如果你经常使用pandas，并且美学优先于性能，那么考虑内置函数pandas. series .drop_duplicate:

    import pandas as pd
    import numpy as np

    uniquifier = lambda alist: pd.Series(alist).drop_duplicates().tolist()

    # from the chosen answer 
    def f7(seq):
        seen = set()
        seen_add = seen.add
        return [ x for x in seq if not (x in seen or seen_add(x))]

    alist = np.random.randint(low=0, high=1000, size=10000).tolist()

    print uniquifier(alist) == f7(alist)  # True

时间:

    In [104]: %timeit f7(alist)
    1000 loops, best of 3: 1.3 ms per loop
    In [110]: %timeit uniquifier(alist)
    100 loops, best of 3: 4.39 ms per loop

使用_sorted_ a numpy数组的相对有效方法:

b = np.array([1,3,3, 8, 12, 12,12])    
numpy.hstack([b[0], [x[0] for x in zip(b[1:], b[:-1]) if x[0]!=x[1]]])

输出:

array([ 1,  3,  8, 12])

5倍更快减少变种，但更复杂

>>> l = [5, 6, 6, 1, 1, 2, 2, 3, 4]
>>> reduce(lambda r, v: v in r[1] and r or (r[0].append(v) or r[1].add(v)) or r, l, ([], set()))[0]
[5, 6, 1, 2, 3, 4]

解释:

default = (list(), set())
# use list to keep order
# use set to make lookup faster

def reducer(result, item):
    if item not in result[1]:
        result[0].append(item)
        result[1].add(item)
    return result

>>> reduce(reducer, l, default)[0]
[5, 6, 1, 2, 3, 4]

l = [1,2,2,3,3,...]
n = []
n.extend(ele for ele in l if ele not in set(n))

一个生成器表达式，它使用集合的O(1)查找来确定是否在新列表中包含元素。

如果你需要一个班轮，那么这可能会有帮助:

reduce(lambda x, y: x + y if y[0] not in x else x, map(lambda x: [x],lst))

．.． 应该工作，但纠正我，如果我错了

这将保持秩序并在O(n)时间内运行。基本上，这个想法是在任何发现副本的地方创建一个洞，并将其沉到底部。使用读写指针。每当发现一个重复项时，只有读指针前进，写指针停留在重复项上覆盖它。

def deduplicate(l):
    count = {}
    (read,write) = (0,0)
    while read < len(l):
        if l[read] in count:
            read += 1
            continue
        count[l[read]] = True
        l[write] = l[read]
        read += 1
        write += 1
    return l[0:write]