collections模块

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     我们知道，Python中有很多模块，有系统自带的模块，还有些需要从网上下载模块，Python之所以越来越受欢迎，主要是由于Python的类库很多，丰富多样，有很多人开发和维护。下面我们来学习了解collections模块，这也是我第一个系统了解的模块，希望多练习，多看掌握模块的知识。

一、Counter(dict)类

class Counter(dict):
    ‘‘‘Dict subclass for counting hashable items.  Sometimes called a bag
    or multiset.  Elements are stored as dictionary keys and their counts
    are stored as dictionary values.

    >>> c = Counter(‘abcdeabcdabcaba‘)  # count elements from a string

    >>> c.most_common(3)                # three most common elements
    [(‘a‘, 5), (‘b‘, 4), (‘c‘, 3)]
    >>> sorted(c)                       # list all unique elements
    [‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘]
    >>> ‘‘.join(sorted(c.elements()))   # list elements with repetitions
    ‘aaaaabbbbcccdde‘
    >>> sum(c.values())                 # total of all counts
    15

    >>> c[‘a‘]                          # count of letter ‘a‘
    5
    >>> for elem in ‘shazam‘:           # update counts from an iterable
    ...     c[elem] += 1                # by adding 1 to each element‘s count
    >>> c[‘a‘]                          # now there are seven ‘a‘
    7
    >>> del c[‘b‘]                      # remove all ‘b‘
    >>> c[‘b‘]                          # now there are zero ‘b‘
    0

    >>> d = Counter(‘simsalabim‘)       # make another counter
    >>> c.update(d)                     # add in the second counter
    >>> c[‘a‘]                          # now there are nine ‘a‘
    9

    >>> c.clear()                       # empty the counter
    >>> c
    Counter()

    Note:  If a count is set to zero or reduced to zero, it will remain
    in the counter until the entry is deleted or the counter is cleared:

    >>> c = Counter(‘aaabbc‘)
    >>> c[‘b‘] -= 2                     # reduce the count of ‘b‘ by two
    >>> c.most_common()                 # ‘b‘ is still in, but its count is zero
    [(‘a‘, 3), (‘c‘, 1), (‘b‘, 0)]

    ‘‘‘
    # References:
    #   http://en.wikipedia.org/wiki/Multiset
    #   http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html
    #   http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm
    #   http://code.activestate.com/recipes/259174/
    #   Knuth, TAOCP Vol. II section 4.6.3

    从上面可以看出，模块中包含Counter(dict)类，而且Counter(dict)继承父类dict类，因此Counter拥有父类的功能，下面我们来看一下Counter
类中都包含了那些方法：
    
    >>> c = Counter(‘abcdeabcdabcaba‘)  # count elements from a string
    首先，我们定义一个计数器c，用于存储Counter()中的结果，看结果是什么：
    >>> c
　　Counter({‘a‘: 5, ‘b‘: 4, ‘c‘: 3, ‘d‘: 2, ‘e‘: 1})
    从结果可以看出，生成了一个Counter({})类似于字典的形式，这是对字典的加工，具有字典的性质，我们可以使用c.keys()、c.values()来遍历
这个Counter中的键和值。

    >>> c.most_common(3)                # three most common elements
    [(‘a‘, 5), (‘b‘, 4), (‘c‘, 3)]
    >>> type(c.most_common(3))
　　<class ‘list‘>
    取列表中前三个元素最多的键值对，生成一个列表，返回前三个元素最多的列表。
    >>> sorted(c)                       # list all unique elements
    [‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘]
    对对象c中的元素进行排序，并且生成一个列表，排序，对对象中的fauns排序
    >>> ‘‘.join(sorted(c.elements()))   # list elements with repetitions
    ‘aaaaabbbbcccdde‘
    >>> sorted(c.elements())
　　[‘a‘, ‘a‘, ‘a‘, ‘a‘, ‘a‘, ‘b‘, ‘b‘, ‘b‘, ‘b‘, ‘c‘, ‘c‘, ‘c‘, ‘d‘, ‘d‘, ‘e‘]
    对对象c中的所有元素进行排序，使用字符串的join(sorted(c.elements()))功能。
     >>> sum(c.values()) # total of all counts
     15
    遍历Counter中的值，并且求和。
    >>> c[‘a‘]        # count of letter ‘a‘ 统计letter中"a"的个数
    5
    >>> for elem in ‘shazam‘:           # update counts from an iterable
    ...     c[elem] += 1                # by adding 1 to each element‘s count
    >>> c[‘a‘]                          # now there are seven ‘a‘
    7
    >>> print(c)
　　Counter({‘a‘: 7, ‘b‘: 4, ‘c‘: 3, ‘d‘: 2, ‘z‘: 1, ‘h‘: 1, ‘e‘: 1, ‘s‘: 1, ‘m‘: 1})
    遍历"shazam"中的每个元素，向c中Counter()添加元素，如果没有这个元素，就增加，如有有，就个数加一，直到元素循环完毕。

    >>> del c[‘b‘]                      # remove all ‘b‘
    >>> c[‘b‘]                          # now there are zero ‘b‘
    0
    >>> c
　　Counter({‘a‘: 7, ‘c‘: 3, ‘d‘: 2, ‘z‘: 1, ‘h‘: 1, ‘e‘: 1, ‘s‘: 1, ‘m‘: 1})
    删除Counter()中的元素b,删除的是元素的值，Counter()是一个计数器的功能，统计对象中的元素用的，只需要统计对象中的元素，因此即便
Counter()中没有这个值，也不会报错，只会返回0，代表没有，这就是计数器的功能，只是用来计数，包含字典的功能。但是本质上还是用来进行
计数的，能够使用数学的算法。
    >>> c = Counter(‘aaabbc‘)
    >>> c[‘b‘] -= 2                     # reduce the count of ‘b‘ by two
    >>> c.most_common()                 # ‘b‘ is still in, but its count is zero
    [(‘a‘, 3), (‘c‘, 1), (‘b‘, 0)]
    >>> c = Counter("aaabbc")
　　>>> c
　　Counter({‘a‘: 3, ‘b‘: 2, ‘c‘: 1})
　　>>> c["b"] -= 2
　　>>> c.most_common()
　　[(‘a‘, 3), (‘c‘, 1), (‘b‘, 0)]
　　>>> c = Counter("aaabbc")
　　>>> c["b"] -= 3
　　>>> c
　　Counter({‘a‘: 3, ‘c‘: 1, ‘b‘: -1})
　　>>> c.most_common(3)
　　[(‘a‘, 3), (‘c‘, 1), (‘b‘, -1)]
    Counter()计数器功能，统计对象中的个数，生成一个Counter()字典。
    1.most_common(self,n=None)
    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))
    返回Counter中数量最多的前三个元素，并将结果封装到一个列表中，列表中的每个元素都是一个元组。
    2.elements(self)
    def elements(self):
    　　‘‘‘Iterator over elements repeating each as many times as its count.

    　　>>> c = Counter(‘ABCABC‘)
    　　>>> sorted(c.elements())
    　　[‘A‘, ‘A‘, ‘B‘, ‘B‘, ‘C‘, ‘C‘]

    　　# Knuth‘s example for prime factors of 1836:  2**2 * 3**3 * 17**1
   　　 >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
    　　>>> product = 1
   　　 >>> for factor in prime_factors.elements():     # loop over factors
    　　...     product *= factor                       # and multiply them
    　　>>> product
    　　1836

    　　Note, if an element‘s count has been set to zero or is a negative
    　　number, elements() will ignore it.

    　　‘‘‘
    　　# Emulate Bag.do from Smalltalk and Multiset.begin from C++.
    　　return _chain.from_iterable(_starmap(_repeat, self.items()))
    elements(self)是返回对象中的每个元素，下面来验证一下：
    >>> c = Counter("abacdefa")
　　>>> for item in c.elements():
　　...     print(item)
　　... 
　　b
　　f
　　d
　　e
　　a
　　a
　　a
　　c
　　>>> c.elements()
　　<itertools.chain object at 0x7f1783cf5278>
　　>>> list(c.elements())
　　[‘b‘, ‘f‘, ‘d‘, ‘e‘, ‘a‘, ‘a‘, ‘a‘, ‘c‘]
　　>>> sorted(c.elements())
　　[‘a‘, ‘a‘, ‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘, ‘f‘]
    我们定义一个Counter计数器，并且使用elements()遍历其中的每个元素，并且打印其中的消息，最后使用list()生成一个列表。使用sorted()
函数进行排序。
    Conter()类具有dict(字典)中的方法，实例如下：
    >>> c = Counter({‘a‘: 3, ‘b‘: 1, ‘f‘: 1, ‘d‘: 1, ‘e‘: 1, ‘c‘: 1})
　　>>> for k,v in c.items():
　　...     print(k,v)
　　... 
　　b 1
　　f 1
　　d 1
　　e 1
　　a 3
　　c 1
    3.update(*args,**kwargs)
    def update(*args, **kwds):
    ‘‘‘Like dict.update() but add counts instead of replacing them.

    Source can be an iterable, a dictionary, or another Counter instance.

    >>> c = Counter(‘which‘)
    >>> c.update(‘witch‘)           # add elements from another iterable
    >>> d = Counter(‘watch‘)
    >>> c.update(d)                 # add elements from another counter
    >>> c[‘h‘]                      # four ‘h‘ in which, witch, and watch
    4

    ‘‘‘
    # The regular dict.update() operation makes no sense here because the
    # replace behavior results in the some of original untouched counts
    # being mixed-in with all of the other counts for a mismash that
    # doesn‘t have a straight-forward interpretation in most counting
    # contexts.  Instead, we implement straight-addition.  Both the inputs
    # and outputs are allowed to contain zero and negative counts.
    update(*args,**kwargs)是向Counter中添加元素，如果里面包含这个元素就加一，包含两个就加二，没有就新加入，就是更新计数器，统计里面
包含的元素的个数。实例如下：
    >>> c = Counter("which")
　　>>> c.update("witch")
　　>>> c
　　Counter({‘h‘: 3, ‘w‘: 2, ‘c‘: 2, ‘i‘: 2, ‘t‘: 1})
　　>>> d = Counter("watch")
　　>>> c.update(d)
　　>>> c
　　Counter({‘h‘: 4, ‘w‘: 3, ‘c‘: 3, ‘t‘: 2, ‘i‘: 2, ‘a‘: 1})
　　>>> c["d"]
　　0
    >>> c["h"]
　　4  
    可以向添加任意对象，["ww"]会被当成一个元素传递给计数器。
    4.def subtract(*args,**kwargs)
    def subtract(*args, **kwds):
   　　 ‘‘‘Like dict.update() but subtracts counts instead of replacing them.
    　　Counts can be reduced below zero.  Both the inputs and outputs are
    　　allowed to contain zero and negative counts.

    　　Source can be an iterable, a dictionary, or another Counter instance.

   　　 >>> c = Counter(‘which‘)
  　　  >>> c.subtract(‘witch‘)             # subtract elements from another iterable
  　　  >>> c.subtract(Counter(‘watch‘))    # subtract elements from another counter
   　　 >>> c[‘h‘]                          # 2 in which, minus 1 in witch, minus 1 in watch
   　　 0
  　　  >>> c[‘w‘]                          # 1 in which, minus 1 in witch, minus 1 in watch
  　　  -1

 　　   ‘‘‘
 subtract(*args,**kwargs)与update(*args,**kwargs)正好相反，一个是加，一个是减，减掉里面元素的个数，计数器。
    >>> c = Counter("which")
　　>>> c.subtract("witch")
　　>>> c
　　Counter({‘h‘: 1, ‘w‘: 0, ‘c‘: 0, ‘i‘: 0, ‘t‘: -1})
　　>>> c.subtract(Counter("watch"))
　　>>> c
　　Counter({‘h‘: 0, ‘i‘: 0, ‘w‘: -1, ‘a‘: -1, ‘c‘: -1, ‘t‘: -2})
　　>>> c["w"]
　　-1
　　>>> c["h"]
　　0  
    5.copy(self)
    def copy(self):
    　　‘Return a shallow copy.‘
    　　return self.__class__(self) 
    复制Counter计数器。
    >>> c = Counter("which")
　　>>> d = c
　　>>> d
　　Counter({‘h‘: 2, ‘w‘: 1, ‘c‘: 1, ‘i‘: 1})
　　>>> c
　　Counter({‘h‘: 2, ‘w‘: 1, ‘c‘: 1, ‘i‘: 1})
    6.__reduce__(self)
    def __reduce__(self):
   　　return self.__class__, (dict(self),)
    
　　7.__delitem__(self,elem)
    def __delitem__(self, elem):
    　　‘Like dict.__delitem__() but does not raise KeyError for missing values.‘
    　　if elem in self:
      　　  super().__delitem__(elem)
    __delitem__(self,elem)删除Counter类中的指定的元素，示例如下：
    >>> c = Counter("abcadcedfad")
　　>>> c.__delitem__("a")
　　>>> c
　　Counter({‘d‘: 3, ‘c‘: 2, ‘b‘: 1, ‘f‘: 1, ‘e‘: 1})
    8.__repr__(self)
    def __repr__(self):
    　　if not self:
        　　return ‘%s()‘ % self.__class__.__name__
    　　try:
        　　items = ‘, ‘.join(map(‘%r: %r‘.__mod__, self.most_common()))
        　　return ‘%s({%s})‘ % (self.__class__.__name__, items)
   　　 except TypeError:
        　　# handle case where values are not orderable
        　　return ‘{0}({1!r})‘.format(self.__class__.__name__, dict(self))
    >>> c = Counter("abdcadsdfs")
　　>>> c
　　Counter({‘d‘: 3, ‘s‘: 2, ‘a‘: 2, ‘b‘: 1, ‘f‘: 1, ‘c‘: 1})
　　>>> c.__repr__()
　　"Counter({‘d‘: 3, ‘s‘: 2, ‘a‘: 2, ‘b‘: 1, ‘f‘: 1, ‘c‘: 1})"
　　>>> type(c.__repr__())
　　<class ‘str‘>
    __repr__(self)不需要参数，但是返回的类型是一个字符串类型，生成一个类似于Counter()字典的字符串。
    9.__iand__(self,other):
    def __iand__(self, other):
    　　‘‘‘Inplace intersection is the minimum of corresponding counts.

   　　 >>> c = Counter(‘abbb‘)
   　　 >>> c &= Counter(‘bcc‘)
   　　 >>> c
    　　Counter({‘b‘: 1})

   　　 ‘‘‘
    　　for elem, count in self.items():
        　　other_count = other[elem]
        　　if other_count < count:
            　　self[elem] = other_count
    　　return self._keep_positive()
    10.__ior__(self,other)
    def __ior__(self, other):
    　　‘‘‘Inplace union is the maximum of value from either counter.

    　　>>> c = Counter(‘abbb‘)
   　　 >>> c |= Counter(‘bcc‘)
   　　 >>> c
    　　Counter({‘b‘: 3, ‘c‘: 2, ‘a‘: 1})

    　　‘‘‘
    　　for elem, other_count in other.items():
        　　count = self[elem]
        　　if other_count > count:
            　　self[elem] = other_count
    　　return self._keep_positive()
    11.__isub__(self,other)
    def __isub__(self, other):
    ‘‘‘Inplace subtract counter, but keep only results with positive counts.

    >>> c = Counter(‘abbbc‘)
    >>> c -= Counter(‘bccd‘)
    >>> c
    Counter({‘b‘: 2, ‘a‘: 1})

    ‘‘‘
    for elem, count in other.items():
        self[elem] -= count
    return self._keep_positive()
    __isub__(other)等价于self.subtract(other),类似于c -= Counter(other)
    12.__iadd__(self,other)
    def __iadd__(self, other):
    　　‘‘‘Inplace add from another counter, keeping only positive counts.

   　　 >>> c = Counter(‘abbb‘)
   　　 >>> c += Counter(‘bcc‘)
   　　 >>> c
   　　 Counter({‘b‘: 4, ‘c‘: 2, ‘a‘: 1})

    　　‘‘‘
   　　 for elem, count in other.items():
    　　    self[elem] += count
    　　return self._keep_positive()  
    实例如下：
    >>> c = Counter("abbb")
　　>>> c += Counter("bcc")
　　>>> c
　　Counter({‘b‘: 4, ‘c‘: 2, ‘a‘: 1})
　　>>> c = Counter("abbb")
　　>>> c.update("bcc")
　　>>> c
　　Counter({‘b‘: 4, ‘c‘: 2, ‘a‘: 1})
　　>>> c = Counter("abbb")
　　>>> c.__iadd__(Counter("bcc"))
　　Counter({‘b‘: 4, ‘c‘: 2, ‘a‘: 1})
    两个同类表的对象进行相加，属性要相同。
    13._keep_positive(slef)
    def _keep_positive(self):
    　　‘‘‘Internal method to strip elements with a negative or zero count‘‘‘
    　　nonpositive = [elem for elem, count in self.items() if not count > 0]
    　　for elem in nonpositive:
        　　del self[elem]
    　　return self
    _keep_positive(self)清除计数器中元素个数小于零的元素。nonpositive = [elem for elem,count in self.items() if not count >0]
    >>> c = Counter({‘b‘: 3, ‘a‘: 1})
　　>>> c._keep_positive()
　　Counter({‘b‘: 3, ‘a‘: 1})
　　>>> c.subtract("aa")
　　>>> c = Counter({‘b‘: 3, ‘a‘: -1})
　　>>> c._keep_positive()
　　Counter({‘b‘: 3})
    14.__neg__(self)
    def __neg__(self):
   　　 ‘‘‘Subtracts from an empty counter.  Strips positive and zero counts,
    　　and flips the sign on negative counts.

    　　‘‘‘
   　　 result = Counter()
    　　for elem, count in self.items():
        　　if count < 0:
           　　result[elem] = 0 - count
    　　return result    
    >>> c = Counter("abcada")
　　>>> c.subtract("aaaaddd")
　　>>> c
　　Counter({‘b‘: 1, ‘c‘: 1, ‘a‘: -1, ‘d‘: -2})
　　>>> c.__neg__()
　　Counter({‘d‘: 2, ‘a‘: 1})
    15.__pos__(self)
    def __pos__(self):
    　　‘Adds an empty counter, effectively stripping negative and zero counts‘
    　　result = Counter()
    　　for elem, count in self.items():
        　　if count > 0:
            　　result[elem] = count
   　　 return result
    __pos__(self)是计数器中元素个数大于1的元素，元素个数大于1处于positive状态，如下所示：
    >>> c = Counter("abcada")
　　>>> c.subtract("aaaaddd")
　　>>> c
　　Counter({‘b‘: 1, ‘c‘: 1, ‘a‘: -1, ‘d‘: -2})
　　>>> c.__pos__()
　　Counter({‘b‘: 1, ‘c‘: 1})
    16.__add__(self,other)
    def __add__(self, other):
    　　‘‘‘Add counts from two counters.

   　　 >>> Counter(‘abbb‘) + Counter(‘bcc‘)
   　　 Counter({‘b‘: 4, ‘c‘: 2, ‘a‘: 1})

   　　 ‘‘‘
    　　if not isinstance(other, Counter):
        　　return NotImplemented
    　　result = Counter()
    　　for elem, count in self.items():
       　　 newcount = count + other[elem]
        　　if newcount > 0:
            　　result[elem] = newcount
    　　for elem, count in other.items():
        　　if elem not in self and count > 0:
            　　result[elem] = count
    　　return result
    17.__sub__(self,other)
    def __sub__(self, other):
    　　‘‘‘ Subtract count, but keep only results with positive counts.

   　　 >>> Counter(‘abbbc‘) - Counter(‘bccd‘)
   　　 Counter({‘b‘: 2, ‘a‘: 1})

    　　‘‘‘
    　　if not isinstance(other, Counter):
        　　return NotImplemented
    　　result = Counter()
    　　for elem, count in self.items():
        　　newcount = count - other[elem]
       　　 if newcount > 0:
           　　 result[elem] = newcount
   　　 for elem, count in other.items():
        　　if elem not in self and count < 0:
            　　result[elem] = 0 - count
    　　return result
    __sub__(slef,other)两个Counter类相减，Counter(self)-Counter(other).
    18.__or__(self,other)
    def __or__(self, other):
    　　‘‘‘Union is the maximum of value in either of the input counters.

    　　>>> Counter(‘abbb‘) | Counter(‘bcc‘)
   　　 Counter({‘b‘: 3, ‘c‘: 2, ‘a‘: 1})

   　　 ‘‘‘
    　　if not isinstance(other, Counter):
        　　return NotImplemented
    　　result = Counter()
    　　for elem, count in self.items():
        　　other_count = other[elem]
        　　newcount = other_count if count < other_count else count
        　　if newcount > 0:
            　　result[elem] = newcount
    　　for elem, count in other.items():
       　　 if elem not in self and count > 0:
           　　 result[elem] = count
   　　 return result
    __or__(self,other)异或等价于Counter(self) | self(other).
    19.__and__(self,other)
    def __and__(self, other):
    　　‘‘‘ Intersection is the minimum of corresponding counts.
        异与，必须两者同时满足才为真，二进制下
    　　>>> Counter(‘abbb‘) & Counter(‘bcc‘)
    　　Counter({‘b‘: 1})

    　　‘‘‘
    　　if not isinstance(other, Counter):
        　　return NotImplemented
    　　result = Counter()
    　　for elem, count in self.items():
        　　other_count = other[elem]
        　　newcount = count if count < other_count else other_count
        　　if newcount > 0:
            　　result[elem] = newcount
   　　 return result
    __and__(self,other)等价于Counter(self) & Counter(other).

二、OrderedDict(dict)有序字典

class OrderedDict(dict):
    ‘Dictionary that remembers insertion order‘
    # An inherited dict maps keys to values.
    # The inherited dict provides __getitem__, __len__, __contains__, and get.
    # The remaining methods are order-aware.
    # Big-O running times for all methods are the same as regular dictionaries.

    # The internal self.__map dict maps keys to links in a doubly linked list.
    # The circular doubly linked list starts and ends with a sentinel element.
    # The sentinel element never gets deleted (this simplifies the algorithm).
    # The sentinel is in self.__hardroot with a weakref proxy in self.__root.
    # The prev links are weakref proxies (to prevent circular references).
    # Individual links are kept alive by the hard reference in self.__map.
    # Those hard references disappear when a key is deleted from an OrderedDict.

    我们知道正常我们使用的字典是无序的，而collections模块中的OrderedDict(dict)类能够实现有序字典的功能，当然也是继承了字典的父类，
具有字典的功能。上面介绍中，有序字典是给字典加上了顺序（order），下面来看看如何实现有序字典的功能：
    字典是无序的，但是我们知道列表是有序的，OrderedDict(dict)就是整合了字典与列表的功能。
    字典   {"k1":"v1","k2":"v2","k10":"v10"}
    列表   [ k1,       k2,       k10       ]
    有序字典内部实现的代码：
    dic = {"k1":"v1","k2":"v2","k10":"v10"}
    lis = ["k1","k2","k10"]
    for k in lis:
　　　　print(dic[k])
    这样我们通过遍历列表中的值，同时列表中的值属于字典中的键，由于列表是有序的，因此打印字典的时候也是按着列表中元素的顺序进行打印
的。
    创建有序字典的两种方法：
    （1）、import collections
    　　   dic = collections.OrderedDict()
    （2）、from collections import OrderedDict
           dic = OrderedDict()
    下面我们来看一个实例，有序字典和字典：
    from collections import OrderedDict

　　dic = OrderedDict()
　　dic["k1"] = "v1"
　　dic["k2"] = "v2"
　　dic["k3"] = "v3"
　　print(dic)
    结果如下：
    OrderedDict([(‘k1‘, ‘v1‘), (‘k2‘, ‘v2‘), (‘k3‘, ‘v3‘)])
    from collections import OrderedDict

　　dic = dict()
　　#dic = OrderedDict()
　　dic["k1"] = "v1"
　　dic["k2"] = "v2"
　　dic["k3"] = "v3"
　　print(dic)
    运行结果如下：
    {‘k2‘: ‘v2‘, ‘k1‘: ‘v1‘, ‘k3‘: ‘v3‘}
    可以看出，OrderedDict(dict)类生成的字典是有序的。所以我们在想让字典有序的时候记得调用collections模块中的OrderedDict()类功能。
    1.__init__(*args,**kwargs)
   def __init__(*args, **kwds):
    　　‘‘‘Initialize an ordered dictionary.  The signature is the same as
    　　regular dictionaries, but keyword arguments are not recommended because
    　　their insertion order is arbitrary.

    　　‘‘‘
    　　if not args:
        　　raise TypeError("descriptor ‘__init__‘ of ‘OrderedDict‘ object "
                        "needs an argument")
    　　self, *args = args
    　　if len(args) > 1:
        　　raise TypeError(‘expected at most 1 arguments, got %d‘ % len(args))
   　　 try:
        　　self.__root
    　　except AttributeError:
       　　self.__hardroot = _Link()
        　　self.__root = root = _proxy(self.__hardroot)
        　　root.prev = root.next = root
        　　self.__map = {}
    　　self.__update(*args, **kwds)
    2.__setitem__(self,key,value)
    def __setitem__(self, key, value,
                dict_setitem=dict.__setitem__, proxy=_proxy, Link=_Link):
    　　‘od.__setitem__(i, y) <==> od[i]=y‘
    　　# Setting a new item creates a new link at the end of the linked list,
   　　 # and the inherited dictionary is updated with the new key/value pair.
    　　if key not in self:
        　　self.__map[key] = link = Link()
       　　 root = self.__root
       　　 last = root.prev
        　　link.prev, link.next, link.key = last, root, key
        　　last.next = link
        　　root.prev = proxy(link)
    　　dict_setitem(self, key, value)  
    __setitem__(self,key,value)向有序字典中添加元素，要指明键和值，实例如下：
    >>> import collections
　　>>> dic = collections.OrderedDict()
　　>>> dic.__setitem__("k1","v1")
　　>>> dic["k2"] = "v2"
    >>> dic
　　OrderedDict([(‘k1‘, ‘v1‘), (‘k2‘, ‘v2‘)])
    3.__delitem__(self,key,dict_delitem=dict.__detitem__)
    def __delitem__(self, key, dict_delitem=dict.__delitem__):
    　　‘od.__delitem__(y) <==> del od[y]‘
   　　 # Deleting an existing item uses self.__map to find the link which gets
   　　 # removed by updating the links in the predecessor and successor nodes.
   　　 dict_delitem(self, key)
    　　link = self.__map.pop(key)
    　　link_prev = link.prev
    　　link_next = link.next
    　　link_prev.next = link_next
    　　link_next.prev = link_prev
    　　link.prev = None
    　　link.next = None
    4.pop(self,key,default=_marker)
    　　def pop(self, key, default=__marker):
   　　 ‘‘‘od.pop(k[,d]) -> v, remove specified key and return the corresponding
   　　 value.  If key is not found, d is returned if given, otherwise KeyError
   　　 is raised.

    　　‘‘‘
    　　if key in self:
        　　result = self[key]
        　　del self[key]
        　　return result
    　　if default is self.__marker:
        　　raise KeyError(key)
   　　 return default
    pop(self,key,default=_marker)是指定删除有序字典中的值，popitem(self)是默认删除最后一个元素的值，实例如下：
    >>> dic = OrderedDict([(‘k1‘, ‘v1‘), (‘k2‘, ‘v2‘), (‘k3‘, ‘v3‘), (‘k4‘, ‘v4‘)])
　　>>> dic.pop("k2")
　　‘v2‘
　　>>> dic
　　OrderedDict([(‘k1‘, ‘v1‘), (‘k3‘, ‘v3‘), (‘k4‘, ‘v4‘)])
    5.__repr__(self)
    def __repr__(self):
    　　‘od.__repr__() <==> repr(od)‘
    　　if not self:
        　　return ‘%s()‘ % (self.__class__.__name__,)
   　　 return ‘%s(%r)‘ % (self.__class__.__name__, list(self.items()))
    6.__reduce__(self)
    def __reduce__(self):
   　　 ‘Return state information for pickling‘
    　　inst_dict = vars(self).copy()
   　　 for k in vars(OrderedDict()):
        　　inst_dict.pop(k, None)
   　　 return self.__class__, (), inst_dict or None, None, iter(self.items())
    7.copy(self)
    def copy(self):
   　　 ‘od.copy() -> a shallow copy of od‘
    　　return self.__class__(self)
    8.fromkeys(cls,iterable,value=None)
    def fromkeys(cls, iterable, value=None):
    　　‘‘‘OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S.
    　　If not specified, the value defaults to None.

    　　‘‘‘
    　　self = cls()
    　　for key in iterable:
        　　self[key] = value
   　　 return self
    9.__eq__(self,other)
    def __eq__(self, other):
    　　‘‘‘od.__eq__(y) <==> od==y.  Comparison to another OD is order-sensitive
   　　 while comparison to a regular mapping is order-insensitive.

    　　‘‘‘
    　　if isinstance(other, OrderedDict):
        　　return dict.__eq__(self, other) and all(map(_eq, self, other))
    　　return dict.__eq__(self, other)
三、defaultdict（默认字典）
    class defaultdict(dict):
    　　"""
    　　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.
   　　 """
    class defaultdict(dict)可以看出，默认字典defaultdict也是继承字典中的方法，下面来看看默认字典中提供的方法：
    1.copy(self)
    def copy(self): # real signature unknown; restored from __doc__
    　　""" D.copy() -> a shallow copy of D. """
    　　pass
    copy(self)复制默认字典。
    2.__copy__(self,*args,**kwargs)
    def __copy__(self, *args, **kwargs): # real signature unknown
    　　""" D.copy() -> a shallow copy of D. """
    　　pass
    3.__getattrubute__(self,*args,**kwargs)
    def __getattribute__(self, *args, **kwargs): # real signature unknown
    　　""" Return getattr(self, name). """
    　　pass
    4.__missing__(self,key)
    def __missing__(self, key): # real signature unknown; restored from __doc__
    """
    　　__missing__(key) # Called by __getitem__ for missing key; pseudo-code:
     　　if self.default_factory is None: raise KeyError((key,))
      　　self[key] = value = self.default_factory()
     　　 return value
    　　"""
   　　 pass
    5.__reduce__(self,*args,**kwargs)
    def __reduce__(self, *args, **kwargs): # real signature unknown
    　　""" Return state information for pickling. """
    　　pass
    6.__repr__(self,*args,**kwargs)
    def __repr__(self, *args, **kwargs): # real signature unknown
    　　""" Return repr(self). """
    　　pass
    下面来看一个实例，假如我们要把一个列表[11,22,33,44,66,55,77,88,99,90]中大于60的元素放在键值为"k1",小于60的元素放在键值为"k2"的字
典中，我们怎样来实现，一种是正常实现，一种是使用默认字典来实现，实例如下：
    方法一：默认字典（defaultdict)

　　from collections import defaultdict
　　values = [11,22,33,44,55,77,66,88,99,90]
　　dic = defaultdict(list)
　　for value in values:
    　　if value >= 60:
        　　dic["k1"].append(value)
    　　else:
       　　 dic["k2"].append(value)

　　print(dic)
    运行结果如下：
    defaultdict(<class ‘list‘>, {‘k1‘: [77, 66, 88, 99, 90], ‘k2‘: [11, 22, 33, 44, 55]})
    方法二：
    values = [11,22,33,44,55,88,66,77,99,90]
　　dic = {}
　　for value in values:
    　　if value >= 60:
        　　if "k1" in dic.keys():
            　　dic["k1"].append(value)
       　　 else:
            　　dic["k1"] = [value,]
   　　 else:
       　　 if "k2" in dic.keys():
           　　 dic["k2"].append(value)
       　　 else:
           　　 dic["k2"] = [value,]

　　print(dic)
    这个方法是我们首先定义一个空的字典，然后判断这个字典的键值中是否包含键"k1",如果不包含，我们就自己定义一个键-值对，创建键"k1"和
值列表，这样我们就不用额外定义空的列表，只需要加个判断即可。
四、可命名元组（nameedtuple)
    根据namedtuple可以创建一个包含tuple所有功能以及其他功能的类型。

def task_done(self):
    ‘‘‘Indicate that a formerly enqueued task is complete.

    Used by Queue consumer threads.  For each get() used to fetch a task,
    a subsequent call to task_done() tells the queue that the processing
    on the task is complete.

    If a join() is currently blocking, it will resume when all items
    have been processed (meaning that a task_done() call was received
    for every item that had been put() into the queue).

    Raises a ValueError if called more times than there were items
    placed in the queue.
    ‘‘‘
    with self.all_tasks_done:
        unfinished = self.unfinished_tasks - 1
        if unfinished <= 0:
            if unfinished < 0:
                raise ValueError(‘task_done() called too many times‘)
            self.all_tasks_done.notify_all()
        self.unfinished_tasks = unfinished

    3.join(self)
    def join(self):
    　　‘‘‘Blocks until all items in the Queue have been gotten and processed.

   　　 The count of unfinished tasks goes up whenever an item is added to the
    　　queue. The count goes down whenever a consumer thread calls task_done()
    　　to indicate the item was retrieved and all work on it is complete.

    　　When the count of unfinished tasks drops to zero, join() unblocks.
    　　‘‘‘
    4.qsize(self)
    def qsize(self):
   　　 ‘‘‘Return the approximate size of the queue (not reliable!).‘‘‘
    　　with self.mutex:
       　　 return self._qsize()
    qsize(self)查看queue里面元素的个数，qsize.
    >>> q1 = Queue()
　　>>> q1.put("tom")
　　>>> q1.put(123)
　　>>> q1.qsize()
　　2
    5.empty(self)
    def empty(self):
    　　‘‘‘Return True if the queue is empty, False otherwise (not reliable!).

   　　 This method is likely to be removed at some point.  Use qsize() == 0
    　　as a direct substitute, but be aware that either approach risks a race
    　　condition where a queue can grow before the result of empty() or
    　　qsize() can be used.

    　　To create code that needs to wait for all queued tasks to be
    　　completed, the preferred technique is to use the join() method.
    　　‘‘‘
    　　with self.mutex:
        　　return not self._qsize()
    empty(self)是判断一个Queue是否是空的单向队列，如果是空就返回True，否则返回False.
    >>> q1.qsize()
　　2
　　>>> q1.empty()
　　False
　　>>> q2 = Queue()
　　>>> q2.empty()
　　True
    6.full(self)
    def full(self):
   　　 ‘‘‘Return True if the queue is full, False otherwise (not reliable!).

   　　 This method is likely to be removed at some point.  Use qsize() >= n
   　　 as a direct substitute, but be aware that either approach risks a race
   　　 condition where a queue can shrink before the result of full() or
   　　 qsize() can be used.
    　　‘‘‘
   　　 with self.mutex:
        　　return 0 < self.maxsize <= self._qsize()
    full(self)我们可以定义一个Queue的长度，规定有多少个元素，而full(self)是用来判断这个Queue是否满了。
    7.put(self,item,block=True,timeout=None)
    def put(self, item, block=True, timeout=None):
    　　‘‘‘Put an item into the queue.
    8.get(self,block=True,timeout=None)
    def get(self, block=True, timeout=None):
    　　‘‘‘Remove and return an item from the queue.
    get(self,block=True,timeout=None)是从Queue中拿取数据，而且只能按照顺序拿取，不需要参数，是按照存取的顺序，先进先出的原则。
    9.put_nowait(self,item)
    def put_nowait(self, item):
   　　‘‘‘Put an item into the queue without blocking.

   　　 Only enqueue the item if a free slot is immediately available.
    　　Otherwise raise the Full exception.
   　　 ‘‘‘
    　　return self.put(item, block=False)
    put_nowait(self,item)
   10.get_nowait(self)
    def get_nowait(self):
    　　‘‘‘Remove and return an item from the queue without blocking.

    　　Only get an item if one is immediately available. Otherwise
   　　 raise the Empty exception.
    　　‘‘‘
    　　return self.get(block=False)
    11.__init(self,maxsize)
    def _init(self, maxsize):
   　　 self.queue = deque()
    12._qsize(self)
　　def _qsize(self):
    　　return len(self.queue)

　　　　# Put a new item in the queue
    13._put(self,item)
　　def _put(self, item):
    　　self.queue.append(item)

　　　　# Get an item from the queue
    14._get(self)
　　def _get(self):
   　　 return self.queue.popleft()

collections模块

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原文地址：http://www.cnblogs.com/gengcx/p/6765167.html