Python模块之collections

Posted 2021-02-20 Bright

一、概述

在collections的源码中，可以看到：

‘‘‘This module implements specialized container datatypes providing
alternatives to Python‘s general purpose built-in containers, dict,
list, set, and tuple.

* namedtuple   factory function for creating tuple subclasses with named fields
* deque        list-like container with fast appends and pops on either end
* ChainMap     dict-like class for creating a single view of multiple mappings
* Counter      dict subclass for counting hashable objects
* OrderedDict  dict subclass that remembers the order entries were added
* defaultdict  dict subclass that calls a factory function to supply missing values
* UserDict     wrapper around dictionary objects for easier dict subclassing
* UserList     wrapper around list objects for easier list subclassing
* UserString   wrapper around string objects for easier string subclassing

‘‘‘

__all__ = [‘deque‘, ‘defaultdict‘, ‘namedtuple‘, ‘UserDict‘, ‘UserList‘,
            ‘UserString‘, ‘Counter‘, ‘OrderedDict‘, ‘ChainMap‘]

这也就说明collections模块包含以下内容：

• deque
• defaultdict
• namedtuple
• UserDict
• UserList
• UserString
• Counter
• OrderDict
• ChainMap

二、namedTuple

（一）Tuple

namedTuple是Tuple的子类，所以Tuple有的特性，namedTuple都存在，那么Tuple有什么特性呢？

1、不可变类型

Tuple是不可变的数据类型：

>>> user_tuple = ("zhangsan",30) #创建Tuple对象

一旦创建不可更改，比如做如下的更改操作：

>>> user_tuple[1] = 32

就会报错：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: ‘tuple‘ object does not support item assignment

但是，Tuple的不可变也不是绝对的，我们看到Tuple内部的元素都是不可变的，如果改变内部可变的数据类型是没有问题的：

>>> user_tuple = ("zhangsan",30,["reading","movies"])
>>> user_tuple[2].append("animals")
>>> user_tuple
(‘zhangsan‘, 30, [‘reading‘, ‘movies‘, ‘animals‘])
>>>

2、可迭代对象

Tuple像列表等数据类型一样，是可迭代对象，自然拥有循环取值、切片这些特性：

#for循环迭代取值
user_tuple = ("zhangsan",30)
for el in user_tuple:
    print(el)

3、拆包

number_Tuple = (1,2,3,4)

first,*others = number_Tuple
print(first,others) #1 [2, 3, 4]

4、作为字典的key

字典的key都是不可变类型，也就是说必须是可哈希的：

condition = ("a","b")
filter_dict = {}
filter_dict[condition] = "result"
print(filter_dict) #{(‘a‘, ‘b‘): ‘result‘}

（二）namedTuple

1、类创建

我们一般创建类是这样来创建的：

class User:

    def __init__(self,username,password):
        self.username = username
        self.password = password

user = User("zhangsan",123456)
print(user.username,user.password) #zhangsan 123456

但是使用namedTuple可以更简单的创建：

User = namedtuple("User",["username","password"])
user = User("zhangsan",123456)
print(user.username,user.password) #zhangsan 123456

至于参数传递实际上与class类中传递是一样的，可以通过*args，**kwargs。

#Tuple传值
User = namedtuple("User",["username","password"])
args = ("zhangsan",123456)
user = User(*args)
print(user.username,user.password) #zhangsan 123456

#Dict传值
User = namedtuple("User",["username","password"])
kwargs = {"username":"zhangsan","password":123456}
user = User(**kwargs)
print(user.username,user.password) #zhangsan 123456

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2、_make

在上面的传值中，我们使用**args或者**kwargs来进行传值，那么通过_make方法可以更简单的进行传值：

from collections import namedtuple

 #define class
User = namedtuple("User",["username","password"])

#define parameters
parameters_list = ["zhangsan",123456]
parameters_tuple = ("zhangsan",123456)
parameters_dict = {"username":"zhangsan","password":123456}

#init object
user = User._make(parameters_list)
user1 = User._make(parameters_tuple)
user2 = User._make(parameters_dict)

#output
print(user.username,user.password) #zhangsan 123456
print(user1.username,user.password) #zhangsan 123456
print(user2.username,user.password) #zhangsan 123456

可以看到在_make方法中只需要传递可迭代对象的参数即可。

@classmethod
def _make(cls, iterable, new=tuple.__new__, len=len):
    ‘Make a new {typename} object from a sequence or iterable‘
    result = new(cls, iterable)
    if len(result) != {num_fields:d}:
        raise TypeError(‘Expected {num_fields:d} arguments, got %d‘ % len(result))
    return result

_make

3、_asdict

该方法可以输出OrderDict类型的结果，将字典进行排序后输出。

from collections import namedtuple

User = namedtuple("User",["username","password"])
kwargs = {"username":"zhangsan"}
user = User(**kwargs,password=123456)
print(user) #User(username=‘zhangsan‘, password=123456)

user_dict = user._asdict()
print(user_dict) #OrderedDict([(‘username‘, ‘zhangsan‘), (‘password‘, 123456)])

三、defaultdict

defaultdict是内置dict的子类，也就是说dict有的特性它都有，另外在源码中：

class defaultdict(dict):

    def __init__(self, default_factory=None, **kwargs): # known case of _collections.defaultdict.__init__
        """
        defaultdict(default_factory[, ...]) --> dict with default factory
        
        The default factory is called without arguments to produce
        a new value when a key is not present, in __getitem__ only.
        A defaultdict compares equal to a dict with the same items.
        All remaining arguments are treated the same as if they were
        passed to the dict constructor, including keyword arguments.
        
        # (copied from class doc)
        """
        pass

从这里可以知道有一个参数是default_factory函数，它是在当dict中的key不存在时，会被给予给默认值。假如现在有这样一个实例：

s = [‘yellow‘, ‘blue‘,‘yellow‘, ‘blue‘,‘red‘]

统计s列表中每个元素出现的个数，我们可能更多的使用如下的方式来实现：

from collections import defaultdict

s = [‘yellow‘, ‘blue‘,‘yellow‘, ‘blue‘,‘red‘]

count_dict = {}
for i in s:
    if i not in count_dict:
        count_dict[i] = 1
    else:
        count_dict[i] += 1

print(count_dict) #{‘yellow‘: 2, ‘blue‘: 2, ‘red‘: 1}

使用defaultdict可以更容易的来实现上述过程：

from collections import defaultdict

s = [‘yellow‘, ‘blue‘,‘yellow‘, ‘blue‘,‘red‘]

d = defaultdict(int) #key值不存在就会使用int类型的默认值，默认为0，相当于{“yellow”:0,"blue":0,"red":0}
for i in s:
    d[i] += 1
print(d) #defaultdict(<class ‘int‘>, {‘red‘: 1, ‘yellow‘: 2, ‘blue‘: 2})

另外可以使用其构造更为复杂的数据结构：

from collections import defaultdict

def gen_default():
    return {
        "username":"",
        "age":0
    }

d = defaultdict(gen_default)
d["g1"]  #g1键值不存在会生成默认的数据结构{"g1":{"username":"","age":0}}

四、deque

（一）deque初始化

先看源码：

class deque(object):

    def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__
        """
        deque([iterable[, maxlen]]) --> deque object
        
        A list-like sequence optimized for data accesses near its endpoints.
        # (copied from class doc)
        """
        pass

初始化一个双端队列的话，需要传入一个可迭代对象。

from collections import deque
d = deque(["a","b","c"])
print(d) #deque([‘a‘, ‘b‘, ‘c‘])

当然，也可以传入元祖和字典（得到的是key值）。

（二）deque方法

deque中有很多方法：

class deque(object):
    """
    deque([iterable[, maxlen]]) --> deque object
    
    A list-like sequence optimized for data accesses near its endpoints.
    """
    def append(self, *args, **kwargs): # real signature unknown
        """ Add an element to the right side of the deque. """
        pass

    def appendleft(self, *args, **kwargs): # real signature unknown
        """ Add an element to the left side of the deque. """
        pass

    def clear(self, *args, **kwargs): # real signature unknown
        """ Remove all elements from the deque. """
        pass

    def copy(self, *args, **kwargs): # real signature unknown
        """ Return a shallow copy of a deque. """
        pass

    def count(self, value): # real signature unknown; restored from __doc__
        """ D.count(value) -> integer -- return number of occurrences of value """
        return 0

    def extend(self, *args, **kwargs): # real signature unknown
        """ Extend the right side of the deque with elements from the iterable """
        pass

    def extendleft(self, *args, **kwargs): # real signature unknown
        """ Extend the left side of the deque with elements from the iterable """
        pass

    def index(self, value, start=None, stop=None): # real signature unknown; restored from __doc__
        """
        D.index(value, [start, [stop]]) -> integer -- return first index of value.
        Raises ValueError if the value is not present.
        """
        return 0

    def insert(self, index, p_object): # real signature unknown; restored from __doc__
        """ D.insert(index, object) -- insert object before index """
        pass

    def pop(self, *args, **kwargs): # real signature unknown
        """ Remove and return the rightmost element. """
        pass

    def popleft(self, *args, **kwargs): # real signature unknown
        """ Remove and return the leftmost element. """
        pass

    def remove(self, value): # real signature unknown; restored from __doc__
        """ D.remove(value) -- remove first occurrence of value. """
        pass

    def reverse(self): # real signature unknown; restored from __doc__
        """ D.reverse() -- reverse *IN PLACE* """
        pass

    def rotate(self, *args, **kwargs): # real signature unknown
        """ Rotate the deque n steps to the right (default n=1).  If n is negative, rotates left. """
        pass

源码中的方法

1、pop

from collections import deque
d = deque(["a","b","c"])
print(d.pop()) #c
print(d) #deque([‘a‘, ‘b‘])

2、popleft

from collections import deque
d = deque(["a","b","c"])
print(d.popleft()) #a
print(d) #deque([‘b‘, ‘c‘])

3、append

from collections import deque
d = deque(["a","b","c"])
d.append("d") 
print(d) #deque([‘a‘, ‘b‘, ‘c‘, ‘d‘])

4、appendleft

from collections import deque
d = deque(["a","b","c"])
d.appendleft("d")
print(d) #deque([‘d‘, ‘a‘, ‘b‘, ‘c‘])

5、extend

from collections import deque
d1 = deque(["a","b","c"])
d2 = deque(["d","e","f"])

d1.extend(d2)
print(d1) #deque([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘f‘])

注意：extend没有返回值，d1调用extend就是对d1的扩展。

6、insert

from collections import deque
d = deque(["a","b","c"])
d.insert(1,"d")
print(d) #deque([‘a‘, ‘d‘, ‘b‘, ‘c‘])

7、reverse

from collections import deque
d = deque(["a","b","c"])
d.reverse()
print(d) #deque([‘c‘, ‘b‘, ‘a‘])

8、copy

from collections import deque
d1 = deque(["a","b","c"])
d2 = d1.copy()

#id不同证明是不同的变量
print(id(d1)) #173766656
print(id(d2)) #173766864

#拷贝之后操作d1对d2没影响
d1.insert(2,"d")
print(d1) #deque([‘a‘, ‘b‘, ‘d‘, ‘c‘])
print(d2) #deque([‘a‘, ‘b‘, ‘c‘])

#如果d1中有可变元素
d3 = deque(["a","b",["c","d"]])
d4 = d3.copy()
print(id(d3)) #173570256
print(id(d4)) #173570360
#操作可变元素,也就是说虽然d3和d4是不同的变量了，但是对于内部的可变元素是指引，不可变元素才是真正的拷贝互不影响
d3[2].append("e")
print(d3) #deque([‘a‘, ‘b‘, [‘c‘, ‘d‘, ‘e‘]])
print(d4) #deque([‘a‘, ‘b‘, [‘c‘, ‘d‘, ‘e‘]])

还有很多方法，其余的可以参考源码进行学习。

五、Counter 

　　Counter类是Python内置dict的一个子类，也就是说dict有的特性它都有，它主要是用来进行数据统计的。它是一个无序集合，其中元素被存储为字典的键，计数被存储为字典的值。计数可以被允许是整数、零或者负数。

（一）统计个数

可以向Counter类中传递可迭代对象，比如：字符串、列表：

1、字符串统计

from collections import Counter

counter1 = Counter("ABCDAD")
print(counter1) #Counter({‘D‘: 2, ‘A‘: 2, ‘C‘: 1, ‘B‘: 1})
"""
因为Counter返回的是一个字典（是dict的子类），所以可以有字典的方法
"""
counter2 = Counter("DEFABC")
counter1.update(counter2)
print(counter1) #Counter({‘A‘: 3, ‘D‘: 3, ‘C‘: 2, ‘B‘: 2, ‘F‘: 1, ‘E‘: 1})

2、列表统计

from collections import Counter

counter1 = Counter(["A", "B", "C", "D", "A", "D"])
print(counter1) #Counter({‘D‘: 2, ‘A‘: 2, ‘C‘: 1, ‘B‘: 1})
"""
因为Counter返回的是一个字典（是dict的子类），所以可以有字典的方法
"""
counter2 = Counter(["D", "E", "F", "A", "B", "C"])
counter1.update(counter2)
print(counter1) #Counter({‘A‘: 3, ‘D‘: 3, ‘C‘: 2, ‘B‘: 2, ‘F‘: 1, ‘E‘: 1})

（二）TopN问题

在Counter类中有一个most_common方法返回的是个数最多的前几项。

from collections import Counter

top3 = Counter(‘abcdeabcdabcaba‘).most_common(3)
print(top3) #[(‘a‘, 5), (‘b‘, 4), (‘c‘, 3)]

源码：

class Counter(dict):

    def most_common(self, n=None):
        ‘‘‘List the n most common elements and their counts from the most
        common to the least.  If n is None, then list all element counts.

        >>> Counter(‘abcdeabcdabcaba‘).most_common(3)
        [(‘a‘, 5), (‘b‘, 4), (‘c‘, 3)]

        ‘‘‘
        # Emulate Bag.sortedByCount from Smalltalk
        if n is None:
            return sorted(self.items(), key=_itemgetter(1), reverse=True)
        return _heapq.nlargest(n, self.items(), key=_itemgetter(1))

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（三）其它方法

1、elements

#迭代器遍历每个元素的次数与它的计数相同
c = Counter("ABCDAD")
print(sorted(c.elements())) #[‘A‘, ‘A‘, ‘B‘, ‘C‘, ‘D‘, ‘D‘]

2、subtract

元素从一个可迭代的或从另一个映射(或计数器)中减去。

from collections import Counter

c = Counter(a=4, b=2, c=0, d=-2)
d = Counter(a=1, b=2, c=3, d=4)
c.subtract(d)
print(c) #Counter({‘a‘: 3, ‘b‘: 0, ‘c‘: -3, ‘d‘: -6})

六、OrderDict

OrderDict是dict的子类，它拥有dict的所有特性，而它本身是有序的（记住插入顺序的字典）。

from collections import OrderedDict

d = OrderedDict() #初始化一个字典
d["a"] = 1
d["b"] = 2
d["c"] = 3
print(d) #OrderedDict([(‘a‘, 1), (‘b‘, 2), (‘c‘, 3)])

可以看到最后生成的结果并不是无序的，而是按照插入到字典中的元素进行排序的。

在OrderDict中有很多的方法，比如：

1、popitem

移除最后一个添加的元素。

from collections import OrderedDict

d = OrderedDict() #初始化一个字典
d["a"] = 1
d["b"] = 2
d["c"] = 3
print(d) #OrderedDict([(‘a‘, 1), (‘b‘, 2), (‘c‘, 3)])
print(d.popitem()) #(‘c‘, 3)
print(d) #OrderedDict([(‘a‘, 1), (‘b‘, 2)])

2、move_to_end

移动一个已经存在的元素到OrderDict的元素最后。

from collections import OrderedDict

d = OrderedDict() #初始化一个字典
d["a"] = 1
d["b"] = 2
d["c"] = 3
print(d) #OrderedDict([(‘a‘, 1), (‘b‘, 2), (‘c‘, 3)])
d.move_to_end("b")
print(d) #OrderedDict([(‘a‘, 1), (‘c‘, 3), (‘b‘, 2)])

3、pop

移除指定key值得元素

from collections import OrderedDict

d = OrderedDict() #初始化一个字典
d["a"] = 1
d["b"] = 2
d["c"] = 3
print(d) #OrderedDict([(‘a‘, 1), (‘b‘, 2), (‘c‘, 3)])
print(d.pop("b")) #2
print(d) #OrderedDict([(‘a‘, 1), (‘c‘, 3)])

七、ChainMap

 Chain是将多个dict或者映射组合在一起，从而创建单一的、可更新的视图。比如下面的情况：

d1 = {"a":1,"b":2}
d2 = {"c":1,"d":2}

#循环打印上面的字典
for k,v in d1.items():
    print(k,v)

for k,v in d2.items():
    print(k,v)

上面的两个字典，分别单独使用for循环打印，如果使用ChainMap就可以这样来做：

from collections import ChainMap

d1 = {"a":1,"b":2}
d2 = {"c":1,"d":2}

d3 = ChainMap(d1,d2)
print(d3) #ChainMap({‘a‘: 1, ‘b‘: 2}, {‘c‘: 1, ‘d‘: 2})

for k,v in d3.items():
    print(k,v)

注意的是ChainMap对两个字典的合并并非是将其拷贝到另一个空间进行合并，只是对之前的两个字典进行指向。当然除了合并还有其它方法，比如：

1、new_child

返回一个新的ChainMap，其中包含一个新映射，以及当前实例中的所有映射。

from collections import ChainMap

d1 = {"a":1,"b":2}
d2 = {"c":1,"d":2}

d3 = ChainMap(d1,d2)

d4 = d3.new_child({"e":5}) #添加新的ChainMap
print(d4) # ChainMap({‘e‘: 5}, {‘b‘: 2, ‘a‘: 1}, {‘d‘: 2, ‘c‘: 1})

2、parents

这是一个属性，返回一个新的ChainMap包含当前实例中除了第一个以外所有的maps。

from collections import ChainMap

d1 = {"a":1,"b":2}
d2 = {"c":1,"d":2}

d3 = ChainMap(d1,d2)

print(d3.parents) #ChainMap({‘d‘: 2, ‘c‘: 1})

3、maps

这是一个属性，返回的是所有maps组成的列表。

from collections import ChainMap

d1 = {"a":1,"b":2}
d2 = {"c":1,"d":2}

d3 = ChainMap(d1,d2)

print(d3.maps) #[{‘b‘: 2, ‘a‘: 1}, {‘d‘: 2, ‘c‘: 1}]