如何从列表中删除重复项,同时保持顺序?使用集合删除重复项会破坏原始顺序。 是否有内置的或python的习语?
当前回答
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。
其他回答
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
一个简单的递归解决方案:
def uniquefy_list(a):
return uniquefy_list(a[1:]) if a[0] in a[1:] else [a[0]]+uniquefy_list(a[1:]) if len(a)>1 else [a[0]]
我觉得如果你想维持秩序,
你可以试试这个:
list1 = ['b','c','d','b','c','a','a']
list2 = list(set(list1))
list2.sort(key=list1.index)
print list2
或者类似地,你可以这样做:
list1 = ['b','c','d','b','c','a','a']
list2 = sorted(set(list1),key=list1.index)
print list2
你还可以这样做:
list1 = ['b','c','d','b','c','a','a']
list2 = []
for i in list1:
if not i in list2:
list2.append(i)`
print list2
它也可以写成这样:
list1 = ['b','c','d','b','c','a','a']
list2 = []
[list2.append(i) for i in list1 if not i in list2]
print list2
sequence = ['1', '2', '3', '3', '6', '4', '5', '6']
unique = []
[unique.append(item) for item in sequence if item not in unique]
unique→[1、(2)、(3)、(6)、(4)、(5)]
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。