ([], []))

或者概括地说:

def split(predicate, iterable):
    return reduce(lambda res, e: res[predicate(e)].append(e) or res, iterable, ([], []))

优点:

最短路径 Predicate对每个元素只应用一次

缺点

需要函数式编程范例的知识

good.append(x) if x in goodvals else bad.append(x)

来自@dansalmo的这个优雅简洁的回答被埋没在评论中，所以我只是把它作为一个答案转发到这里，这样它就能得到应有的重视，尤其是对新读者来说。

完整的例子:

good, bad = [], []
for x in my_list:
    good.append(x) if x in goodvals else bad.append(x)

有时候，列表理解并不是最好的选择!

我根据人们对这个话题的回答做了一个小测试，在一个随机生成的列表上测试。以下是列表的生成(可能有更好的方法，但这不是重点):

good_list = ('.jpg','.jpeg','.gif','.bmp','.png')

import random
import string
my_origin_list = []
for i in xrange(10000):
    fname = ''.join(random.choice(string.lowercase) for i in range(random.randrange(10)))
    if random.getrandbits(1):
        fext = random.choice(good_list)
    else:
        fext = "." + ''.join(random.choice(string.lowercase) for i in range(3))

    my_origin_list.append((fname + fext, random.randrange(1000), fext))

好了

# Parand
def f1():
    return [e for e in my_origin_list if e[2] in good_list], [e for e in my_origin_list if not e[2] in good_list]

# dbr
def f2():
    a, b = list(), list()
    for e in my_origin_list:
        if e[2] in good_list:
            a.append(e)
        else:
            b.append(e)
    return a, b

# John La Rooy
def f3():
    a, b = list(), list()
    for e in my_origin_list:
        (b, a)[e[2] in good_list].append(e)
    return a, b

# Ants Aasma
def f4():
    l1, l2 = tee((e[2] in good_list, e) for e in my_origin_list)
    return [i for p, i in l1 if p], [i for p, i in l2 if not p]

# My personal way to do
def f5():
    a, b = zip(*[(e, None) if e[2] in good_list else (None, e) for e in my_origin_list])
    return list(filter(None, a)), list(filter(None, b))

# BJ Homer
def f6():
    return filter(lambda e: e[2] in good_list, my_origin_list), filter(lambda e: not e[2] in good_list, my_origin_list)

使用cmpthese函数，最好的结果是dbr答案:

f1     204/s  --    -5%   -14%   -15%   -20%   -26%
f6     215/s     6%  --    -9%   -11%   -16%   -22%
f3     237/s    16%    10%  --    -2%    -7%   -14%
f4     240/s    18%    12%     2%  --    -6%   -13%
f5     255/s    25%    18%     8%     6%  --    -8%
f2     277/s    36%    29%    17%    15%     9%  --