【python3】collections系列介紹

文章來源：http://www.jb51.net/article/48771.htm

　　　　（http://www.cnblogs.com/wushank/p/5122786.html）

修改人：天馬流行拳

時間：2016/6/22

 

Collections模塊基本介紹

我們都知道，Python擁有一些內置的數據類型，比如str, int, list, tuple, dict等， collections模塊在這些內置數據類型的基礎上，提供了幾個額外的數據類型：

1.namedtuple(): 生成可以使用名字來訪問元素內容的tuple子類
2.deque: 雙端隊列，可以快速的從另外一側追加和推出對象
3.Counter: 計數器，主要用來計數
4.OrderedDict: 有序字典
5.defaultdict: 帶有默認值的字典

1、可命名元組(namedtuple)

# 作用：namedtuple主要用來產生可以使用名稱來訪問元素的數據對象，通常用來增強代碼的可讀性， 在訪問一些tuple類型的數據時尤其好用。

class Mytuple(__builtin__.tuple)
 |  Mytuple(x, y)
 |  
 |  Method resolution order:
 |      Mytuple
 |      __builtin__.tuple
 |      __builtin__.object
 |  
 |  Methods defined here:
 |  
 |  __getnewargs__(self)
 |      Return self as a plain tuple.  Used by copy and pickle.
 |  
 |  __getstate__(self)
 |      Exclude the OrderedDict from pickling
 |  
 |  __repr__(self)
 |      Return a nicely formatted representation string
 |  
 |  _asdict(self)
 |      Return a new OrderedDict which maps field names to their values
 |  
 |  _replace(_self, **kwds)
 |      Return a new Mytuple object replacing specified fields with new values
 |  
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |  
 |  _make(cls, iterable, new=<built-in method __new__ of type object>, len=<built-in function len>) from __builtin__.type
 |      Make a new Mytuple object from a sequence or iterable
 |  
 |  ----------------------------------------------------------------------
 |  Static methods defined here:
 |  
 |  __new__(_cls, x, y)
 |      Create new instance of Mytuple(x, y)
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      Return a new OrderedDict which maps field names to their values
 |  
 |  x
 |      Alias for field number 0
 |  
 |  y
 |      Alias for field number 1
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  _fields = ('x', 'y')
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from __builtin__.tuple:
 |  
 |  __add__(...)
 |      x.__add__(y) <==> x+y
 |  
 |  __contains__(...)
 |      x.__contains__(y) <==> y in x
 |  
 |  __eq__(...)
 |      x.__eq__(y) <==> x==y
 |  
 |  __ge__(...)
 |      x.__ge__(y) <==> x>=y
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  __getitem__(...)
 |      x.__getitem__(y) <==> x[y]
 |  
 |  __getslice__(...)
 |      x.__getslice__(i, j) <==> x[i:j]
 |      
 |      Use of negative indices is not supported.
 |  
 |  __gt__(...)
 |      x.__gt__(y) <==> x>y
 |  
 |  __hash__(...)
 |      x.__hash__() <==> hash(x)
 |  
 |  __iter__(...)
 |      x.__iter__() <==> iter(x)
 |  
 |  __le__(...)
 |      x.__le__(y) <==> x<=y
 |  
 |  __len__(...)
 |      x.__len__() <==> len(x)
 |  
 |  __lt__(...)
 |      x.__lt__(y) <==> x<y
 |  
 |  __mul__(...)
 |      x.__mul__(n) <==> x*n
 |  
 |  __ne__(...)
 |      x.__ne__(y) <==> x!=y
 |  
 |  __rmul__(...)
 |      x.__rmul__(n) <==> n*x
 |  
 |  __sizeof__(...)
 |      T.__sizeof__() -- size of T in memory, in bytes
 |  
 |  count(...)
 |      T.count(value) -> integer -- return number of occurrences of value
 |  
 |  index(...)
 |      T.index(value, [start, [stop]]) -> integer -- return first index of value.
 |      Raises ValueError if the value is not present.

Mytuple

Mytuple

namedtuple

創建一個自己的可擴展tuple的類（包含tuple所有功能以及其他功能的類型），在根據類創建對象，然后調用對象

最長用於坐標，普通的元組類似於列表以index編號來訪問，而自定義可擴展的可以類似於字典的keys進行訪問

下例列舉用collections.namedtuple以及普通元組進行元素調用的實例子。

實例1：

 

【collections方法】
import collections　　　　　　　　#導入collections模塊

mytuple = collections.namedtuple('mytuple',['x','y','z'])　　　#調用namedtuple方法來定義mytuple的變量，並創建一個名稱為['x','y','z']的列表。

a = mytuple(3,5,7)　　　　　　　　#給mytuple賦值，這里賦值的（3,5,7）是分別賦值給['x','y','z']這個列表中每個元素的。
print(a)
>>>mytuple(x=3, y=5, z=7)  　　 #打印結果可以看出賦值的每個值已經傳給了列表中對應的每個元素中了。

print(a.x)　　　　　　　　　　　　 #上述我們把mytuple賦給了變量a,所以a=mytuple。那么我們在調用mytuple中的元素時，要使用a.x,a.y,a.z的方式去調用。

>>>3

print(a.x*a.z)　　　　　　　　　　#a.x=3，a.z=7那么再相乘結果為21

>>>21

 

 

【普通tuple調用方法】

mytuple = (3,5,7)　　　　　　　　#生成一個普通的數字元組
print(mytuple)

>>>(3,5,7)

print(mytuple[0]*mytuple[2])   #在做元素調用以及算法計算時，因為元組調用元素跟列表一樣是通過index編號來訪問的所以要取出每個元素必須使用坐標，然后再做計算。

>>>21

 總結：通過上述方法可以看出使用collections模塊中的namedtuple方法可以給每個元素起別名，通過名稱調用的方式來獲取值使用。而普通元組的方法必須通過下標的方式來取值。

實例2：

from collections import namedtuple　　　　　　　　　　　　　　　　#通過from import的方式直接調用collections模塊中namedtuple這個方法。而import collections是導入這個模塊中所有的方法。這種調用在使用時必須collections.namedtuple的方式來使用。

websites = [
    ('Sohu', 'http://www.google.com/', u'liupeng'),
    ('Sina', 'http://www.sina.com.cn/', u'tony'),
    ('163', 'http://www.163.com/', u'jack')
]　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　#假設我們有一個列表，列表中有三個元組，每個元組中的元素都是不同格式的字符串

Website = namedtuple('Website_list', ['name', 'url', 'founder'])  #通過調用namedtuple，來設置一個列表'Website_list'是這個列表的別名.而['name','url','founder']的命名是分別為了分配給大列表websites中哥哥元組中的各個元素的。
for i in websites:　　　　　　　　　　　　　　　　　　　　　　　　　# for循環websites這個大列表，這里的i循環得出的結果是這個大列表中每個元組
    x = Website._make(i)　　　　　　　　　　　　　　　　　　　　　 #從已經存在迭代對象或者序列生成一個新的命名元組。 Website是namedtuple('Website_list', ['name', 'url', 'founder'])的內容，._make(i)是websites各個元組的內容，把這兩個元組重組成新的元組。
    print (x)　　　　　　　　　　　　　　　　　　　　　　　　　　　　#x打印結果如下，生成了新的命名元組。是使用了namedtuple中._make的方法生成的。

# Result:
Website_list(name='Sohu', url='http://www.google.com/', founder='liupeng')
Website_list(name='Sina', url='http://www.sina.com.cn/', founder='tony')
Website_list(name='163', url='http://www.163.com/', founder='jack')

 

 2、隊列(deque)

作用：deque其實是 double-ended queue 的縮寫，翻譯過來就是雙端隊列，它最大的好處就是實現了從隊列 頭部快速增加和取出對象: .popleft(), .appendleft() 。

 

class Queue:
    """Create a queue object with a given maximum size.

    If maxsize is <= 0, the queue size is infinite.
    """
    def __init__(self, maxsize=0):
        self.maxsize = maxsize
        self._init(maxsize)
        # mutex must be held whenever the queue is mutating.  All methods
        # that acquire mutex must release it before returning.  mutex
        # is shared between the three conditions, so acquiring and
        # releasing the conditions also acquires and releases mutex.
        self.mutex = _threading.Lock()
        # Notify not_empty whenever an item is added to the queue; a
        # thread waiting to get is notified then.
        self.not_empty = _threading.Condition(self.mutex)
        # Notify not_full whenever an item is removed from the queue;
        # a thread waiting to put is notified then.
        self.not_full = _threading.Condition(self.mutex)
        # Notify all_tasks_done whenever the number of unfinished tasks
        # drops to zero; thread waiting to join() is notified to resume
        self.all_tasks_done = _threading.Condition(self.mutex)
        self.unfinished_tasks = 0

    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.
        """
        self.all_tasks_done.acquire()
        try:
            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
        finally:
            self.all_tasks_done.release()

    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.
        """
        self.all_tasks_done.acquire()
        try:
            while self.unfinished_tasks:
                self.all_tasks_done.wait()
        finally:
            self.all_tasks_done.release()

    def qsize(self):
        """Return the approximate size of the queue (not reliable!)."""
        self.mutex.acquire()
        n = self._qsize()
        self.mutex.release()
        return n

    def empty(self):
        """Return True if the queue is empty, False otherwise (not reliable!)."""
        self.mutex.acquire()
        n = not self._qsize()
        self.mutex.release()
        return n

    def full(self):
        """Return True if the queue is full, False otherwise (not reliable!)."""
        self.mutex.acquire()
        n = 0 < self.maxsize == self._qsize()
        self.mutex.release()
        return n

    def put(self, item, block=True, timeout=None):
        """Put an item into the queue.

        If optional args 'block' is true and 'timeout' is None (the default),
        block if necessary until a free slot is available. If 'timeout' is
        a non-negative number, it blocks at most 'timeout' seconds and raises
        the Full exception if no free slot was available within that time.
        Otherwise ('block' is false), put an item on the queue if a free slot
        is immediately available, else raise the Full exception ('timeout'
        is ignored in that case).
        """
        self.not_full.acquire()
        try:
            if self.maxsize > 0:
                if not block:
                    if self._qsize() == self.maxsize:
                        raise Full
                elif timeout is None:
                    while self._qsize() == self.maxsize:
                        self.not_full.wait()
                elif timeout < 0:
                    raise ValueError("'timeout' must be a non-negative number")
                else:
                    endtime = _time() + timeout
                    while self._qsize() == self.maxsize:
                        remaining = endtime - _time()
                        if remaining <= 0.0:
                            raise Full
                        self.not_full.wait(remaining)
            self._put(item)
            self.unfinished_tasks += 1
            self.not_empty.notify()
        finally:
            self.not_full.release()

    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, False)

    def get(self, block=True, timeout=None):
        """Remove and return an item from the queue.

        If optional args 'block' is true and 'timeout' is None (the default),
        block if necessary until an item is available. If 'timeout' is
        a non-negative number, it blocks at most 'timeout' seconds and raises
        the Empty exception if no item was available within that time.
        Otherwise ('block' is false), return an item if one is immediately
        available, else raise the Empty exception ('timeout' is ignored
        in that case).
        """
        self.not_empty.acquire()
        try:
            if not block:
                if not self._qsize():
                    raise Empty
            elif timeout is None:
                while not self._qsize():
                    self.not_empty.wait()
            elif timeout < 0:
                raise ValueError("'timeout' must be a non-negative number")
            else:
                endtime = _time() + timeout
                while not self._qsize():
                    remaining = endtime - _time()
                    if remaining <= 0.0:
                        raise Empty
                    self.not_empty.wait(remaining)
            item = self._get()
            self.not_full.notify()
            return item
        finally:
            self.not_empty.release()

    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(False)

    # Override these methods to implement other queue organizations
    # (e.g. stack or priority queue).
    # These will only be called with appropriate locks held

    # Initialize the queue representation
    def _init(self, maxsize):
        self.queue = deque()

    def _qsize(self, len=len):
        return len(self.queue)

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

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

Queue.Queue

queue

 

a = collections.deque(range(9))　　　　 　　#通過調用collections中deque方法來創建一個數字列表。[0,1,2,3,4,5,6,7,8]
a.appendleft(4)　　　　　　　　　　　　　　　　#.appendleft（傳參）  是把傳的參數添加到列表的最左邊。appendleft一次只支持傳一個參數。
a.extend([1,2,3,4,5])　　　　　　　　　　　　#.extend（）以及append（）方法是把傳的參數添加到列表的最后邊。而.extend（【列表，或者元組】）可以把列表中的各個元素傳到列表中生成一個新的列表。
print(a.count(3))　　　　　　　　　　　　　　 #a.count()括號中的參數可以指定。count是查看出現的次數的。按照上例除了生成原列表中生成的數字3以外，我們在extend列表的時候又有一個3，所以count出來的結果應該是2.說明3出現了2次。

print(a)

import collections
from collections import deque

data = collections.deque()

data.extendleft([('Sohu', 'http://www.google.com/', u'liupeng'),
    ('Sina', 'http://www.sina.com.cn/', u'tony'),
    ('163', 'http://www.163.com/', u'jack')])


li = []

for i in data:
    li.append(i)
print(li)

nam = collections.namedtuple('website',['name','url','founder'])

for i in li:
    x = nam._make(i)
    print(x)

 3、counter計數器

 計數器是一個非常常用的功能需求，collections也貼心的為你提供了這個功能。如果counter(dict)是對字典的一個補充，如果counter(list)則是對列表的補充，初步測試對字典的值進行排序。

########################################################################
###  Counter
########################################################################

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

    >>> c['a']                          # count of letter 'a'
    >>> 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'
    >>> del c['b']                      # remove all 'b'
    >>> c['b']                          # now there are zero 'b'

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

    >>> 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

    def __init__(self, iterable=None, **kwds):
        '''Create a new, empty Counter object.  And if given, count elements
        from an input iterable.  Or, initialize the count from another mapping
        of elements to their counts.

        >>> c = Counter()                           # a new, empty counter
        >>> c = Counter('gallahad')                 # a new counter from an iterable
        >>> c = Counter({'a': 4, 'b': 2})           # a new counter from a mapping
        >>> c = Counter(a=4, b=2)                   # a new counter from keyword args

        '''
        super(Counter, self).__init__()
        self.update(iterable, **kwds)

    def __missing__(self, key):
        """ 對於不存在的元素，返回計數器為0 """
        'The count of elements not in the Counter is zero.'
        # Needed so that self[missing_item] does not raise KeyError
        return 0

    def most_common(self, n=None):
        """ 數量大於等n的所有元素和計數器 """
        '''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.iteritems(), key=_itemgetter(1), reverse=True)
        return _heapq.nlargest(n, self.iteritems(), key=_itemgetter(1))

    def elements(self):
        """ 計數器中的所有元素，注：此處非所有元素集合，而是包含所有元素集合的迭代器 """
        '''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

        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.iteritems()))

    # Override dict methods where necessary

    @classmethod
    def fromkeys(cls, iterable, v=None):
        # There is no equivalent method for counters because setting v=1
        # means that no element can have a count greater than one.
        raise NotImplementedError(
            'Counter.fromkeys() is undefined.  Use Counter(iterable) instead.')

    def update(self, iterable=None, **kwds):
        """ 更新計數器，其實就是增加；如果原來沒有，則新建，如果有則加一 """
        '''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

        '''
        # 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.

        if iterable is not None:
            if isinstance(iterable, Mapping):
                if self:
                    self_get = self.get
                    for elem, count in iterable.iteritems():
                        self[elem] = self_get(elem, 0) + count
                else:
                    super(Counter, self).update(iterable) # fast path when counter is empty
            else:
                self_get = self.get
                for elem in iterable:
                    self[elem] = self_get(elem, 0) + 1
        if kwds:
            self.update(kwds)

    def subtract(self, iterable=None, **kwds):
        """ 相減，原來的計數器中的每一個元素的數量減去后添加的元素的數量 """
        '''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
        >>> c['w']                          # 1 in which, minus 1 in witch, minus 1 in watch
        -1

        '''
        if iterable is not None:
            self_get = self.get
            if isinstance(iterable, Mapping):
                for elem, count in iterable.items():
                    self[elem] = self_get(elem, 0) - count
            else:
                for elem in iterable:
                    self[elem] = self_get(elem, 0) - 1
        if kwds:
            self.subtract(kwds)

    def copy(self):
        """ 拷貝 """
        'Return a shallow copy.'
        return self.__class__(self)

    def __reduce__(self):
        """ 返回一個元組（類型，元組） """
        return self.__class__, (dict(self),)

    def __delitem__(self, elem):
        """ 刪除元素 """
        'Like dict.__delitem__() but does not raise KeyError for missing values.'
        if elem in self:
            super(Counter, self).__delitem__(elem)

    def __repr__(self):
        if not self:
            return '%s()' % self.__class__.__name__
        items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
        return '%s({%s})' % (self.__class__.__name__, items)

    # Multiset-style mathematical operations discussed in:
    #       Knuth TAOCP Volume II section 4.6.3 exercise 19
    #       and at http://en.wikipedia.org/wiki/Multiset
    #
    # Outputs guaranteed to only include positive counts.
    #
    # To strip negative and zero counts, add-in an empty counter:
    #       c += Counter()

    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

    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

    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

    def __and__(self, other):
        ''' Intersection is the minimum of corresponding counts.

        >>> 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

Counter

Counter

Counter

實例1：

a = collections.Counter('ababc')　　　　　　　　　　　　　　#通過Counter創建a跟b兩個元組，元組中的元素是以字典的方式顯示的。通過字典把每個元素重復的次數做統計分別作為字典的keys跟values.例如：Counter({'b': 2, 'a': 2, 'c': 1})　
b = collections.Counter('1234abd')

print(a.most_common(3))　　　　　　　　　　　　　　　　　　# 顯示n個個數。變量.most_common()中填寫的位數代表從大到小取前幾個數值的意思。例如是3的話，只會去3位數值[('b', 2), ('a', 2), ('c', 1)]
print(a)　　　　　　　　　　　　　　　　　　　　　　　　　　　#結果為[('b', 2), ('a', 2), ('c', 1)]
a.update(b)　　　　　　　　　　　　　　　　　　　　　　　　　# 把b中的值傳到a中。組合一個新的元組。（疊加）
print(a)　　　　　　　　　　　　　　　　　　　　　　　　　　　#結果為Counter({'a': 3, 'b': 3, '1': 1, '4': 1, 'c': 1, 'd': 1, '3': 1, '2': 1})，因為字典是無序的所以不是按照順序排列的。但是可以看出b中的元素已經傳到了a中。
a.subtract(b)　　　　　　　　　　　　　　　　　　　　　　　　#於.update（）相反。.subtract（）是表示相減。但是雖然相減了，仍然會把相減后不存在的key中的value以0的方式顯示。
print(a)　　　　　　　　　　　　　　　　　　　　　　　　　　 #結果為Counter({'a': 2, 'b': 2, 'c': 1, '1': 0, '4': 0, 'd': 0, '3': 0, '2': 0})

 

# Result：
[('b', 2), ('a', 2), ('c', 1)]
Counter({'b': 2, 'a': 2, 'c': 1})
Counter({'a': 3, 'b': 3, '1': 1, '4': 1, 'c': 1, 'd': 1, '3': 1, '2': 1})
Counter({'a': 2, 'b': 2, 'c': 1, '1': 0, '4': 0, 'd': 0, '3': 0, '2': 0})

實例2：

# Result：
 """
下面這個例子就是使用Counter模塊統計一段句子里面所有字符出現次數
"""
from collections import Counter
s = '''A Counter is a dict subclass for counting hashable objects. It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. The Counter class is similar to bags or multisets in other languages.'''.lower()   
#.lower()這里指把字符串中所有的內容以小寫字母呈現。（大寫轉小寫）

print(s)
c = Counter(s)
print (c.most_common(5))　　　　　　　　　　　　　　# 獲取出現頻率最高的5個字符


# Result：
a counter is a dict subclass for counting hashable objects. it is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. counts are allowed to be any integer value including zero or negative counts. the counter class is similar to bags or multisets in other languages.
[(' ', 54), ('e', 32), ('s', 25), ('a', 24), ('t', 24)]

 4、有序字典(orderedDict )

在Python中，dict這個數據結構由於hash的特性，是無序的，這在有的時候會給我們帶來一些麻煩， 幸運的是，collections模塊為我們提供了OrderedDict，當你要獲得一個有序的字典對象時，用它就對了。

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).
    # Each link is stored as a list of length three:  [PREV, NEXT, KEY].

    def __init__(self, *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 len(args) > 1:
            raise TypeError('expected at most 1 arguments, got %d' % len(args))
        try:
            self.__root
        except AttributeError:
            self.__root = root = []                     # sentinel node
            root[:] = [root, root, None]
            self.__map = {}
        self.__update(*args, **kwds)

    def __setitem__(self, key, value, dict_setitem=dict.__setitem__):
        '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:
            root = self.__root
            last = root[0]
            last[1] = root[0] = self.__map[key] = [last, root, key]
        return dict_setitem(self, key, value)

    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_prev, link_next, _ = self.__map.pop(key)
        link_prev[1] = link_next                        # update link_prev[NEXT]
        link_next[0] = link_prev                        # update link_next[PREV]

    def __iter__(self):
        'od.__iter__() <==> iter(od)'
        # Traverse the linked list in order.
        root = self.__root
        curr = root[1]                                  # start at the first node
        while curr is not root:
            yield curr[2]                               # yield the curr[KEY]
            curr = curr[1]                              # move to next node

    def __reversed__(self):
        'od.__reversed__() <==> reversed(od)'
        # Traverse the linked list in reverse order.
        root = self.__root
        curr = root[0]                                  # start at the last node
        while curr is not root:
            yield curr[2]                               # yield the curr[KEY]
            curr = curr[0]                              # move to previous node

    def clear(self):
        'od.clear() -> None.  Remove all items from od.'
        root = self.__root
        root[:] = [root, root, None]
        self.__map.clear()
        dict.clear(self)

    # -- the following methods do not depend on the internal structure --

    def keys(self):
        'od.keys() -> list of keys in od'
        return list(self)

    def values(self):
        'od.values() -> list of values in od'
        return [self[key] for key in self]

    def items(self):
        'od.items() -> list of (key, value) pairs in od'
        return [(key, self[key]) for key in self]

    def iterkeys(self):
        'od.iterkeys() -> an iterator over the keys in od'
        return iter(self)

    def itervalues(self):
        'od.itervalues -> an iterator over the values in od'
        for k in self:
            yield self[k]

    def iteritems(self):
        'od.iteritems -> an iterator over the (key, value) pairs in od'
        for k in self:
            yield (k, self[k])

    update = MutableMapping.update

    __update = update # let subclasses override update without breaking __init__

    __marker = object()

    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

    def setdefault(self, key, default=None):
        'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od'
        if key in self:
            return self[key]
        self[key] = default
        return default

    def popitem(self, last=True):
        '''od.popitem() -> (k, v), return and remove a (key, value) pair.
        Pairs are returned in LIFO order if last is true or FIFO order if false.

        '''
        if not self:
            raise KeyError('dictionary is empty')
        key = next(reversed(self) if last else iter(self))
        value = self.pop(key)
        return key, value

    def __repr__(self, _repr_running={}):
        'od.__repr__() <==> repr(od)'
        call_key = id(self), _get_ident()
        if call_key in _repr_running:
            return '...'
        _repr_running[call_key] = 1
        try:
            if not self:
                return '%s()' % (self.__class__.__name__,)
            return '%s(%r)' % (self.__class__.__name__, self.items())
        finally:
            del _repr_running[call_key]

    def __reduce__(self):
        'Return state information for pickling'
        items = [[k, self[k]] for k in self]
        inst_dict = vars(self).copy()
        for k in vars(OrderedDict()):
            inst_dict.pop(k, None)
        if inst_dict:
            return (self.__class__, (items,), inst_dict)
        return self.__class__, (items,)

    def copy(self):
        'od.copy() -> a shallow copy of od'
        return self.__class__(self)

    @classmethod
    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

    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(_imap(_eq, self, other))
        return dict.__eq__(self, other)

    def __ne__(self, other):
        'od.__ne__(y) <==> od!=y'
        return not self == other

    # -- the following methods support python 3.x style dictionary views --

    def viewkeys(self):
        "od.viewkeys() -> a set-like object providing a view on od's keys"
        return KeysView(self)

    def viewvalues(self):
        "od.viewvalues() -> an object providing a view on od's values"
        return ValuesView(self)

    def viewitems(self):
        "od.viewitems() -> a set-like object providing a view on od's items"
        return ItemsView(self)

OrderedDict

ordereddict

案例:

import collections

dic = collections.OrderedDict()  #創建有序字典下列分別是創建有序字典中的keys跟values

dic['name'] = 'liupeng'

dic['Job'] = 'IT'

dic['City'] = 'YanTai'

print(dic)

 

# Result：
OrderedDict([('name', 'liupeng'), ('Job', 'IT'), ('City', 'YanTai')])　　　　#打印有序字典結果

dic['school'] = 'DaLian'　　　　#往有序字典中添加新的Key跟value

print(dic)

# Result：
OrderedDict([('name', 'liupeng'), ('Job', 'IT'), ('City', 'YanTai'), ('school', 'DaLian')])　　　　#從打印有序字典結果中可以看出添加的key跟value已經追加到有序字典中去了

#這里就不列舉無序字典例子了。有序字典最大的好處就是它有序。。。接下來你懂得。（- * -））


dic1 = {'name1':'liupeng1','job1':'IT1','city1':'yantai1'}

print(dic1)

 5、默認字典(defaultdict)

 即為字典中的values設置一個默認類型：

 defaultdict的參數默認是dict，也可以為list,tuple

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.
    """
    def copy(self): # real signature unknown; restored from __doc__
        """ D.copy() -> a shallow copy of D. """
        pass

    def __copy__(self, *args, **kwargs): # real signature unknown
        """ D.copy() -> a shallow copy of D. """
        pass

    def __getattribute__(self, name): # real signature unknown; restored from __doc__
        """ x.__getattribute__('name') <==> x.name """
        pass

    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

    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

    def __reduce__(self, *args, **kwargs): # real signature unknown
        """ Return state information for pickling. """
        pass

    def __repr__(self): # real signature unknown; restored from __doc__
        """ x.__repr__() <==> repr(x) """
        pass

    default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
    """Factory for default value called by __missing__()."""

defaultdict

defaultdict

實例說明1：

在使用的dict時，無法指定values的類型，在賦值時要進行判斷，具體如下：

values = [11,22,33,44,55,66,77,88,99,90]

mydic = {}

for v in values:
    if v > 66:
        if 'k1' in mydic:　　　　　　　　#python2.7中有個.has_key的方法。在3.0以后版本中被廢除，用in來替代。python2.7用法：if my_dict.has_key('k1')
            mydic['k1'].append(v)

        else:
            mydic['k1']=[v]

    else:
        if 'k2' in mydic:
            mydic['k2'].append(v)

        else:
            mydic['k2']= [v]
            
print(mydic)

# Result:
{'k2': [11, 22, 33, 44, 55, 66], 'k1': [77, 88, 99, 90]}

而在使用了defaultdict時，代碼進行了簡化：

from collections import defaultdict

values = [11,22,33,44,55,66,77,88,99,90]

my_dict = defaultdict(list)

for v in values:　　　　　　　　　　#v始終都是my_dict中的values,而defaultdict(list)后我們對於keys的指定對比上例就方便很多。不用再做一層if判斷了。
    if v >66:
        my_dict['k1'].append(v)

    else:
        my_dict['k2'].append(v)

print(my_dict)

# Result:
defaultdict(<class 'list'>, {'k1': [77, 88, 99, 90], 'k2': [11, 22, 33, 44, 55, 66]})

 實例說明2：

from collections import defaultdict

members = [
    ['male', 'John'],
    ['male', 'Jack'],
    ['female', 'Lily'],
    ['male', 'Pony'],
    ['female', 'Lucy'],
]
result = defaultdict(list)
for sex, name in members:　　　　　　　　#這里設置2個變量作為字典(result)中的key跟value.
    result[sex].append(name)　　　　　　#這里把[sex]作為了字典中的key，name這個變量作為了value並append到字典result對應的key中。
print (result)

# Result:
defaultdict(<class 'list'>, {'female': ['Lily', 'Lucy'], 'male': ['John', 'Jack', 'Pony']})

以上代碼均在python3.4版本中測試過。

上面只是非常簡單的介紹了一下collections模塊的主要內容，主要目的就是當你碰到適合使用 它們的場所時，能夠記起並使用它們，起到事半功倍的效果。
如果要對它們有一個更全面和深入了解的話，還是建議閱讀官方文檔和模塊源碼。

https://docs.python.org/2/library/collections.html#module-collections

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