-- collection是对内置数据类型的一种扩充，其主要扩充类型包括：
1.namedtuple(): 生成可以使用名字来访问元素内容的tuple子类，以增强可读性。
def namedtuple(typename, field_names, verbose=False, rename=False):
"""Returns a new subclass of tuple with named fields.
返回一个新的命名域元组子类，typename为类型名，field_names为数据变量域名
>>> Point = namedtuple('Point', ['x', 'y']) #定义一个命名元组，类型为Point，数据变量域为['x','y']
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # 定义一个Point,x=11,y=22，（instantiate with positional args or keywords）
>>> p[0] + p[1] # 也可以用下标，p[0]即x,p[1]即y，(indexable like a plain tuple)
33
>>> x, y = p # 也可以这样赋值，p即p[0]和p[1]或p.x和p.y，（unpack like a regular tuple）
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessable by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)
2.deque: 双端队列，可以快速的从另外一侧追加和推出对象。deque其实是 double-ended queue 的缩写，翻译过来就是双端队列，它最大的好处就是实现了从队列 头部快速增加和取出对象: .popleft(), .appendleft() 。
从_collection模块得到：
class deque(object): """ deque([iterable[, maxlen]]) --> deque object A list-like sequence optimized for data accesses near its endpoints. """ def append(self, *args, **kwargs): # real signature unknown """ Add an element to the right side of the deque.从队列右边增加一个元素
如：
d = deque() d.append('1') d.append('2') d.append('3')
len(d) d[0] d[-1]print(d)
print("d[0]=%s,d[1]=%s,d[2]=%s"%(d[0],d[1],d[2]))
结果
deque(['1', '2', '3'])
d[0]=1,d[1]=2,d[2]=3
"""
pass def appendleft(self, *args, **kwargs): # real signature unknown """ Add an element to the left side of the deque. 从队列左边增加一个元素""" pass def clear(self, *args, **kwargs): # real signature unknown """ Remove all elements from the deque. 清除所有队列数据""" pass def copy(self, *args, **kwargs): # real signature unknown """ Return a shallow copy of a deque. 返回一个浅拷贝队列""" 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 从右边扩充队列值""" pass def extendleft(self, *args, **kwargs): # real signature unknown """ Extend the left side of the deque with elements from the iterable """ pass def index(self, value, start=None, stop=None): # real signature unknown; restored from __doc__ """ D.index(value, [start, [stop]]) -> integer -- return first index of value.返回值得第一个下标 Raises ValueError if the value is not present. """ return 0 def insert(self, index, p_object): # real signature unknown; restored from __doc__ """ D.insert(index, object) -- insert object before index """ pass def pop(self, *args, **kwargs): # real signature unknown """ Remove and return the rightmost element. """ pass def popleft(self, *args, **kwargs): # real signature unknown """ Remove and return the leftmost element. """ pass def remove(self, value): # real signature unknown; restored from __doc__ """ D.remove(value) -- remove first occurrence of value. """ pass def reverse(self): # real signature unknown; restored from __doc__ """ D.reverse() -- reverse *IN PLACE* """ pass def rotate(self, *args, **kwargs): # real signature unknown """ Rotate the deque n steps to the right (default n=1). If n is negative, rotates left. """ pass
3.Counter: 计数器，主要用来计数,是对字典的一种扩充。
下面是Counter类，从dict继承而来。
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') # 从字串中计算元素个数
Counter({'a': 5, 'b': 4, 'c': 3, 'd': 2, 'e': 1}) >>> c.most_common(3) # 选出3个元素最多的值 [('a', 5), ('b', 4), ('c', 3)] >>> sorted(c) # 对每个独立的元素进行列表排序 ['a', 'b', 'c', 'd', 'e'] >>> ''.join(sorted(c.elements())) # 按排序列出重复元素 'aaaaabbbbcccdde' >>> sum(c.values()) # 求出字串元素的总个数 15 >>> c['a'] # 计算c字串中a元素的个数 5
#遍历'shazam'字串，为每个遍历到的元素数量加1，所以总的a元素数量为7
>>> 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'] # 删除所有的b元素 >>> c['b'] # now there are zero 'b' 0 >>> d = Counter('simsalabim') # 生成一个新的计数器 >>> c.update(d) # 将新的计数器d加到原来的计数器c中 >>> c['a'] # 此时计算的a元素为9个 9 >>> c.clear() # 清空计数器 >>> 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 # 将b元素减少2个 >>> c.most_common() # 此时b仍然存在，但计数数量为0 [('a', 3), ('c', 1), ('b', 0)] '''
4.OrderedDict: 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.
def clear(self):
'od.clear() -> None. Remove all items from od.'
root = self.__root
root.prev = root.next = root
self.__map.clear()
dict.clear(self)
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')
root = self.__root
if last:
link = root.prev
link_prev = link.prev
link_prev.next = root
root.prev = link_prev
else:
link = root.next
link_next = link.next
root.next = link_next
link_next.prev = root
key = link.key
del self.__map[key]
value = dict.pop(self, key)
return key, value
def move_to_end(self, key, last=True):
'''Move an existing element to the end (or beginning if last==False).
Raises KeyError if the element does not exist.
When last=True, acts like a fast version of self[key]=self.pop(key).
'''
link = self.__map[key]
link_prev = link.prev
link_next = link.next
link_prev.next = link_next
link_next.prev = link_prev
root = self.__root
if last:
last = root.prev
link.prev = last
link.next = root
last.next = root.prev = link
else:
first = root.next
link.prev = root
link.next = first
root.next = first.prev = link
def __sizeof__(self):
sizeof = _sys.getsizeof
n = len(self) + 1 # number of links including root
size = sizeof(self.__dict__) # instance dictionary
size += sizeof(self.__map) * 2 # internal dict and inherited dict
size += sizeof(self.__hardroot) * n # link objects
size += sizeof(self.__root) * n # proxy objects
return size
update = __update = MutableMapping.update
def keys(self):
"D.keys() -> a set-like object providing a view on D's keys"
return _OrderedDictKeysView(self)
def items(self):
"D.items() -> a set-like object providing a view on D's items"
return _OrderedDictItemsView(self)
def values(self):
"D.values() -> an object providing a view on D's values"
return _OrderedDictValuesView(self)
__ne__ = MutableMapping.__ne__
__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
@_recursive_repr()
def __repr__(self):
'od.__repr__() <==> repr(od)'
if not self:
return '%s()' % (self.__class__.__name__,)
return '%s(%r)' % (self.__class__.__name__, list(self.items()))
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())
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(map(_eq, self, other))
return dict.__eq__(self, other)
try:
from _collections import OrderedDict
except ImportError:
# Leave the pure Python version in place.
pass
5.defaultdict: 带有默认值的字典
我们都知道，在使用Python原生的数据结构dict的时候，如果用 d[key] 这样的方式访问， 当指定的key不存在时，是会抛出KeyError异常的。
但是，如果使用defaultdict，只要你传入一个默认的工厂方法，那么请求一个不存在的key时， 便会调用这个工厂方法使用其结果来作为这个key的默认值。
默认值可以很方便
众所周知，在Python中如果访问字典中不存在的键，会引发KeyError异常（JavaScript中如果对象中不存在某个属性，则返回undefined）。但是有时候，字典中的每个键都存在默认值是非常方便的。例如下面的例子：
strings = ('puppy', 'kitten', 'puppy', 'puppy',
'weasel', 'puppy', 'kitten', 'puppy')
counts = {}
for kw in strings:
counts[kw] += 1
该例子统计strings中某个单词出现的次数，并在counts字典中作记录。单词每出现一次，在counts相对应的键所存的值数字加1。但是事实上，运行这段代码会抛出KeyError异常，出现的时机是每个单词第一次统计的时候，因为Python的dict中不存在默认值的说法，可以在Python命令行中验证：
>>> counts = dict()
>>> counts
{}
>>> counts['puppy'] += 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'puppy'
使用判断语句检查
既然如此，首先可能想到的方法是在单词第一次统计的时候，在counts中相应的键存下默认值1。这需要在处理的时候添加一个判断语句：
strings = ('puppy', 'kitten', 'puppy', 'puppy',
'weasel', 'puppy', 'kitten', 'puppy')
counts = {}
for kw in strings:
if kw not in counts:
counts[kw] = 1
else:
counts[kw] += 1
# counts:
# {'puppy': 5, 'weasel': 1, 'kitten': 2}
使用dict.setdefault()方法
也可以通过dict.setdefault()方法来设置默认值：
strings = ('puppy', 'kitten', 'puppy', 'puppy',
'weasel', 'puppy', 'kitten', 'puppy')
counts = {}
for kw in strings:
counts.setdefault(kw, 0)
counts[kw] += 1 # 原PPT中这里有一个笔误
dict.setdefault()方法接收两个参数，第一个参数是健的名称，第二个参数是默认值。假如字典中不存在给定的键，则返回参数中提供的默认值；反之，则返回字典中保存的值。利用dict.setdefault()方法的返回值可以重写for循环中的代码，使其更加简洁：
strings = ('puppy', 'kitten', 'puppy', 'puppy',
'weasel', 'puppy', 'kitten', 'puppy')
counts = {}
for kw in strings:
counts[kw] = counts.setdefault(kw, 0) + 1
使用collections.defaultdict类
以上的方法虽然在一定程度上解决了dict中不存在默认值的问题，但是这时候我们会想，有没有一种字典它本身提供了默认值的功能呢？答案是肯定的，那就是collections.defaultdict。
defaultdict类就好像是一个dict，但是它是使用一个类型来初始化的：
>>> from collections import defaultdict
>>> dd = defaultdict(list)
>>> dd
defaultdict(<type 'list'>, {})
defaultdict类的初始化函数接受一个类型作为参数，当所访问的键不存在的时候，可以实例化一个值作为默认值：
>>> dd['foo']
[]
>>> dd
defaultdict(<type 'list'>, {'foo': []})
>>> dd['bar'].append('quux')
>>> dd
defaultdict(<type 'list'>, {'foo': [], 'bar': ['quux']})
需要注意的是，这种形式的默认值只有在通过dict[key]或者dict.__getitem__(key)访问的时候才有效，这其中的原因在下文会介绍。
>>> from collections import defaultdict
>>> dd = defaultdict(list)
>>> 'something' in dd
False
>>> dd.pop('something')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'pop(): dictionary is empty'
>>> dd.get('something')
>>> dd['something']
[]
defaultdict类除了接受类型名称作为初始化函数的参数之外，还可以使用任何不带参数的可调用函数，到时该函数的返回结果作为默认值，这样使得默认值的取值更加灵活。下面用一个例子来说明，如何用自定义的不带参数的函数zero()作为defaultdict类的初始化函数的参数：
>>> from collections import defaultdict
>>> def zero():
... return 0
...
>>> dd = defaultdict(zero)
>>> dd
defaultdict(<function zero at 0xb7ed2684>, {})
>>> dd['foo']
0
>>> dd
defaultdict(<function zero at 0xb7ed2684>, {'foo': 0})
利用collections.defaultdict来解决最初的单词统计问题，代码如下：
from collections import defaultdict
strings = ('puppy', 'kitten', 'puppy', 'puppy',
'weasel', 'puppy', 'kitten', 'puppy')
counts = defaultdict(lambda: 0) # 使用lambda来定义简单的函数
for s in strings:
counts[s] += 1
defaultdict类是如何实现的
通过上面的内容，想必大家已经了解了defaultdict类的用法，那么在defaultdict类中又是如何来实现默认值的功能呢？这其中的关键是使用了看__missing__()这个方法：
>>> from collections import defaultdict
>>> print defaultdict.__missing__.__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
通过查看__missing__()方法的docstring，可以看出当使用__getitem__()方法访问一个不存在的键时(dict[key]这种形式实际上是__getitem__()方法的简化形式)，会调用__missing__()方法获取默认值，并将该键添加到字典中去。
关于__missing__()方法的具体介绍可以参考Python官方文档中的"Mapping Types — dict"一节。
文档中介绍，从2.5版本开始，如果派生自dict的子类定义了__missing__()方法，当访问不存在的键时，dict[key]会调用__missing__()方法取得默认值。
从中可以看出，虽然dict支持__missing__()方法，但是在dict本身是不存在这个方法的，而是需要在派生的子类中自行实现这个方法。可以简单的验证这一点：
>>> print dict.__missing__.__doc__
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'dict' has no attribute '__missing__'
同时，我们可以进一步的做实验，定义一个子类Missing并实现__missing__()方法:
>>> class Missing(dict):
... def __missing__(self, key):
... return 'missing'
...
>>> d = Missing()
>>> d
{}
>>> d['foo']
'missing'
>>> d
{}
返回结果反映了__missing__()方法确实发挥了作用。在此基础上，我们稍许修改__missing__()方法,使得该子类同defautldict类一样为不存在的键设置一个默认值：
>>> class Defaulting(dict):
... def __missing__(self, key):
... self[key] = 'default'
... return 'default'
...
>>> d = Defaulting()
>>> d
{}
>>> d['foo']
'default'
>>> d
{'foo': 'default'}
在旧版本的Python中实现类defaultdict的功能
defaultdict类是从2.5版本之后才添加的，在一些旧版本中并不支持它，因此为旧版本实现一个兼容的defaultdict类是必要的。这其实很简单，虽然性能可能未必如2.5版本中自带的defautldict类好，但在功能上是一样的。
首先，__getitem__()方法需要在访问键失败时，调用__missing__()方法：
class defaultdict(dict):
def __getitem__(self, key):
try:
return dict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
其次，需要实现__missing__()方法用来设置默认值：
class defaultdict(dict):
def __getitem__(self, key):
try:
return dict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
self[key] = value = self.default_factory()
return value
然后，defaultdict类的初始化函数__init__()需要接受类型或者可调用函数参数:
class defaultdict(dict):
def __init__(self, default_factory=None, *a, **kw):
dict.__init__(self, *a, **kw)
self.default_factory = default_factory
def __getitem__(self, key):
try:
return dict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
self[key] = value = self.default_factory()
return value
最后，综合以上内容，通过以下方式完成兼容新旧Python版本的代码：
try:
from collections import defaultdict
except ImportError:
class defaultdict(dict):
def __init__(self, default_factory=None, *a, **kw):
dict.__init__(self, *a, **kw)
self.default_factory = default_factory
def __getitem__(self, key):
try:
return dict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
self[key] = value = self.default_factory()
return value
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, *args, **kwargs): # real signature unknown """ Return getattr(self, 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, *args, **kwargs): # real signature unknown """ Return repr(self). """ pass default_factory = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Factory for default value called by __missing__()."""