这个答案漏掉了一个方面:OP要求所有的组合……不仅仅是长度为r的组合。

所以你要么要遍历所有长度为L的循环:

import itertools

stuff = [1, 2, 3]
for L in range(len(stuff) + 1):
    for subset in itertools.combinations(stuff, L):
        print(subset)

或者——如果你想变得时髦(或者让那些在你之后阅读你的代码的人动脑筋)——你可以生成“组合()”生成器链，并遍历它:

from itertools import chain, combinations
def all_subsets(ss):
    return chain(*map(lambda x: combinations(ss, x), range(0, len(ss)+1)))

for subset in all_subsets(stuff):
    print(subset)

我想我应该为那些寻求答案的人添加这个函数，而不需要导入itertools或任何其他额外的库。

def powerSet(items):
    """
    Power set generator: get all possible combinations of a list’s elements

    Input:
        items is a list
    Output:
        returns 2**n combination lists one at a time using a generator 

    Reference: edx.org 6.00.2x Lecture 2 - Decision Trees and dynamic programming
    """

    N = len(items)
    # enumerate the 2**N possible combinations
    for i in range(2**N):
        combo = []
        for j in range(N):
            # test bit jth of integer i
            if (i >> j) % 2 == 1:
                combo.append(items[j])
        yield combo

简单Yield Generator用法:

for i in powerSet([1,2,3,4]):
    print (i, ", ",  end="")

以上用法示例的输出:

[], [1], [2], [1, 2], [3], [1, 3], [2, 3], [1, 2, 3], [4]. [1, 4], [2, 4], [1, 2, 4], [3, 4], [1, 3, 4], [2, 3, 4], [1, 2, 3, 4],

我同意Dan H的观点，Ben确实要求所有的组合。itertools.combination()不会给出所有的组合。

另一个问题是，如果输入iterable很大，返回一个生成器而不是列表中的所有内容可能会更好:

iterable = range(10)
for s in xrange(len(iterable)+1):
  for comb in itertools.combinations(iterable, s):
    yield comb

不使用itertools:

def combine(inp):
    return combine_helper(inp, [], [])


def combine_helper(inp, temp, ans):
    for i in range(len(inp)):
        current = inp[i]
        remaining = inp[i + 1:]
        temp.append(current)
        ans.append(tuple(temp))
        combine_helper(remaining, temp, ans)
        temp.pop()
    return ans


print(combine(['a', 'b', 'c', 'd']))

In comments under the highly upvoted answer by @Dan H, mention is made of the powerset() recipe in the itertools documentation—including one by Dan himself. However, so far no one has posted it as an answer. Since it's probably one of the better if not the best approach to the problem—and given a little encouragement from another commenter, it's shown below. The function produces all unique combinations of the list elements of every length possible (including those containing zero and all the elements).

注意:如果略有不同，目标是只获得唯一元素的组合，将s = list(iterable)一行更改为s = list(set(iterable))以消除任何重复的元素。无论如何，iterable最终被转换为列表这一事实意味着它将与生成器一起工作(与其他几个答案不同)。

from itertools import chain, combinations

def powerset(iterable):
    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [1, 2, 3]
for i, combo in enumerate(powerset(stuff), 1):
    print('combo #{}: {}'.format(i, combo))

输出:

combo #1: ()
combo #2: (1,)
combo #3: (2,)
combo #4: (3,)
combo #5: (1, 2)
combo #6: (1, 3)
combo #7: (2, 3)
combo #8: (1, 2, 3)

如果你不想使用组合库，这里是解决方案:

nums = [1,2,3]
p = [[]]
fnl = [[],nums]

for i in range(len(nums)):
    for j in range(i+1,len(nums)):
        p[-1].append([i,j])

for i in range(len(nums)-3):
    p.append([])
    for m in p[-2]:
        p[-1].append(m+[m[-1]+1])

for i in p:
    for j in i:
        n = []
        for m in j:
            if m < len(nums):
                n.append(nums[m])
        if n not in fnl:
            fnl.append(n)

for i in nums:
    if [i] not in fnl:
        fnl.append([i])

print(fnl)

输出:

[[], [1, 2, 3], [1, 2], [1, 3], [2, 3], [1], [2], [3]]