1、计数器（counter）
Counter是对字典类型的补充，用于追踪值的出现次数。
ps：具备字典的所有功能 + 自己的功能
如：
c = collections.Counter('abcdeabcdabcaba')
print c
输出：Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1})
参数里可以是列表或者远足也可以
counter原代码：
######################################################################## ### 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): """ 对于不存在的元素，返回计数器为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): """ 数量从大到写排列，获取前N个元素 """ '''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): """ 计数器中的所有元素，注：此处非所有元素集合，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): """ 更新计数器，其实就是增加；如果原来没有，则新建，如果有则加一 """ '''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): """ 相减，原来的计数器中的每一个元素的数量减去后添加的元素的数量 """ '''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): """ 拷贝 """ 'Return a shallow copy.' return self.__class__(self) def __reduce__(self): """ 返回一个元组（类型，元组） """ return self.__class__, (dict(self),) def __delitem__(self, elem): """ 删除元素 """ '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
2、有序字典(orderedDict )
orderdDict是对字典类型的补充，他记住了字典元素添加的顺序
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
3、默认字典(defaultdict)
defaultdict是对字典的类型的补充，他默认给字典的值设置了一个类型。
4、可命名元组(namedtuple)
根据nametuple可以创建一个包含tuple所有功能以及其他功能的类型。
mytuple = collections.namedtuple('mytuple',['x','y','z'])
new = mytuple(1,2,3)
print new
结果：mytuple(x=1, y=2, z=3)
可得出可命名元组，调用时: new.x , 结果: 1
一般运用在坐标上
5、双向队列(deque)
一个线程安全的双向队列
q = collections.deque()
q.append(1)
q.append(2)
print q
结果：deque([1,2])
q.pop() 结果：2 从右起把2取走
q.popleft() 从左起把1取走
class deque(object): """ deque([iterable[, maxlen]]) --> deque object Build an ordered collection with optimized access from 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 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 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 def __copy__(self, *args, **kwargs): # real signature unknown """ Return a shallow copy of a deque. """ pass def __delitem__(self, y): # real signature unknown; restored from __doc__ """ x.__delitem__(y) <==> del x[y] """ pass def __eq__(self, y): # real signature unknown; restored from __doc__ """ x.__eq__(y) <==> x==y """ pass def __getattribute__(self, name): # real signature unknown; restored from __doc__ """ x.__getattribute__('name') <==> x.name """ pass def __getitem__(self, y): # real signature unknown; restored from __doc__ """ x.__getitem__(y) <==> x[y] """ pass def __ge__(self, y): # real signature unknown; restored from __doc__ """ x.__ge__(y) <==> x>=y """ pass def __gt__(self, y): # real signature unknown; restored from __doc__ """ x.__gt__(y) <==> x>y """ pass def __iadd__(self, y): # real signature unknown; restored from __doc__ """ x.__iadd__(y) <==> x+=y """ pass def __init__(self, iterable=(), maxlen=None): # known case of _collections.deque.__init__ """ deque([iterable[, maxlen]]) --> deque object Build an ordered collection with optimized access from its endpoints. # (copied from class doc) """ pass def __iter__(self): # real signature unknown; restored from __doc__ """ x.__iter__() <==> iter(x) """ pass def __len__(self): # real signature unknown; restored from __doc__ """ x.__len__() <==> len(x) """ pass def __le__(self, y): # real signature unknown; restored from __doc__ """ x.__le__(y) <==> x<=y """ pass def __lt__(self, y): # real signature unknown; restored from __doc__ """ x.__lt__(y) <==> x<y """ pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __ne__(self, y): # real signature unknown; restored from __doc__ """ x.__ne__(y) <==> x!=y """ 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 def __reversed__(self): # real signature unknown; restored from __doc__ """ D.__reversed__() -- return a reverse iterator over the deque """ pass def __setitem__(self, i, y): # real signature unknown; restored from __doc__ """ x.__setitem__(i, y) <==> x[i]=y """ pass def __sizeof__(self): # real signature unknown; restored from __doc__ """ D.__sizeof__() -- size of D in memory, in bytes """ pass maxlen = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """maximum size of a deque or None if unbounded""" __hash__ = None deque
注：既然有双向队列，也有单项队列（先进先出 FIFO ） 使用的是Queue模块，源代码如下：
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
说到队列，再提下其和栈的区别：
队列：FIFO(先进先出)
栈：后进先出