假设我有6个列表或数组。每个列表都有任何数量的单词。
0 | 1 | 2 | 3 | 4 | 5 | ... N
-----------------------------------------------------------
cat dog pine tree light fan
cat dog pine tree light fan
cat dog pine tree light fan
cat dog pine tree light fan
cat dog pine tree light fan
我不想输入所有这些单词，但让我们说我想得到交集。找到所有的交集是非常简单的，可以在python中使用这样的函数来完成：
all = set(zer0).intersection(one).intersection(two).intersection(...N)
我想确保我没有错过更简单的解决方案而不是过度思考。
对于上面的例子，要获得我需要做的任何两个列表的匹配。
0&1, 0&2, 0&3, 0&4, 0&5, 0&..N
三个
0&1&2, 0&1&3, 0&1&4, 0&1&5, 0&1&..N
我问的原因是因为查看只有两个列表的示例，如果数组零和数组1不包含相似的单词但数组为零，那么该怎么办？
有没有办法概括这一点，我有强烈的感觉它已经解决了，我在思考这个问题。
我希望能够找出说出来的单词cat 0,1,2，.. N列表。
[编辑] 以下是我正在使用的一些sample data。
data0 = unicode("Rainforests are forests characterized by high rainfall, with annual rainfall between 250 and 450 centimetres (98 and 177 in).[1] There are two types of rainforest: tropical rainforest and temperate rainforest. The monsoon trough, alternatively known as the intertropical convergence zone, plays a significant role in creating the climatic conditions necessary for the Earth's tropical rainforests. Around 40% to 75% of all biotic species are indigenous to the rainforests.[2] It has been estimated that there may be many millions of species of plants, insects and microorganisms still undiscovered in tropical rainforests. Tropical rainforests have been called the \"jewels of the Earth\" and the \"world's largest pharmacy\", because over one quarter of natural medicines have been discovered there.[3] Rainforests are also responsible for 28% of the world's oxygen turnover, sometimes misnamed oxygen production,[4] processing it through photosynthesis from carbon dioxide and consuming it through respiration. The undergrowth in some areas of a rainforest can be restricted by poor penetration of sunlight to ground level. If the leaf canopy is destroyed or thinned, the ground beneath is soon colonized by a dense, tangled growth of vines, shrubs and small trees, called a jungle. The term jungle is also sometimes applied to tropical rainforests generally.", "utf-8")
data1 = unicode("Tropical rainforests are characterized by a warm and wet climate with no substantial dry season: typically found within 10 degrees north and south of the equator. Mean monthly temperatures exceed 18 °C (64 °F) during all months of the year.[5] Average annual rainfall is no less than 168 cm (66 in) and can exceed 1,000 cm (390 in) although it typically lies between 175 cm (69 in) and 200 cm (79 in).[6] Many of the world's tropical forests are associated with the location of the monsoon trough, also known as the intertropical convergence zone.[7] The broader category of tropical moist forests are located in the equatorial zone between the Tropic of Cancer and Tropic of Capricorn. Tropical rainforests exist in Southeast Asia (from Myanmar (Burma) to the Philippines, Malaysia, Indonesia, Papua New Guinea, Sri Lanka, Sub-Saharan Africa from Cameroon to the Congo (Congo Rainforest), South America (e.g. the Amazon Rainforest), Central America (e.g. Bosawás, southern Yucatán Peninsula-El Peten-Belize-Calakmul), Australia, and on many of the Pacific Islands (such as Hawaiʻi). Tropical forests have been called the \"Earth's lungs\", although it is now known that rainforests contribute little net oxygen addition to the atmosphere through photosynthesis", "utf-8")
data2 = unicode("Tropical forests cover a large part of the globe, but temperate rainforests only occur in few regions around the world. Temperate rainforests are rainforests in temperate regions. They occur in North America (in the Pacific Northwest in Alaska, British Columbia, Washington, Oregon and California), in Europe (parts of the British Isles such as the coastal areas of Ireland and Scotland, southern Norway, parts of the western Balkans along the Adriatic coast, as well as in Galicia and coastal areas of the eastern Black Sea, including Georgia and coastal Turkey), in East Asia (in southern China, Highlands of Taiwan, much of Japan and Korea, and on Sakhalin Island and the adjacent Russian Far East coast), in South America (southern Chile) and also in Australia and New Zealand.[10]", "utf-8")
我清理文本，将其标记为三个列表，data0_list，... data2_list。
之后，像这样的函数调用输出数据。
master_list.append(data_0)
master_list.append(data_1)
master_list.append(data_2)
for item in master_list:
for index, item in enumerate(item):
print(index, item)
输出如下：
=========== start data_0 ==============
(0, ((u'the',), 13))
(1, ((u'of',), 10))
(2, ((u'rainforests',), 7))
(3, ((u'and',), 7))
(4, ((u'tropical',), 5))
(5, ((u'to',), 4))
(6, ((u'rainforest',), 4))
(7, ((u'in',), 4))
(8, ((u'are',), 4))
(9, ((u'a',), 4))
(10, ((u'it',), 3))
(11, ((u'by',), 3))
(12, ((u'been',), 3))
(13, ((u's',), 3))
(14, ((u'is',), 3))
(15, ((u'there',), 3))
(16, ((u'have',), 2))
(17, ((u'earth',), 2))
(18, ((u'sometimes',), 2))
(19, ((u'also',), 2))
(20, ((u'oxygen',), 2))
(21, ((u'jungle',), 2))
(22, ((u'rainfall',), 2))
(23, ((u'for',), 2))
(24, ((u'through',), 2))
(25, ((u'called',), 2))
(26, ((u'be',), 2))
(27, ((u'world',), 2))
(28, ((u'species',), 2))
(29, ((u'ground',), 2))
(30, ((u'shrubs',), 1))
(31, ((u'may',), 1))
(32, ((u'biotic',), 1))
(33, ((u'from',), 1))
(34, ((u'respiration',), 1))
(35, ((u'known',), 1))
(36, ((u'largest',), 1))
(37, ((u'discovered',), 1))
(38, ((u'two',), 1))
(39, ((u'plants',), 1))
(40, ((u'conditions',), 1))
(41, ((u'insects',), 1))
(42, ((u'necessary',), 1))
(43, ((u'1',), 1))
(44, ((u'convergence',), 1))
(45, ((u'jewels',), 1))
(46, ((u'poor',), 1))
(47, ((u'estimated',), 1))
(48, ((u'if',), 1))
(49, ((u'creating',), 1))
(50, ((u'that',), 1))
(51, ((u'75',), 1))
(52, ((u'growth',), 1))
(53, ((u'penetration',), 1))
(54, ((u'thinned',), 1))
(55, ((u'has',), 1))
(56, ((u'characterized',), 1))
(57, ((u'plays',), 1))
(58, ((u'temperate',), 1))
(59, ((u'production',), 1))
(60, ((u'because',), 1))
(61, ((u'high',), 1))
(62, ((u'98',), 1))
(63, ((u'trough',), 1))
(64, ((u'centimetres',), 1))
(65, ((u'over',), 1))
(66, ((u'some',), 1))
(67, ((u'undiscovered',), 1))
(68, ((u'natural',), 1))
(69, ((u'still',), 1))
(70, ((u'misnamed',), 1))
(71, ((u'all',), 1))
(72, ((u'many',), 1))
(73, ((u'sunlight',), 1))
(74, ((u'millions',), 1))
(75, ((u'dioxide',), 1))
(76, ((u'around',), 1))
(77, ((u'28',), 1))
(78, ((u'monsoon',), 1))
(79, ((u'canopy',), 1))
(80, ((u'photosynthesis',), 1))
(81, ((u'level',), 1))
(82, ((u'177',), 1))
(83, ((u'trees',), 1))
(84, ((u'carbon',), 1))
(85, ((u'one',), 1))
(86, ((u'4',), 1))
(87, ((u'between',), 1))
(88, ((u'areas',), 1))
(89, ((u'responsible',), 1))
(90, ((u'as',), 1))
(91, ((u'vines',), 1))
(92, ((u'450',), 1))
(93, ((u'turnover',), 1))
(94, ((u'leaf',), 1))
(95, ((u'role',), 1))
(96, ((u'indigenous',), 1))
(97, ((u'can',), 1))
(98, ((u'with',), 1))
(99, ((u'types',), 1))
(100, ((u'alternatively',), 1))
(101, ((u'annual',), 1))
(102, ((u'generally',), 1))
(103, ((u'zone',), 1))
(104, ((u'beneath',), 1))
(105, ((u'significant',), 1))
(106, ((u'consuming',), 1))
(107, ((u'microorganisms',), 1))
(108, ((u'applied',), 1))
(109, ((u'soon',), 1))
(110, ((u'2',), 1))
(111, ((u'tangled',), 1))
(112, ((u'250',), 1))
(113, ((u'restricted',), 1))
(114, ((u'undergrowth',), 1))
(115, ((u'medicines',), 1))
(116, ((u'climatic',), 1))
(117, ((u'colonized',), 1))
(118, ((u'forests',), 1))
(119, ((u'dense',), 1))
(120, ((u'pharmacy',), 1))
(121, ((u'quarter',), 1))
(122, ((u'intertropical',), 1))
(123, ((u'term',), 1))
(124, ((u'or',), 1))
(125, ((u'destroyed',), 1))
(126, ((u'processing',), 1))
(127, ((u'3',), 1))
(128, ((u'small',), 1))
(129, ((u'40',), 1))
=========== start data_1 ==============
(0, ((u'the',), 15))
(1, ((u'of',), 8))
(2, ((u'in',), 6))
(3, ((u'and',), 6))
(4, ((u'tropical',), 5))
(5, ((u'cm',), 4))
(6, ((u'to',), 3))
(7, ((u'are',), 3))
(8, ((u'rainforests',), 3))
(9, ((u'forests',), 3))
(10, ((u'south',), 2))
(11, ((u'from',), 2))
(12, ((u'it',), 2))
(13, ((u'g',), 2))
(14, ((u'no',), 2))
(15, ((u'known',), 2))
(16, ((u'rainforest',), 2))
(17, ((u'exceed',), 2))
(18, ((u'although',), 2))
(19, ((u'typically',), 2))
(20, ((u'america',), 2))
(21, ((u'e',), 2))
(22, ((u'many',), 2))
(23, ((u's',), 2))
(24, ((u'between',), 2))
(25, ((u'as',), 2))
(26, ((u'is',), 2))
(27, ((u'with',), 2))
(28, ((u'zone',), 2))
(29, ((u'congo',), 2))
(30, ((u'tropic',), 2))
(31, ((u'equatorial',), 1))
(32, ((u'within',), 1))
(33, ((u'located',), 1))
(34, ((u'convergence',), 1))
(35, ((u'now',), 1))
(36, ((u'el',), 1))
(37, ((u'by',), 1))
(38, ((u'saharan',), 1))
(39, ((u'average',), 1))
(40, ((u'lungs',), 1))
(41, ((u'less',), 1))
(42, ((u'64',), 1))
(43, ((u'have',), 1))
(44, ((u'degreef',), 1))
(45, ((u'temperatures',), 1))
(46, ((u'1',), 1))
(47, ((u'africa',), 1))
(48, ((u'earth',), 1))
(49, ((u'200',), 1))
(50, ((u'australia',), 1))
(51, ((u'18',), 1))
(52, ((u'peninsula',), 1))
(53, ((u'indonesia',), 1))
(54, ((u'that',), 1))
(55, ((u'390',), 1))
(56, ((u'been',), 1))
(57, ((u'10',), 1))
(58, ((u'characterized',), 1))
(59, ((u'also',), 1))
(60, ((u'yucatan',), 1))
(61, ((u'6',), 1))
(62, ((u'such',), 1))
(63, ((u'months',), 1))
(64, ((u'000',), 1))
(65, ((u'islands',), 1))
(66, ((u'trough',), 1))
(67, ((u'dry',), 1))
(68, ((u'66',), 1))
(69, ((u'equator',), 1))
(70, ((u'season',), 1))
(71, ((u'mean',), 1))
(72, ((u'sub',), 1))
(73, ((u'oxygen',), 1))
(74, ((u'degrees',), 1))
(75, ((u'7',), 1))
(76, ((u'rainfall',), 1))
(77, ((u'lanka',), 1))
(78, ((u'all',), 1))
(79, ((u'monthly',), 1))
(80, ((u'cancer',), 1))
(81, ((u'monsoon',), 1))
(82, ((u'asia',), 1))
(83, ((u'on',), 1))
(84, ((u'photosynthesis',), 1))
(85, ((u'degreec',), 1))
(86, ((u'southern',), 1))
(87, ((u'location',), 1))
(88, ((u'addition',), 1))
(89, ((u'sri',), 1))
(90, ((u'capricorn',), 1))
(91, ((u'southeast',), 1))
(92, ((u'warm',), 1))
(93, ((u'found',), 1))
(94, ((u'through',), 1))
(95, ((u'cameroon',), 1))
(96, ((u'climate',), 1))
(97, ((u'called',), 1))
(98, ((u'bosawas',), 1))
(99, ((u'pacific',), 1))
(100, ((u'69',), 1))
(101, ((u'5',), 1))
(102, ((u'can',), 1))
(103, ((u'burma',), 1))
(104, ((u'79',), 1))
(105, ((u'papua',), 1))
(106, ((u'annual',), 1))
(107, ((u'lies',), 1))
(108, ((u'atmosphere',), 1))
(109, ((u'substantial',), 1))
(110, ((u'new',), 1))
(111, ((u'168',), 1))
(112, ((u'category',), 1))
(113, ((u'moist',), 1))
(114, ((u'year',), 1))
(115, ((u'little',), 1))
(116, ((u'contribute',), 1))
(117, ((u'during',), 1))
(118, ((u'175',), 1))
(119, ((u'belize',), 1))
(120, ((u'wet',), 1))
(121, ((u'than',), 1))
(122, ((u'guinea',), 1))
(123, ((u'north',), 1))
(124, ((u'philippines',), 1))
(125, ((u'hawai\u02bbi',), 1))
(126, ((u'myanmar',), 1))
(127, ((u'world',), 1))
(128, ((u'peten',), 1))
(129, ((u'exist',), 1))
(130, ((u'net',), 1))
(131, ((u'a',), 1))
(132, ((u'broader',), 1))
(133, ((u'intertropical',), 1))
(134, ((u'calakmul',), 1))
(135, ((u'central',), 1))
(136, ((u'associated',), 1))
(137, ((u'malaysia',), 1))
(138, ((u'amazon',), 1))
=========== start data_2 ==============
(0, ((u'in',), 11))
(1, ((u'the',), 9))
(2, ((u'and',), 9))
(3, ((u'of',), 7))
(4, ((u'temperate',), 3))
(5, ((u'southern',), 3))
(6, ((u'as',), 3))
(7, ((u'coastal',), 3))
(8, ((u'rainforests',), 3))
(9, ((u'east',), 2))
(10, ((u'parts',), 2))
(11, ((u'america',), 2))
(12, ((u'areas',), 2))
(13, ((u'british',), 2))
(14, ((u'coast',), 2))
(15, ((u'occur',), 2))
(16, ((u'regions',), 2))
(17, ((u'are',), 1))
(18, ((u'turkey',), 1))
(19, ((u'they',), 1))
(20, ((u'on',), 1))
(21, ((u'australia',), 1))
(22, ((u'far',), 1))
(23, ((u'oregon',), 1))
(24, ((u'galicia',), 1))
(25, ((u'chile',), 1))
(26, ((u'island',), 1))
(27, ((u'few',), 1))
(28, ((u'zealand',), 1))
(29, ((u'columbia',), 1))
(30, ((u'but',), 1))
(31, ((u'world',), 1))
(32, ((u'sea',), 1))
(33, ((u'taiwan',), 1))
(34, ((u'northwest',), 1))
(35, ((u'europe',), 1))
(36, ((u'10',), 1))
(37, ((u'much',), 1))
(38, ((u'also',), 1))
(39, ((u'north',), 1))
(40, ((u'adriatic',), 1))
(41, ((u'such',), 1))
(42, ((u'cover',), 1))
(43, ((u'forests',), 1))
(44, ((u'part',), 1))
(45, ((u'including',), 1))
(46, ((u'western',), 1))
(47, ((u'a',), 1))
(48, ((u'norway',), 1))
(49, ((u'large',), 1))
(50, ((u'georgia',), 1))
(51, ((u'well',), 1))
(52, ((u'south',), 1))
(53, ((u'globe',), 1))
(54, ((u'tropical',), 1))
(55, ((u'adjacent',), 1))
(56, ((u'washington',), 1))
(57, ((u'only',), 1))
(58, ((u'russian',), 1))
(59, ((u'pacific',), 1))
(60, ((u'japan',), 1))
(61, ((u'black',), 1))
(62, ((u'along',), 1))
(63, ((u'highlands',), 1))
(64, ((u'ireland',), 1))
(65, ((u'sakhalin',), 1))
(66, ((u'balkans',), 1))
(67, ((u'korea',), 1))
(68, ((u'asia',), 1))
(69, ((u'around',), 1))
(70, ((u'scotland',), 1))
(71, ((u'eastern',), 1))
(72, ((u'alaska',), 1))
(73, ((u'china',), 1))
(74, ((u'isles',), 1))
(75, ((u'new',), 1))
(76, ((u'california',), 1))
在此示例中，雨林，世界，林以及其他一些更常见的字词都在所有三个数据集中。
我现在要做的是找到多个列表中的单词。
例如，我希望能够说明雨林这个词在3/3列表中。
另一方面，氧气这个词在2/3列表中，它位于data_0＆amp; _1。答案 0 :(得分：1)
查找多个列表交集的最简单方法是使用 list slicing 功能以及set.intersection()。例如：
my_list =[
['cat', 'dog', 'fan'],
['cat', 'dog', 'pine'],
['cat', 'light', 'tree', 'dog'],
['dog', 'pine', 'cat', 'tree'],
['fan', 'pine', 'dog', 'tree', 'cat'],
['light', 'dog', 'pine', 'cat', 'tree']]
然后所有列表的交集可以计算为：
# v Unwrapped list from index '1'
set(my_list[0]).intersection(*my_list[1:])
# ^ First element in list
将返回：
set(['dog', 'cat'])
修改：看起来你不需要交集。您需要根据语句在所有列表中查找项目计数：
我希望能够找出说出的单词cat出现在0,1,2，..N列表中。
如果您只关心 count项 ，则可以将collections.Counter()与itertools.chain()一起使用：
from itertools import chain
from collections import Counter
my_count = Counter(chain(*my_list))
my_count将保留的位置：
{'dog': 6,
'cat': 6,
'tree': 4,
'pine': 4,
'light': 2,
'fan': 2}
如果您 还想要项目的列表 的映射，您可以创建dict来映射项目。但是，首先你需要将所有项目结合起来：
all_items = set(my_list[0]).union(*my_list[1:])
# which will hold: set(['light', 'tree', 'dog', 'pine', 'cat', 'fan'])
然后将其存储在dict中。我正在使用collections.defaultdict()轻松：
from collections import defaultdict
my_dict = defaultdict(list)
for item in all_items:
for sub_list in my_list:
my_dict[item].append(item in sub_list)
现在my_dict将保留价值：
{
'light': [False, False, True, False, False, True],
# ^ ^ Present in list 3
# ^ Not present in list 1
'tree': [False, False, True, True, True, True],
'dog': [True, True, True, True, True, True],
'pine': [False, True, False, True, True, True],
'cat': [True, True, True, True, True, True],
'fan': [True, False, False, False, True, False]
}
您可以从此dict中找到发生次数。
答案 1 :(得分：1)
虽然您的大部分问题都是关于集合的交叉点，但您实际想要的内容似乎与该概念没有直接关系：
我希望能够找出说出的单词cat出现在0,1,2，..N列表中。
你可以在不打扰交叉路口，集合等的情况下找到它：
one = ['cat', 'dog', 'pine']
two = ['cat', 'fan', 'pine']
three = ['cat', 'pine', 'tree']
four = ['dog', 'pine', 'tree']
five = ['fan', 'pine', 'tree']
six = ['light', 'pine', 'tree']
>>> sum(True for s in (one, two, three, four, five, six) if 'cat' in s)
3
>>> sum(True for s in (one, two, three, four, five, six) if 'tree' in s)
4
这是有效的，因为True在算术中使用时就像整数1一样（sum()所基于的）。
如果您真正想要的是所有“集合”的交集，那也很简单：
>>> set.intersection(*(set(s) for s in (one, two, three, four, five, six)))
{'pine'}
更新：现在您已经澄清了问题，很明显您确实需要计算各个列表中单词出现次数。除了上面描述的方法来计算单个单词的出现次数，正如我在my comment Andrea Reina's answer上提到的那样（并且Moinuddin Quadri随后添加到his own answer），这是一种惯用的方法在Python中使用collections.Counter和itertools.chain：
>>> from collections import Counter
>>> from itertools import chain
>>> counts = Counter(chain(one, two, three, four, five, six))
>>> counts
Counter({'pine': 6, 'tree': 4, 'cat': 3, 'dog': 2, 'fan': 2, 'light': 1})
>>> counts['cat']
3
答案 2 :(得分：0)
如果您使用的是Python 3.x，则可以使用reduce（functools.reduce）
>>> from functools import reduce # for python 3.x
>>> animals_list = [
... ['cat', 'dog', 'pine', 'tree', 'light', 'fan'],
... ['cat', 'pine', 'tree', 'light', 'fan'],
... ['cat', 'dog', 'pine', 'light', 'fan'],
... ['cat', 'dog', 'pine', 'tree', 'fan'],
... ]
>>> reduce(lambda x, y: set(x).intersection(y), animals_list)
{'pine', 'fan', 'cat'}
答案 3 :(得分：0)
如果您可以使用frozenset代替list，这是一个概括：
要创建所有组合，我们使用itertools.combinations：
from itertools import combinations
我们将使用可用的frozenset（稍后用作dict键）：
sets = tuple(frozenset(s) for s in (set([1,2,3]), set([2,3,4]), set([3,4,5]), set([4,5,6])))
通过在所有组合中应用frozenset.intersections来创建映射（选择大小为3的组合作为示例），将结果存储在dict中：
intersections = {frozenset(k): frozenset.intersection(*k) for k in combinations(sets, 3)}
结果：
{frozenset({frozenset({2, 3, 4}), frozenset({3, 4, 5}), frozenset({4, 5, 6})}): frozenset({4}), frozenset({frozenset({1, 2, 3}), frozenset({2, 3, 4}), frozenset({4, 5, 6})}): frozenset(), frozenset({frozenset({1, 2, 3}), frozenset({3, 4, 5}), frozenset({4, 5, 6})}): frozenset(), frozenset({frozenset({1, 2, 3}), frozenset({2, 3, 4}), frozenset({3, 4, 5})}): frozenset({3})}
答案 4 :(得分：0)
one = ['cat', 'dog', 'fan']
two = ['cat', 'dog', 'pine']
three = ['cat', 'light', 'tree']
four = ['dog', 'pine', 'tree']
five = ['fan', 'pine', 'tree']
six = ['light', 'pine', 'tree']
lists = one + two + three + four + five + six
[(e, lists.count(e)) for e in set(lists)]
# => [('light', 2), ('tree', 4), ('dog', 3), ('pine', 4), ('cat', 3), ('fan', 2)]