This answer missed one aspect: the OP asked for ALL combinations… not just combinations of length "r".

So you’d either have to loop through all lengths "L":

import itertools

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

Or — if you want to get snazzy (or bend the brain of whoever reads your code after you) — you can generate the chain of "combinations()" generators, and iterate through that:

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)

Here’s a lazy one-liner, also using itertools:

from itertools import compress, product

def combinations(items):
    return ( set(compress(items,mask)) for mask in product(*[[0,1]]*len(items)) )
    # alternative:                      ...in product([0,1], repeat=len(items)) )

Main idea behind this answer: there are 2^N combinations — same as the number of binary strings of length N. For each binary string, you pick all elements corresponding to a “1”.

items=abc * mask=###
 |
 V
000 -> 
001 ->   c
010 ->  b
011 ->  bc
100 -> a
101 -> a c
110 -> ab
111 -> abc

Things to consider:

• This requires that you can call len(...) on items (workaround: if items is something like an iterable like a generator, turn it into a list first with items=list(_itemsArg))
• This requires that the order of iteration on items is not random (workaround: don’t be insane)
• This requires that the items are unique, or else {2,2,1} and {2,1,1} will both collapse to {2,1} (workaround: use collections.Counter as a drop-in replacement for set; it’s basically a multiset… though you may need to later use tuple(sorted(Counter(...).elements())) if you need it to be hashable)

Demo

>>> list(combinations(range(4)))
[set(), {3}, {2}, {2, 3}, {1}, {1, 3}, {1, 2}, {1, 2, 3}, {0}, {0, 3}, {0, 2}, {0, 2, 3}, {0, 1}, {0, 1, 3}, {0, 1, 2}, {0, 1, 2, 3}]

>>> list(combinations('abcd'))
[set(), {'d'}, {'c'}, {'c', 'd'}, {'b'}, {'b', 'd'}, {'c', 'b'}, {'c', 'b', 'd'}, {'a'}, {'a', 'd'}, {'a', 'c'}, {'a', 'c', 'd'}, {'a', 'b'}, {'a', 'b', 'd'}, {'a', 'c', 'b'}, {'a', 'c', 'b', 'd'}]

Using list comprehension:

def selfCombine( list2Combine, length ):
    listCombined = str( ['list2Combine[i' + str( i ) + ']' for i in range( length )] ).replace( "'", '' ) 
                     + 'for i0 in range(len( list2Combine ) )'
    if length > 1:
        listCombined += str( [' for i' + str( i ) + ' in range( i' + str( i - 1 ) + ', len( list2Combine ) )' for i in range( 1, length )] )
            .replace( "', '", ' ' )
            .replace( "['", '' )
            .replace( "']", '' )

    listCombined = '[' + listCombined + ']'
    listCombined = eval( listCombined )

    return listCombined

list2Combine = ['A', 'B', 'C']
listCombined = selfCombine( list2Combine, 2 )

Output would be:

['A', 'A']
['A', 'B']
['A', 'C']
['B', 'B']
['B', 'C']
['C', 'C']

I agree with Dan H that Ben indeed asked for all combinations. itertools.combinations() does not give all combinations.

Another issue is, if the input iterable is big, it is perhaps better to return a generator instead of everything in a list:

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

Here is one using recursion:

>>> import copy
>>> def combinations(target,data):
...     for i in range(len(data)):
...         new_target = copy.copy(target)
...         new_data = copy.copy(data)
...         new_target.append(data[i])
...         new_data = data[i+1:]
...         print new_target
...         combinations(new_target,
...                      new_data)
...                      
... 
>>> target = []
>>> data = ['a','b','c','d']
>>> 
>>> combinations(target,data)
['a']
['a', 'b']
['a', 'b', 'c']
['a', 'b', 'c', 'd']
['a', 'b', 'd']
['a', 'c']
['a', 'c', 'd']
['a', 'd']
['b']
['b', 'c']
['b', 'c', 'd']
['b', 'd']
['c']
['c', 'd']
['d']

This one-liner gives you all the combinations (between 0 and n items if the original list/set contains n distinct elements) and uses the native method itertools.combinations:

Python 2

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([map(list, combinations(input, i)) for i in range(len(input) + 1)], [])

Python 3

from itertools import combinations

input = ['a', 'b', 'c', 'd']

output = sum([list(map(list, combinations(input, i))) for i in range(len(input) + 1)], [])

The output will be:

[[],
 ['a'],
 ['b'],
 ['c'],
 ['d'],
 ['a', 'b'],
 ['a', 'c'],
 ['a', 'd'],
 ['b', 'c'],
 ['b', 'd'],
 ['c', 'd'],
 ['a', 'b', 'c'],
 ['a', 'b', 'd'],
 ['a', 'c', 'd'],
 ['b', 'c', 'd'],
 ['a', 'b', 'c', 'd']]

Try it online:

http://ideone.com/COghfX

You can generate all combinations of a list in Python using this simple code:

import itertools

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

Result would be:

[()]
[(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)]

This code employs a simple algorithm with nested lists…

# FUNCTION getCombos: To generate all combos of an input list, consider the following sets of nested lists...
#
#           [ [ [] ] ]
#           [ [ [] ], [ [A] ] ]
#           [ [ [] ], [ [A],[B] ],         [ [A,B] ] ]
#           [ [ [] ], [ [A],[B],[C] ],     [ [A,B],[A,C],[B,C] ],                   [ [A,B,C] ] ]
#           [ [ [] ], [ [A],[B],[C],[D] ], [ [A,B],[A,C],[B,C],[A,D],[B,D],[C,D] ], [ [A,B,C],[A,B,D],[A,C,D],[B,C,D] ], [ [A,B,C,D] ] ]
#
#  There is a set of lists for each number of items that will occur in a combo (including an empty set).
#  For each additional item, begin at the back of the list by adding an empty list, then taking the set of
#  lists in the previous column (e.g., in the last list, for sets of 3 items you take the existing set of
#  3-item lists and append to it additional lists created by appending the item (4) to the lists in the
#  next smallest item count set. In this case, for the three sets of 2-items in the previous list. Repeat
#  for each set of lists back to the initial list containing just the empty list.
#

def getCombos(listIn = ['A','B','C','D','E','F'] ):
    listCombos = [ [ [] ] ]     # list of lists of combos, seeded with a list containing only the empty list
    listSimple = []             # list to contain the final returned list of items (e.g., characters)

    for item in listIn:
        listCombos.append([])   # append an emtpy list to the end for each new item added
        for index in xrange(len(listCombos)-1, 0, -1):  # set the index range to work through the list
            for listPrev in listCombos[index-1]:        # retrieve the lists from the previous column
                listCur = listPrev[:]                   # create a new temporary list object to update
                listCur.append(item)                    # add the item to the previous list to make it current
                listCombos[index].append(listCur)       # list length and append it to the current list

                itemCombo = ''                          # Create a str to concatenate list items into a str
                for item in listCur:                    # concatenate the members of the lists to create
                    itemCombo += item                   # create a string of items
                listSimple.append(itemCombo)            # add to the final output list

    return [listSimple, listCombos]
# END getCombos()

Below is a “standard recursive answer”, similar to the other similar answer https://stackoverflow.com/a/23743696/711085 . (We don’t realistically have to worry about running out of stack space since there’s no way we could process all N! permutations.)

It visits every element in turn, and either takes it or leaves it (we can directly see the 2^N cardinality from this algorithm).

def combs(xs, i=0):
    if i==len(xs):
        yield ()
        return
    for c in combs(xs,i+1):
        yield c
        yield c+(xs[i],)

Demo:

>>> list( combs(range(5)) )
[(), (0,), (1,), (1, 0), (2,), (2, 0), (2, 1), (2, 1, 0), (3,), (3, 0), (3, 1), (3, 1, 0), (3, 2), (3, 2, 0), (3, 2, 1), (3, 2, 1, 0), (4,), (4, 0), (4, 1), (4, 1, 0), (4, 2), (4, 2, 0), (4, 2, 1), (4, 2, 1, 0), (4, 3), (4, 3, 0), (4, 3, 1), (4, 3, 1, 0), (4, 3, 2), (4, 3, 2, 0), (4, 3, 2, 1), (4, 3, 2, 1, 0)]

>>> list(sorted( combs(range(5)), key=len))
[(), 
 (0,), (1,), (2,), (3,), (4,), 
 (1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2), (4, 0), (4, 1), (4, 2), (4, 3), 
 (2, 1, 0), (3, 1, 0), (3, 2, 0), (3, 2, 1), (4, 1, 0), (4, 2, 0), (4, 2, 1), (4, 3, 0), (4, 3, 1), (4, 3, 2), 
 (3, 2, 1, 0), (4, 2, 1, 0), (4, 3, 1, 0), (4, 3, 2, 0), (4, 3, 2, 1), 
 (4, 3, 2, 1, 0)]

>>> len(set(combs(range(5))))
32

Here is yet another solution (one-liner), involving using the itertools.combinations function, but here we use a double list comprehension (as opposed to a for loop or sum):

def combs(x):
    return [c for i in range(len(x)+1) for c in combinations(x,i)]

Demo:

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

As stated in the documentation

def combinations(iterable, r):
    # combinations('ABCD', 2) --> AB AC AD BC BD CD
    # combinations(range(4), 3) --> 012 013 023 123
    pool = tuple(iterable)
    n = len(pool)
    if r > n:
        return
    indices = list(range(r))
    yield tuple(pool[i] for i in indices)
    while True:
        for i in reversed(range(r)):
            if indices[i] != i + n - r:
                break
        else:
            return
        indices[i] += 1
        for j in range(i+1, r):
            indices[j] = indices[j-1] + 1
        yield tuple(pool[i] for i in indices)


x = [2, 3, 4, 5, 1, 6, 4, 7, 8, 3, 9]
for i in combinations(x, 2):
    print i

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).

Note: If the, subtly different, goal is to obtain only combinations of unique elements, change the line s = list(iterable) to s = list(set(iterable)) to eliminate any duplicate elements. Regardless, the fact that the iterable is ultimately turned into a list means it will work with generators (unlike several of the other answers).

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))

Output:

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)

I thought I would add this function for those seeking an answer without importing itertools or any other extra libraries.

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

Simple Yield Generator Usage:

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

Output from Usage example above:

[] , [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] ,

This is my implementation

def get_combinations(list_of_things):
"""gets every combination of things in a list returned as a list of lists

Should be read : add all combinations of a certain size to the end of a list for every possible size in the
the list_of_things.

"""
list_of_combinations = [list(combinations_of_a_certain_size)
                        for possible_size_of_combinations in range(1,  len(list_of_things))
                        for combinations_of_a_certain_size in itertools.combinations(list_of_things,
                                                                                     possible_size_of_combinations)]
return list_of_combinations

I know it’s far more practical to use itertools to get the all the combinations, but you can achieve this partly with only list comprehension if you so happen to desire, granted you want to code a lot

For combinations of two pairs:

lambda l: [(a, b) for i, a in enumerate(l) for b in l[i+1:]]

And, for combinations of three pairs, it’s as easy as this:

lambda l: [(a, b, c) for i, a in enumerate(l) for ii, b in enumerate(l[i+1:]) for c in l[i+ii+2:]]

The result is identical to using itertools.combinations:

import itertools
combs_3 = lambda l: [
    (a, b, c) for i, a in enumerate(l) 
    for ii, b in enumerate(l[i+1:]) 
    for c in l[i+ii+2:]
]
data = ((1, 2), 5, "a", None)
print("A:", list(itertools.combinations(data, 3)))
print("B:", combs_3(data))
# A: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]
# B: [((1, 2), 5, 'a'), ((1, 2), 5, None), ((1, 2), 'a', None), (5, 'a', None)]

Without using 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']))

Here are two implementations of itertools.combinations

One that returns a list

def combinations(lst, depth, start=0, items=[]):
    if depth <= 0:
        return [items]
    out = []
    for i in range(start, len(lst)):
        out += combinations(lst, depth - 1, i + 1, items + [lst[i]])
    return out

One returns a generator

def combinations(lst, depth, start=0, prepend=[]):
    if depth <= 0:
        yield prepend
    else:
        for i in range(start, len(lst)):
            for c in combinations(lst, depth - 1, i + 1, prepend + [lst[i]]):
                yield c

Please note that providing a helper function to those is advised because the prepend argument is static and is not changing with every call

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[1, 2, 3], [1, 2, 4], [1, 3, 4], [2, 3, 4]]

# get a hold of prepend
prepend = [c for c in combinations([], -1)][0]
prepend.append(None)

print([c for c in combinations([1, 2, 3, 4], 3)])
# [[None, 1, 2, 3], [None, 1, 2, 4], [None, 1, 3, 4], [None, 2, 3, 4]]

This is a very superficial case but better be safe than sorry

If someone is looking for a reversed list, like I was:

stuff = [1, 2, 3, 4]

def reverse(bla, y):
    for subset in itertools.combinations(bla, len(bla)-y):
        print list(subset)
    if y != len(bla):
        y += 1
        reverse(bla, y)

reverse(stuff, 1)

How about this.. used a string instead of list, but same thing.. string can be treated like a list in Python:

def comb(s, res):
    if not s: return
    res.add(s)
    for i in range(0, len(s)):
        t = s[0:i] + s[i + 1:]
        comb(t, res)

res = set()
comb('game', res) 

print(res)

Combination from itertools

import itertools
col_names = ["aa","bb", "cc", "dd"]
all_combinations = itertools.chain(*[itertools.combinations(col_names,i+1) for i,_ in enumerate(col_names)])
print(list(all_combinations))

Without itertools in Python 3 you could do something like this:

def combinations(arr, carry):
    for i in range(len(arr)):
        yield carry + arr[i]
        yield from combinations(arr[i + 1:], carry + arr[i])

where initially carry = "".

This is an approach that can be easily transfered to all programming languages supporting recursion (no itertools, no yield, no list comprehension):

def combs(a):
    if len(a) == 0:
        return [[]]
    cs = []
    for c in combs(a[1:]):
        cs += [c, c+[a[0]]]
    return cs

>>> combs([1,2,3,4,5])
[[], [1], [2], [2, 1], [3], [3, 1], [3, 2], ..., [5, 4, 3, 2, 1]]

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')

from itertools import combinations


features = ['A', 'B', 'C']
tmp = []
for i in range(len(features)):
    oc = combinations(features, i + 1)
    for c in oc:
        tmp.append(list(c))

output

[
 ['A'],
 ['B'],
 ['C'],
 ['A', 'B'],
 ['A', 'C'],
 ['B', 'C'],
 ['A', 'B', 'C']
]

3 functions:

1. all combinations of n elements list
2. all combinations of n elements list where order is not distinct
3. all permutations

import sys

def permutations(a):
    return combinations(a, len(a))

def combinations(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinations(a[:i] + a[i+1:], n-1):
                yield [a[i]] + x

def combinationsNoOrder(a, n):
    if n == 1:
        for x in a:
            yield [x]
    else:
        for i in range(len(a)):
            for x in combinationsNoOrder(a[:i], n-1):
                yield [a[i]] + x
    
if __name__ == "__main__":
    for s in combinations(list(map(int, sys.argv[2:])), int(sys.argv[1])):
        print(s)

You can also use the powerset function from the excellent more_itertools package.

from more_itertools import powerset

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

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

We can also verify, that it meets OP’s requirement

from more_itertools import ilen

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

I’m late to the party but would like to share the solution I found to the same issue:
Specifically, I was looking to do sequential combinations, so for "STAR" I wanted "STAR", "TA", "AR", but not "SR".

lst = [S, T, A, R]
lstCombos = []
for Length in range(0,len(lst)+1):
    for i in lst:
        lstCombos.append(lst[lst.index(i):lst.index(i)+Length])

Duplicates can be filtered with adding in an additional if before the last line:

lst = [S, T, A, R]
lstCombos = []
for Length in range(0,len(lst)+1):
    for i in lst:
         if not lst[lst.index(i):lst.index(i)+Length]) in lstCombos:
             lstCombos.append(lst[lst.index(i):lst.index(i)+Length])

If for some reason this returns blank lists in the output, which happened to me, I added:

for subList in lstCombos:
    if subList = '':
         lstCombos.remove(subList)

If you do not want to use combinations library, here is the solution:

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)

Output:

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

I’m a bit late on this topic, but think I can help someone.

You can use product from itertools:

from itertools import product

n = [1, 2, 3]

result = product(n, repeat=3) # You can change the repeat more then n length

print(list(result))

Output:

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

Another example, but changing repeat arguement:

from itertools import product

n = [1, 2, 3]

result = product(n, repeat=4) # Changing repeat to 4
print(list(result))

Output:

(1, 1, 2, 3), (1, 1, 3, 1), (1, 1, 3, 2), (1, 1, 3, 3), (1, 2, 1, 1), 
(1, 2, 1, 2), (1, 2, 1, 3), (1, 2, 2, 1), (1, 2, 2, 2), (1, 2, 2, 3), 
(1, 2, 3, 1), (1, 2, 3, 2), (1, 2, 3, 3), (1, 3, 1, 1), (1, 3, 1, 2), 
(1, 3, 1, 3), (1, 3, 2, 1), (1, 3, 2, 2), (1, 3, 2, 3), (1, 3, 3, 1), 
(1, 3, 3, 2), (1, 3, 3, 3), (2, 1, 1, 1), (2, 1, 1, 2), (2, 1, 1, 3), 
(2, 1, 2, 1), (2, 1, 2, 2), (2, 1, 2, 3), (2, 1, 3, 1), (2, 1, 3, 2),
 (2, 1, 3, 3), (2, 2, 1, 1), (2, 2, 1, 2), (2, 2, 1, 3), (2, 2, 2, 1), 
(2, 2, 2, 2), (2, 2, 2, 3), (2, 2, 3, 1), (2, 2, 3, 2), (2, 2, 3, 3), 
(2, 3, 1, 1), (2, 3, 1, 2), (2, 3, 1, 3), (2, 3, 2, 1), (2, 3, 2, 2), 
(2, 3, 2, 3), (2, 3, 3, 1), (2, 3, 3, 2), (2, 3, 3, 3), (3, 1, 1, 1), 
(3, 1, 1, 2), (3, 1, 1, 3), (3, 1, 2, 1), (3, 1, 2, 2), (3, 1, 2, 3), 
(3, 1, 3, 1), (3, 1, 3, 2), (3, 1, 3, 3), (3, 2, 1, 1), (3, 2, 1, 2), 
(3, 2, 1, 3), (3, 2, 2, 1), (3, 2, 2, 2), (3, 2, 2, 3), (3, 2, 3, 1), 
(3, 2, 3, 2), (3, 2, 3, 3), (3, 3, 1, 1), (3, 3, 1, 2), (3, 3, 1, 3), 
(3, 3, 2, 1), (3, 3, 2, 2), (3, 3, 2, 3), (3, 3, 3, 1), (3, 3, 3, 2), 
(3, 3, 3, 3)]```

As mentioned by James Brady, you itertools.combinations is a key. But it is not a full solution.

Solution 1

import itertools
def all(lst):
    # ci is a bitmask which denotes particular combination,
    # see explanation below
    for ci in range(1, 2**len(lst)):
        yield tuple(itertools.compress(
            lst,
            [ci & (1<<k) for k in  range(0, len(lst))]
        ))

Solution 2

import itertools
def all_combs(lst):
    for r in range(1, len(lst)+1):
        for comb in itertools.combinations(lst, r):
            yield comb

Example

>>> list(all_combs([1,2,3]))
[(1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]
>>> len(list(all_combs([1,2,3])))
7
>>> len(list(all_combs(range(0, 15))))
32767
>>> list(all([1,2,3]))
[(1,), (2,), (1, 2), (3,), (1, 3), (2, 3), (1, 2, 3)]
>>> len(list(all(range(15))))
32767

Explanation

Assume your array A has length N. Let a bitmask B of length N will denote a particular combination C. If B[i] is 1, then A[i] belongs to combination C.

Solution 1 explanation

So we can just go over all bitmasks and filter source array A with this bitmask, which might be done by itertools.compress.

Solution 2 explanation

…Or, we can represent it as combinations

Now we need to consider cases, when there is single one in B, then when only two ones, and so on. Each case belongs to particular Combination.
Thus once we combine all combinations sets we will get all subsequences.

Also, it becomes obvious that amount of all possible combinations in such case is 2^N – 1. We drop case when all B[i] are zeroes, since we assume empty set is not a combination. Otherwise, just don’t substract 1.

I like this problem because there are so many ways to implement it. I decided to create a reference answer for the future.

What to use in production?

The intertools’ documentation has a self-contained example, why not use it in your code? Some people suggested using more_itertools.powerset, but it has exactly the same implementation! If I were you I wouldn’t install the whole package for one tiny thing. Probably this is the best way to go:

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))

Other possible approaches

Approach 0: Using combinations

import itertools

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

Approach 1: Straightforward recursion

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

Approach 2: Backtracking

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

Approach 3: Bitmasking

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

or (not really bitmasking, using intuition that there’s exactly 2^n subsets)

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

Approach 4: Cascading

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