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],

flag = 0
requiredCals =12
from itertools import chain, combinations

def powerset(iterable):
    s = list(iterable)  # allows duplicate elements
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

stuff = [2,9,5,1,6]
for i, combo in enumerate(powerset(stuff), 1):
    if(len(combo)>0):
        #print(combo , sum(combo))
        if(sum(combo)== requiredCals):
            flag = 1
            break
if(flag==1):
    print('True')
else:
    print('else')

你可以使用以下简单的代码在Python中生成列表的所有组合:

import itertools

a = [1,2,3,4]
for i in xrange(0,len(a)+1):
   print list(itertools.combinations(a,i))

结果将是:

[()]
[(1,), (2,), (3,), (4,)]
[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
[(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]
[(1, 2, 3, 4)]

还可以使用more_itertools包中的powerset函数。

from more_itertools import powerset

l = [1,2,3]
list(powerset(l))

# [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]

我们也可以验证，它满足OP的要求

from more_itertools import ilen

assert ilen(powerset(range(15))) == 32_768

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)

我喜欢这个问题，因为有很多方法来实现它。我决定为未来创造一个参考答案。

在生产中使用什么?

intertools的文档有一个独立的例子，为什么不在你的代码中使用它呢?一些人建议使用more_itertools。Powerset，但它具有完全相同的实现!如果我是你，我不会为一个小东西安装整个软件包。也许这是最好的方法:

import itertools

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

其他可能的方法

方法0:使用组合

import itertools

def subsets(nums):
    result = []
    for i in range(len(nums) + 1):
        result += itertools.combinations(nums, i)
    return result

方法1:简单的递归

def subsets(nums):
    result = []

    def powerset(alist, index, curr):
        if index == len(alist):
            result.append(curr)
            return

        powerset(alist, index + 1, curr + [alist[index]])
        powerset(alist, index + 1, curr)

    powerset(nums, 0, [])
    return result

方法2:回溯

def subsets(nums):
    result = []

    def backtrack(index, curr, k):
        if len(curr) == k:
            result.append(list(curr))
            return
        for i in range(index, len(nums)):
            curr.append(nums[i])
            backtrack(i + 1, curr, k)
            curr.pop()

    for k in range(len(nums) + 1):
        backtrack(0, [], k)
    return result

or

def subsets(nums):
    result = []

    def dfs(nums, index, path, result):
        result.append(path)
        for i in range(index, len(nums)):
            dfs(nums, i + 1, path + [nums[i]], result)

    dfs(nums, 0, [], result)
    return result

方法3:位掩码

def subsets(nums):
    res = []
    n = len(nums)
    for i in range(1 << n):
        aset = []
        for j in range(n):
            value = (1 << j) & i  # value = (i >> j) & 1
            if value:
                aset.append(nums[j])
        res.append(aset)
    return res

或者(不是位掩码，直觉上是2^n个子集)

def subsets(nums):
    subsets = []
    expected_subsets = 2 ** len(nums)

    def generate_subset(subset, nums):
        if len(subsets) >= expected_subsets:
            return
        if len(subsets) < expected_subsets:
            subsets.append(subset)
        for i in range(len(nums)):
            generate_subset(subset + [nums[i]], nums[i + 1:])

    generate_subset([], nums)
    return subsets

方法4:级联

def subsets(nums):
    result = [[]]
    for i in range(len(nums)):
        for j in range(len(result)):
            subset = list(result[j])
            subset.append(nums[i])
            result.append(subset)
    return result