在没有任何python数据结构的帮助下，你可以简单地尝试下面的代码。这将工作于寻找重复的各种输入，如字符串，列表等。

# finding duplicates in unsorted an array 
def duplicates(numbers):
    store=[]
    checked=[]
    for i in range(len(numbers)):
        counter =1 
        for j in range(i+1,len(numbers)):
            if numbers[i] not in checked and numbers[j]==numbers[i] :
                counter +=1 
        if counter > 1 :
            store.append(numbers[i])
            checked.append(numbers[i])
    return store

print(duplicates([1,2,2,3,3,3,4,4,5]))  # output:  [2, 3, 4]
print(duplicates("madam"))              # output:  ['m', 'a']

我必须这样做，因为我挑战自己不使用其他方法:

def dupList(oldlist):
    if type(oldlist)==type((2,2)):
        oldlist=[x for x in oldlist]
    newList=[]
    newList=newList+oldlist
    oldlist=oldlist
    forbidden=[]
    checkPoint=0
    for i in range(len(oldlist)):
        #print 'start i', i
        if i in forbidden:
            continue
        else:
            for j in range(len(oldlist)):
                #print 'start j', j
                if j in forbidden:
                    continue
                else:
                    #print 'after Else'
                    if i!=j: 
                        #print 'i,j', i,j
                        #print oldlist
                        #print newList
                        if oldlist[j]==oldlist[i]:
                            #print 'oldlist[i],oldlist[j]', oldlist[i],oldlist[j]
                            forbidden.append(j)
                            #print 'forbidden', forbidden
                            del newList[j-checkPoint]
                            #print newList
                            checkPoint=checkPoint+1
    return newList

所以你的样本工作如下:

>>>a = [1,2,3,3,3,4,5,6,6,7]
>>>dupList(a)
[1, 2, 3, 4, 5, 6, 7]

一句话解决方案:

set([i for i in list if sum([1 for a in list if a == i]) > 1])

下面是一个快速生成器，它使用dict将每个元素存储为一个带有布尔值的键，用于检查是否已经产生了重复项。

对于所有元素都是可哈希类型的列表:

def gen_dupes(array):
    unique = {}
    for value in array:
        if value in unique and unique[value]:
            unique[value] = False
            yield value
        else:
            unique[value] = True

array = [1, 2, 2, 3, 4, 1, 5, 2, 6, 6]
print(list(gen_dupes(array)))
# => [2, 1, 6]

对于可能包含列表的列表:

def gen_dupes(array):
    unique = {}
    for value in array:
        is_list = False
        if type(value) is list:
            value = tuple(value)
            is_list = True

        if value in unique and unique[value]:
            unique[value] = False
            if is_list:
                value = list(value)

            yield value
        else:
            unique[value] = True

array = [1, 2, 2, [1, 2], 3, 4, [1, 2], 5, 2, 6, 6]
print(list(gen_dupes(array)))
# => [2, [1, 2], 6]

你不需要计数，只需要该物品之前是否被看到过。把这个答案用在这个问题上:

def list_duplicates(seq):
  seen = set()
  seen_add = seen.add
  # adds all elements it doesn't know yet to seen and all other to seen_twice
  seen_twice = set( x for x in seq if x in seen or seen_add(x) )
  # turn the set into a list (as requested)
  return list( seen_twice )

a = [1,2,3,2,1,5,6,5,5,5]
list_duplicates(a) # yields [1, 2, 5]

以防速度很重要，这里有一些时间安排:

# file: test.py
import collections

def thg435(l):
    return [x for x, y in collections.Counter(l).items() if y > 1]

def moooeeeep(l):
    seen = set()
    seen_add = seen.add
    # adds all elements it doesn't know yet to seen and all other to seen_twice
    seen_twice = set( x for x in l if x in seen or seen_add(x) )
    # turn the set into a list (as requested)
    return list( seen_twice )

def RiteshKumar(l):
    return list(set([x for x in l if l.count(x) > 1]))

def JohnLaRooy(L):
    seen = set()
    seen2 = set()
    seen_add = seen.add
    seen2_add = seen2.add
    for item in L:
        if item in seen:
            seen2_add(item)
        else:
            seen_add(item)
    return list(seen2)

l = [1,2,3,2,1,5,6,5,5,5]*100

以下是结果:(做得好@JohnLaRooy!)

$ python -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
10000 loops, best of 3: 74.6 usec per loop
$ python -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
10000 loops, best of 3: 91.3 usec per loop
$ python -mtimeit -s 'import test' 'test.thg435(test.l)'
1000 loops, best of 3: 266 usec per loop
$ python -mtimeit -s 'import test' 'test.RiteshKumar(test.l)'
100 loops, best of 3: 8.35 msec per loop

有趣的是，除了计时本身，当使用pypy时，排名也略有变化。最有趣的是，基于counter的方法极大地受益于pypy的优化，而我建议的方法缓存方法似乎几乎没有任何效果。

$ pypy -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
100000 loops, best of 3: 17.8 usec per loop
$ pypy -mtimeit -s 'import test' 'test.thg435(test.l)'
10000 loops, best of 3: 23 usec per loop
$ pypy -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
10000 loops, best of 3: 39.3 usec per loop

显然，这种效应与输入数据的“重复性”有关。我设置了l = [random.randrange(1000000) for I in xrange(10000)]，得到了这些结果:

$ pypy -mtimeit -s 'import test' 'test.moooeeeep(test.l)'
1000 loops, best of 3: 495 usec per loop
$ pypy -mtimeit -s 'import test' 'test.JohnLaRooy(test.l)'
1000 loops, best of 3: 499 usec per loop
$ pypy -mtimeit -s 'import test' 'test.thg435(test.l)'
1000 loops, best of 3: 1.68 msec per loop

集合。Counter是python 2.7中的新功能:

Python 2.5.4 (r254:67916, May 31 2010, 15:03:39) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-46)] on linux2
a = [1,2,3,2,1,5,6,5,5,5]
import collections
print [x for x, y in collections.Counter(a).items() if y > 1]
Type "help", "copyright", "credits" or "license" for more information.
  File "", line 1, in 
AttributeError: 'module' object has no attribute 'Counter'
>>> 

在早期版本中，你可以使用传统的字典:

a = [1,2,3,2,1,5,6,5,5,5]
d = {}
for elem in a:
    if elem in d:
        d[elem] += 1
    else:
        d[elem] = 1

print [x for x, y in d.items() if y > 1]