如何在整数列表中找到重复项并创建重复项的另一个列表?
当前回答
假设我们有这个元素列表:
a = [1, 2, 3, 2, 1, 5, 6, 5, 5, 5]
我们可以只使用集合来找到唯一的元素:
unique = set()
for num in a:
if num not in unique:
unique.add(num)
else:
unique = unique - set([num])
最后:
>>> unique
{3, 6}
如果你想要得到副本,你可以简单地做:
>>> duplicates = set(a) - unique
>>> duplicates
{1, 2, 5}
注:
集合中的元素查找是O(1) 从集合中移除的元素是O(1)
其他回答
我会用熊猫做这个,因为我经常用熊猫
import pandas as pd
a = [1,2,3,3,3,4,5,6,6,7]
vc = pd.Series(a).value_counts()
vc[vc > 1].index.tolist()
给了
[3,6]
可能不是很有效,但它肯定比许多其他答案的代码更少,所以我想我可以贡献一下
还有其他测试。当然要做……
set([x for x in l if l.count(x) > 1])
...代价太大了。使用下一个final方法大约快500倍(数组越长结果越好):
def dups_count_dict(l):
d = {}
for item in l:
if item not in d:
d[item] = 0
d[item] += 1
result_d = {key: val for key, val in d.iteritems() if val > 1}
return result_d.keys()
只有2个循环,没有非常昂贵的l.count()操作。
下面是一个比较方法的代码。代码如下,输出如下:
dups_count: 13.368s # this is a function which uses l.count()
dups_count_dict: 0.014s # this is a final best function (of the 3 functions)
dups_count_counter: 0.024s # collections.Counter
测试代码:
import numpy as np
from time import time
from collections import Counter
class TimerCounter(object):
def __init__(self):
self._time_sum = 0
def start(self):
self.time = time()
def stop(self):
self._time_sum += time() - self.time
def get_time_sum(self):
return self._time_sum
def dups_count(l):
return set([x for x in l if l.count(x) > 1])
def dups_count_dict(l):
d = {}
for item in l:
if item not in d:
d[item] = 0
d[item] += 1
result_d = {key: val for key, val in d.iteritems() if val > 1}
return result_d.keys()
def dups_counter(l):
counter = Counter(l)
result_d = {key: val for key, val in counter.iteritems() if val > 1}
return result_d.keys()
def gen_array():
np.random.seed(17)
return list(np.random.randint(0, 5000, 10000))
def assert_equal_results(*results):
primary_result = results[0]
other_results = results[1:]
for other_result in other_results:
assert set(primary_result) == set(other_result) and len(primary_result) == len(other_result)
if __name__ == '__main__':
dups_count_time = TimerCounter()
dups_count_dict_time = TimerCounter()
dups_count_counter = TimerCounter()
l = gen_array()
for i in range(3):
dups_count_time.start()
result1 = dups_count(l)
dups_count_time.stop()
dups_count_dict_time.start()
result2 = dups_count_dict(l)
dups_count_dict_time.stop()
dups_count_counter.start()
result3 = dups_counter(l)
dups_count_counter.stop()
assert_equal_results(result1, result2, result3)
print 'dups_count: %.3f' % dups_count_time.get_time_sum()
print 'dups_count_dict: %.3f' % dups_count_dict_time.get_time_sum()
print 'dups_count_counter: %.3f' % dups_count_counter.get_time_sum()
我在寻找相关的东西时遇到了这个问题-想知道为什么没有人提供基于生成器的解决方案?解决这个问题的方法是:
>>> print list(getDupes_9([1,2,3,2,1,5,6,5,5,5]))
[1, 2, 5]
我很关心可伸缩性,所以测试了几种方法,包括在小列表上工作得很好的naive项,但随着列表变大,可伸缩性很差(注意-使用timeit会更好,但这只是说明)。
我加入了@moooeeeep作为比较(它的速度非常快:如果输入列表是完全随机的,速度最快)和itertools方法,对于大多数排序的列表,它甚至更快……现在包括来自@ fireynx的熊猫方法-缓慢,但不是可怕的,而且简单。注意:在我的机器上,sort/tee/zip方法对于大型有序列表始终是最快的,moooeeeep对于洗牌列表是最快的,但您的情况可能会有所不同。
优势
非常快速简单的测试'任何'重复使用相同的代码
假设
重复项只应报告一次 重复的订单不需要保留 Duplicate可能位于列表中的任何位置
最快的解决方案,1m条目:
def getDupes(c):
'''sort/tee/izip'''
a, b = itertools.tee(sorted(c))
next(b, None)
r = None
for k, g in itertools.izip(a, b):
if k != g: continue
if k != r:
yield k
r = k
方法测试
import itertools
import time
import random
def getDupes_1(c):
'''naive'''
for i in xrange(0, len(c)):
if c[i] in c[:i]:
yield c[i]
def getDupes_2(c):
'''set len change'''
s = set()
for i in c:
l = len(s)
s.add(i)
if len(s) == l:
yield i
def getDupes_3(c):
'''in dict'''
d = {}
for i in c:
if i in d:
if d[i]:
yield i
d[i] = False
else:
d[i] = True
def getDupes_4(c):
'''in set'''
s,r = set(),set()
for i in c:
if i not in s:
s.add(i)
elif i not in r:
r.add(i)
yield i
def getDupes_5(c):
'''sort/adjacent'''
c = sorted(c)
r = None
for i in xrange(1, len(c)):
if c[i] == c[i - 1]:
if c[i] != r:
yield c[i]
r = c[i]
def getDupes_6(c):
'''sort/groupby'''
def multiple(x):
try:
x.next()
x.next()
return True
except:
return False
for k, g in itertools.ifilter(lambda x: multiple(x[1]), itertools.groupby(sorted(c))):
yield k
def getDupes_7(c):
'''sort/zip'''
c = sorted(c)
r = None
for k, g in zip(c[:-1],c[1:]):
if k == g:
if k != r:
yield k
r = k
def getDupes_8(c):
'''sort/izip'''
c = sorted(c)
r = None
for k, g in itertools.izip(c[:-1],c[1:]):
if k == g:
if k != r:
yield k
r = k
def getDupes_9(c):
'''sort/tee/izip'''
a, b = itertools.tee(sorted(c))
next(b, None)
r = None
for k, g in itertools.izip(a, b):
if k != g: continue
if k != r:
yield k
r = k
def getDupes_a(l):
'''moooeeeep'''
seen = set()
seen_add = seen.add
# adds all elements it doesn't know yet to seen and all other to seen_twice
for x in l:
if x in seen or seen_add(x):
yield x
def getDupes_b(x):
'''iter*/sorted'''
x = sorted(x)
def _matches():
for k,g in itertools.izip(x[:-1],x[1:]):
if k == g:
yield k
for k, n in itertools.groupby(_matches()):
yield k
def getDupes_c(a):
'''pandas'''
import pandas as pd
vc = pd.Series(a).value_counts()
i = vc[vc > 1].index
for _ in i:
yield _
def hasDupes(fn,c):
try:
if fn(c).next(): return True # Found a dupe
except StopIteration:
pass
return False
def getDupes(fn,c):
return list(fn(c))
STABLE = True
if STABLE:
print 'Finding FIRST then ALL duplicates, single dupe of "nth" placed element in 1m element array'
else:
print 'Finding FIRST then ALL duplicates, single dupe of "n" included in randomised 1m element array'
for location in (50,250000,500000,750000,999999):
for test in (getDupes_2, getDupes_3, getDupes_4, getDupes_5, getDupes_6,
getDupes_8, getDupes_9, getDupes_a, getDupes_b, getDupes_c):
print 'Test %-15s:%10d - '%(test.__doc__ or test.__name__,location),
deltas = []
for FIRST in (True,False):
for i in xrange(0, 5):
c = range(0,1000000)
if STABLE:
c[0] = location
else:
c.append(location)
random.shuffle(c)
start = time.time()
if FIRST:
print '.' if location == test(c).next() else '!',
else:
print '.' if [location] == list(test(c)) else '!',
deltas.append(time.time()-start)
print ' -- %0.3f '%(sum(deltas)/len(deltas)),
print
print
“all dupes”测试的结果是一致的,在这个数组中找到“first”重复,然后是“all”重复:
Finding FIRST then ALL duplicates, single dupe of "nth" placed element in 1m element array
Test set len change : 500000 - . . . . . -- 0.264 . . . . . -- 0.402
Test in dict : 500000 - . . . . . -- 0.163 . . . . . -- 0.250
Test in set : 500000 - . . . . . -- 0.163 . . . . . -- 0.249
Test sort/adjacent : 500000 - . . . . . -- 0.159 . . . . . -- 0.229
Test sort/groupby : 500000 - . . . . . -- 0.860 . . . . . -- 1.286
Test sort/izip : 500000 - . . . . . -- 0.165 . . . . . -- 0.229
Test sort/tee/izip : 500000 - . . . . . -- 0.145 . . . . . -- 0.206 *
Test moooeeeep : 500000 - . . . . . -- 0.149 . . . . . -- 0.232
Test iter*/sorted : 500000 - . . . . . -- 0.160 . . . . . -- 0.221
Test pandas : 500000 - . . . . . -- 0.493 . . . . . -- 0.499
当列表首先被打乱时,排序的代价就变得明显了——效率显著下降,@moooeeeep方法占主导地位,set和dict方法类似,但性能较差:
Finding FIRST then ALL duplicates, single dupe of "n" included in randomised 1m element array
Test set len change : 500000 - . . . . . -- 0.321 . . . . . -- 0.473
Test in dict : 500000 - . . . . . -- 0.285 . . . . . -- 0.360
Test in set : 500000 - . . . . . -- 0.309 . . . . . -- 0.365
Test sort/adjacent : 500000 - . . . . . -- 0.756 . . . . . -- 0.823
Test sort/groupby : 500000 - . . . . . -- 1.459 . . . . . -- 1.896
Test sort/izip : 500000 - . . . . . -- 0.786 . . . . . -- 0.845
Test sort/tee/izip : 500000 - . . . . . -- 0.743 . . . . . -- 0.804
Test moooeeeep : 500000 - . . . . . -- 0.234 . . . . . -- 0.311 *
Test iter*/sorted : 500000 - . . . . . -- 0.776 . . . . . -- 0.840
Test pandas : 500000 - . . . . . -- 0.539 . . . . . -- 0.540
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]
为了好玩,只需要一行语句。
(lambda iterable: reduce(lambda (uniq, dup), item: (uniq, dup | {item}) if item in uniq else (uniq | {item}, dup), iterable, (set(), set())))(some_iterable)
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