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]

Pandas用户应该查看Pandas .unique。

>>> import pandas as pd
>>> lst = [1, 2, 1, 3, 3, 2, 4]
>>> pd.unique(lst)
array([1, 2, 3, 4])

该函数返回一个NumPy数组。如果需要，可以使用tolist方法将其转换为列表。

对于不可哈希类型(例如列表的列表)，基于MizardX的:

def f7_noHash(seq)
    seen = set()
    return [ x for x in seq if str( x ) not in seen and not seen.add( str( x ) )]

你可以做一个丑陋的列表理解黑客。

[l[i] for i in range(len(l)) if l.index(l[i]) == i]

不使用导入模块或集的解决方案:

text = "ask not what your country can do for you ask what you can do for your country"
sentence = text.split(" ")
noduplicates = [(sentence[i]) for i in range (0,len(sentence)) if sentence[i] not in sentence[:i]]
print(noduplicates)

给输出:

['ask', 'not', 'what', 'your', 'country', 'can', 'do', 'for', 'you']

只是从外部module1中添加这样一个功能的另一个(非常高性能的)实现:

>>> from iteration_utilities import unique_everseen
>>> lst = [1,1,1,2,3,2,2,2,1,3,4]

>>> list(unique_everseen(lst))
[1, 2, 3, 4]

计时

我做了一些计时(Python 3.6)，这些表明它比我测试的所有其他替代方案都快，包括OrderedDict.fromkeys, f7和more_itertools.unique_everseen:

%matplotlib notebook

from iteration_utilities import unique_everseen
from collections import OrderedDict
from more_itertools import unique_everseen as mi_unique_everseen

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

def iteration_utilities_unique_everseen(seq):
    return list(unique_everseen(seq))

def more_itertools_unique_everseen(seq):
    return list(mi_unique_everseen(seq))

def odict(seq):
    return list(OrderedDict.fromkeys(seq))

from simple_benchmark import benchmark

b = benchmark([f7, iteration_utilities_unique_everseen, more_itertools_unique_everseen, odict],
              {2**i: list(range(2**i)) for i in range(1, 20)},
              'list size (no duplicates)')
b.plot()

为了确保这一点，我还做了一个重复的测试，看看是否有区别:

import random

b = benchmark([f7, iteration_utilities_unique_everseen, more_itertools_unique_everseen, odict],
              {2**i: [random.randint(0, 2**(i-1)) for _ in range(2**i)] for i in range(1, 20)},
              'list size (lots of duplicates)')
b.plot()

一个只包含一个值:

b = benchmark([f7, iteration_utilities_unique_everseen, more_itertools_unique_everseen, odict],
              {2**i: [1]*(2**i) for i in range(1, 20)},
              'list size (only duplicates)')
b.plot()

在所有这些情况下，iteration_utilities。Unique_everseen函数是最快的(在我的电脑上)。

这iteration_utilities。unique_everseen函数也可以处理输入中的不可哈希值(但是当值是可哈希值时，性能是O(n*n)而不是O(n))。

>>> lst = [{1}, {1}, {2}, {1}, {3}]

>>> list(unique_everseen(lst))
[{1}, {2}, {3}]

1免责声明:我是该软件包的作者。