8.3. collections — 容器数据类型 ¶

8.3.1. ChainMap 对象 ¶

A ChainMap class is provided for quickly linking a number of mappings so they can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple update() 调用。

The class can be used to simulate nested scopes and is useful in templating.

class collections. ChainMap ( *maps ) ¶

A ChainMap groups multiple dicts or other mappings together to create a single, updateable view. If no maps are specified, a single empty dictionary is provided so that a new chain always has at least one mapping.

The underlying mappings are stored in a list. That list is public and can be accessed or updated using the maps attribute. There is no other state.

Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping.

A ChainMap incorporates the underlying mappings by reference. So, if one of the underlying mappings gets updated, those changes will be reflected in ChainMap .

All of the usual dictionary methods are supported. In addition, there is a maps attribute, a method for creating new subcontexts, and a property for accessing all but the first mapping:

maps ¶

A user updateable list of mappings. The list is ordered from first-searched to last-searched. It is the only stored state and can be modified to change which mappings are searched. The list should always contain at least one mapping.

new_child ( m=None ) ¶

返回新 ChainMap containing a new map followed by all of the maps in the current instance. If m is specified, it becomes the new map at the front of the list of mappings; if not specified, an empty dict is used, so that a call to d.new_child() 相当于： ChainMap({}, *d.maps) . This method is used for creating subcontexts that can be updated without altering values in any of the parent mappings.

3.4 版改变： 可选 m 参数被添加。

parents ¶

Property returning a new ChainMap containing all of the maps in the current instance except the first one. This is useful for skipping the first map in the search. Use cases are similar to those for the nonlocal keyword used in nested scopes . The use cases also parallel those for the built-in super() function. A reference to d.parents 相当于： ChainMap(*d.maps[1:]) .

import builtins
pylookup = ChainMap(locals(), globals(), vars(builtins))
									

import os, argparse
defaults = {'color': 'red', 'user': 'guest'}
parser = argparse.ArgumentParser()
parser.add_argument('-u', '--user')
parser.add_argument('-c', '--color')
namespace = parser.parse_args()
command_line_args = {k:v for k, v in vars(namespace).items() if v}
combined = ChainMap(command_line_args, os.environ, defaults)
print(combined['color'])
print(combined['user'])
									

c = ChainMap()        # Create root context
d = c.new_child()     # Create nested child context
e = c.new_child()     # Child of c, independent from d
e.maps[0]             # Current context dictionary -- like Python's locals()
e.maps[-1]            # Root context -- like Python's globals()
e.parents             # Enclosing context chain -- like Python's nonlocals
d['x']                # Get first key in the chain of contexts
d['x'] = 1            # Set value in current context
del d['x']            # Delete from current context
list(d)               # All nested values
k in d                # Check all nested values
len(d)                # Number of nested values
d.items()             # All nested items
dict(d)               # Flatten into a regular dictionary
									

class DeepChainMap(ChainMap):
    'Variant of ChainMap that allows direct updates to inner scopes'
    def __setitem__(self, key, value):
        for mapping in self.maps:
            if key in mapping:
                mapping[key] = value
                return
        self.maps[0][key] = value
    def __delitem__(self, key):
        for mapping in self.maps:
            if key in mapping:
                del mapping[key]
                return
        raise KeyError(key)
>>> d = DeepChainMap({'zebra': 'black'}, {'elephant': 'blue'}, {'lion': 'yellow'})
>>> d['lion'] = 'orange'         # update an existing key two levels down
>>> d['snake'] = 'red'           # new keys get added to the topmost dict
>>> del d['elephant']            # remove an existing key one level down
>>> d                            # display result
DeepChainMap({'zebra': 'black', 'snake': 'red'}, {}, {'lion': 'orange'})
									

8.3.2. Counter 对象 ¶

A counter tool is provided to support convenient and rapid tallies. For example:

>>> # Tally occurrences of words in a list
>>> cnt = Counter()
>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
...     cnt[word] += 1
>>> cnt
Counter({'blue': 3, 'red': 2, 'green': 1})
>>> # Find the ten most common words in Hamlet
>>> import re
>>> words = re.findall(r'\w+', open('hamlet.txt').read().lower())
>>> Counter(words).most_common(10)
[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),
 ('you', 554),  ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]
								

class collections. Counter ( [ iterable-or-mapping ] ) ¶

A Counter 是 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.

Elements are counted from an iterable or initialized from another 映射 (or counter):

>>> c = Counter()                           # a new, empty counter
>>> c = Counter('gallahad')                 # a new counter from an iterable
>>> c = Counter({'red': 4, 'blue': 2})      # a new counter from a mapping
>>> c = Counter(cats=4, dogs=8)             # a new counter from keyword args
										

Counter objects have a dictionary interface except that they return a zero count for missing items instead of raising a KeyError :

>>> c = Counter(['eggs', 'ham'])
>>> c['bacon']                              # count of a missing element is zero
0
										

Setting a count to zero does not remove an element from a counter. Use del to remove it entirely:

>>> c['sausage'] = 0                        # counter entry with a zero count
>>> del c['sausage']                        # del actually removes the entry
										

3.1 版新增。

Counter objects support three methods beyond those available for all dictionaries:

elements ( ) ¶

Return an iterator over elements repeating each as many times as its count. Elements are returned in arbitrary order. If an element’s count is less than one, elements() will ignore it.

>>> c = Counter(a=4, b=2, c=0, d=-2)
>>> sorted(c.elements())
['a', 'a', 'a', 'a', 'b', 'b']
												

most_common ( [ n ] ) ¶

Return a list of the n most common elements and their counts from the most common to the least. If n 被省略或 None , most_common() 返回 all elements in the counter. Elements with equal counts are ordered arbitrarily:

>>> Counter('abracadabra').most_common(3)  # doctest: +SKIP
[('a', 5), ('r', 2), ('b', 2)]
												

subtract ( [ iterable-or-mapping ] ) ¶

Elements are subtracted from an iterable or from another 映射 (or counter). Like dict.update() but subtracts counts instead of replacing them. Both inputs and outputs may be zero or negative.

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

3.2 版新增。

The usual dictionary methods are available for Counter objects except for two which work differently for counters.

fromkeys ( iterable ) ¶

This class method is not implemented for Counter 对象。

update ( [ iterable-or-mapping ] ) ¶

Elements are counted from an iterable or added-in from another 映射 (or counter). Like dict.update() but adds counts instead of replacing them. Also, the iterable is expected to be a sequence of elements, not a sequence of (key, value) pairs.

Common patterns for working with Counter 对象：

sum(c.values())                 # total of all counts
c.clear()                       # reset all counts
list(c)                         # list unique elements
set(c)                          # convert to a set
dict(c)                         # convert to a regular dictionary
c.items()                       # convert to a list of (elem, cnt) pairs
Counter(dict(list_of_pairs))    # convert from a list of (elem, cnt) pairs
c.most_common()[:-n-1:-1]       # n least common elements
+c                              # remove zero and negative counts
								

Several mathematical operations are provided for combining Counter objects to produce multisets (counters that have counts greater than zero). Addition and subtraction combine counters by adding or subtracting the counts of corresponding elements. Intersection and union return the minimum and maximum of corresponding counts. Each operation can accept inputs with signed counts, but the output will exclude results with counts of zero or less.

>>> c = Counter(a=3, b=1)
>>> d = Counter(a=1, b=2)
>>> c + d                       # add two counters together:  c[x] + d[x]
Counter({'a': 4, 'b': 3})
>>> c - d                       # subtract (keeping only positive counts)
Counter({'a': 2})
>>> c & d                       # intersection:  min(c[x], d[x]) # doctest: +SKIP
Counter({'a': 1, 'b': 1})
>>> c | d                       # union:  max(c[x], d[x])
Counter({'a': 3, 'b': 2})
								

Unary addition and subtraction are shortcuts for adding an empty counter or subtracting from an empty counter.

>>> c = Counter(a=2, b=-4)
>>> +c
Counter({'a': 2})
>>> -c
Counter({'b': 4})
								

8.3.3. deque 对象 ¶

class collections. deque ( [ iterable [ , maxlen ] ] ) ¶

Returns a new deque object initialized left-to-right (using append() ) with data from iterable 。若 iterable is not specified, the new deque is empty.

Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.

Though list objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0, v) operations which change both the size and position of the underlying data representation.

若 maxlen is not specified or is None , deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to the tail filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest.

Deque objects support the following methods:

append ( x ) ¶

添加 x to the right side of the deque.

appendleft ( x ) ¶

添加 x to the left side of the deque.

clear ( ) ¶

Remove all elements from the deque leaving it with length 0.

copy ( ) ¶

Create a shallow copy of the deque.

3.5 版新增。

count ( x ) ¶

Count the number of deque elements equal to x .

3.2 版新增。

extend ( iterable ) ¶

Extend the right side of the deque by appending elements from the iterable argument.

extendleft ( iterable ) ¶

Extend the left side of the deque by appending elements from iterable . Note, the series of left appends results in reversing the order of elements in the iterable argument.

index ( x [ , start [ , stop ] ] ) ¶

Return the position of x in the deque (at or after index start and before index stop ). Returns the first match or raises ValueError if not found.

3.5 版新增。

insert ( i , x ) ¶

Insert x into the deque at position i .

If the insertion would cause a bounded deque to grow beyond maxlen , an IndexError 被引发。

3.5 版新增。

pop ( ) ¶

Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError .

popleft ( ) ¶

Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError .

remove ( value ) ¶

Remove the first occurrence of value . If not found, raises a ValueError .

reverse ( ) ¶

Reverse the elements of the deque in-place and then return None .

3.2 版新增。

rotate ( n=1 ) ¶

Rotate the deque n steps to the right. If n is negative, rotate to the left.

When the deque is not empty, rotating one step to the right is equivalent to d.appendleft(d.pop()) , and rotating one step to the left is equivalent to d.append(d.popleft()) .

Deque objects also provide one read-only attribute:

maxlen ¶

Maximum size of a deque or None if unbounded.

3.1 版新增。

In addition to the above, deques support iteration, pickling, len(d) , reversed(d) , copy.copy(d) , copy.deepcopy(d) , membership testing with the in operator, and subscript references such as d[-1] . Indexed access is O(1) at both ends but slows to O(n) in the middle. For fast random access, use lists instead.

Starting in version 3.5, deques support __add__() , __mul__() ，和 __imul__() .

范例：

>>> from collections import deque
>>> d = deque('ghi')                 # make a new deque with three items
>>> for elem in d:                   # iterate over the deque's elements
...     print(elem.upper())
G
H
I
>>> d.append('j')                    # add a new entry to the right side
>>> d.appendleft('f')                # add a new entry to the left side
>>> d                                # show the representation of the deque
deque(['f', 'g', 'h', 'i', 'j'])
>>> d.pop()                          # return and remove the rightmost item
'j'
>>> d.popleft()                      # return and remove the leftmost item
'f'
>>> list(d)                          # list the contents of the deque
['g', 'h', 'i']
>>> d[0]                             # peek at leftmost item
'g'
>>> d[-1]                            # peek at rightmost item
'i'
>>> list(reversed(d))                # list the contents of a deque in reverse
['i', 'h', 'g']
>>> 'h' in d                         # search the deque
True
>>> d.extend('jkl')                  # add multiple elements at once
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> d.rotate(1)                      # right rotation
>>> d
deque(['l', 'g', 'h', 'i', 'j', 'k'])
>>> d.rotate(-1)                     # left rotation
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> deque(reversed(d))               # make a new deque in reverse order
deque(['l', 'k', 'j', 'i', 'h', 'g'])
>>> d.clear()                        # empty the deque
>>> d.pop()                          # cannot pop from an empty deque
Traceback (most recent call last):
    File "<pyshell#6>", line 1, in -toplevel-
        d.pop()
IndexError: pop from an empty deque
>>> d.extendleft('abc')              # extendleft() reverses the input order
>>> d
deque(['c', 'b', 'a'])
								

def tail(filename, n=10):
    'Return the last n lines of a file'
    with open(filename) as f:
        return deque(f, n)
									

def moving_average(iterable, n=3):
    # moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
    # http://en.wikipedia.org/wiki/Moving_average
    it = iter(iterable)
    d = deque(itertools.islice(it, n-1))
    d.appendleft(0)
    s = sum(d)
    for elem in it:
        s += elem - d.popleft()
        d.append(elem)
        yield s / n
									

def delete_nth(d, n):
    d.rotate(-n)
    d.popleft()
    d.rotate(n)
									

8.3.4. defaultdict 对象 ¶

class collections. defaultdict ( [ default_factory [ , ... ] ] ) ¶

返回新的像字典对象。 defaultdict 是子类为内置 dict class. It overrides one method and adds one writable instance variable. The remaining functionality is the same as for the dict class and is not documented here.

The first argument provides the initial value for the default_factory attribute; it defaults to None . All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments.

defaultdict objects support the following method in addition to the standard dict operations:

__missing__ ( key ) ¶

若 default_factory attribute is None , this raises a KeyError exception with the key 作为自变量。

若 default_factory 不是 None , it is called without arguments to provide a default value for the given key , this value is inserted in the dictionary for the key , and returned.

If calling default_factory raises an exception this exception is propagated unchanged.

This method is called by the __getitem__() 方法在 dict class when the requested key is not found; whatever it returns or raises is then returned or raised by __getitem__() .

注意， __missing__() is not called for any operations besides __getitem__() . This means that get() will, like normal dictionaries, return None as a default rather than using default_factory .

defaultdict objects support the following instance variable:

default_factory ¶

This attribute is used by the __missing__() method; it is initialized from the first argument to the constructor, if present, or to None , if absent.

>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
>>> d = defaultdict(list)
>>> for k, v in s:
...     d[k].append(v)
...
>>> sorted(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
									

>>> d = {}
>>> for k, v in s:
...     d.setdefault(k, []).append(v)
...
>>> sorted(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
									

>>> s = 'mississippi'
>>> d = defaultdict(int)
>>> for k in s:
...     d[k] += 1
...
>>> sorted(d.items())
[('i', 4), ('m', 1), ('p', 2), ('s', 4)]
									

>>> def constant_factory(value):
...     return lambda: value
>>> d = defaultdict(constant_factory('<missing>'))
>>> d.update(name='John', action='ran')
>>> '%(name)s %(action)s to %(object)s' % d
'John ran to <missing>'
									

>>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
>>> d = defaultdict(set)
>>> for k, v in s:
...     d[k].add(v)
...
>>> sorted(d.items())
[('blue', {2, 4}), ('red', {1, 3})]
									

8.3.5. namedtuple() 用于带命名字段的元组的工厂函数 ¶

Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.

collections. namedtuple ( typename , field_names , * , verbose=False , rename=False , module=None ) ¶

Returns a new tuple subclass named typename . The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with typename and field_names) and a helpful __repr__() method which lists the tuple contents in a name=value 格式。

field_names are a sequence of strings such as ['x', 'y'] . Alternatively, field_names can be a single string with each fieldname separated by whitespace and/or commas, for example 'x y' or 'x, y' .

Any valid Python identifier may be used for a fieldname except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a keyword such as class , for , return , global , pass ，或 raise .

若 rename is true, invalid fieldnames are automatically replaced with positional names. For example, ['abc', 'def', 'ghi', 'abc'] is converted to ['abc', '_1', 'ghi', '_3'] , eliminating the keyword def and the duplicate fieldname abc .

若 verbose is true, the class definition is printed after it is built. This option is outdated; instead, it is simpler to print the _source 属性。

若 模块 is defined, the __module__ attribute of the named tuple is set to that value.

Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.

3.1 版改变： 添加支持 rename .

3.6 版改变： verbose and rename 参数变为 keyword-only arguments .

3.6 版改变： 添加 模块 参数。

>>> # Basic example
>>> Point = namedtuple('Point', ['x', 'y'])
>>> p = Point(11, y=22)     # instantiate with positional or keyword arguments
>>> p[0] + p[1]             # indexable like the plain tuple (11, 22)
33
>>> x, y = p                # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y               # fields also accessible by name
33
>>> p                       # readable __repr__ with a name=value style
Point(x=11, y=22)
								

Named tuples are especially useful for assigning field names to result tuples returned by the csv or sqlite3 modules:

EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
import csv
for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
    print(emp.name, emp.title)
import sqlite3
conn = sqlite3.connect('/companydata')
cursor = conn.cursor()
cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
for emp in map(EmployeeRecord._make, cursor.fetchall()):
    print(emp.name, emp.title)
								

In addition to the methods inherited from tuples, named tuples support three additional methods and two attributes. To prevent conflicts with field names, the method and attribute names start with an underscore.

classmethod somenamedtuple. _make ( iterable ) ¶

Class method that makes a new instance from an existing sequence or iterable.

>>> t = [11, 22]
>>> Point._make(t)
Point(x=11, y=22)
										

somenamedtuple. _asdict ( ) ¶

返回新 OrderedDict which maps field names to their corresponding values:

>>> p = Point(x=11, y=22)
>>> p._asdict()
OrderedDict([('x', 11), ('y', 22)])
										

3.1 版改变： 返回 OrderedDict instead of a regular dict .

somenamedtuple. _replace ( **kwargs ) ¶

Return a new instance of the named tuple replacing specified fields with new values:

>>> p = Point(x=11, y=22)
>>> p._replace(x=33)
Point(x=33, y=22)
>>> for partnum, record in inventory.items():
...     inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())
										

somenamedtuple. _source ¶

A string with the pure Python source code used to create the named tuple class. The source makes the named tuple self-documenting. It can be printed, executed using exec() , or saved to a file and imported.

3.3 版新增。

somenamedtuple. _fields ¶

Tuple of strings listing the field names. Useful for introspection and for creating new named tuple types from existing named tuples.

>>> p._fields            # view the field names
('x', 'y')
>>> Color = namedtuple('Color', 'red green blue')
>>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)
>>> Pixel(11, 22, 128, 255, 0)
Pixel(x=11, y=22, red=128, green=255, blue=0)
										

To retrieve a field whose name is stored in a string, use the getattr() 函数：

>>> getattr(p, 'x')
11
								

To convert a dictionary to a named tuple, use the double-star-operator (as described in Unpacking Argument Lists ):

>>> d = {'x': 11, 'y': 22}
>>> Point(**d)
Point(x=11, y=22)
								

Since a named tuple is a regular Python class, it is easy to add or change functionality with a subclass. Here is how to add a calculated field and a fixed-width print format:

>>> class Point(namedtuple('Point', ['x', 'y'])):
...     __slots__ = ()
...     @property
...     def hypot(self):
...         return (self.x ** 2 + self.y ** 2) ** 0.5
...     def __str__(self):
...         return 'Point: x=%6.3f  y=%6.3f  hypot=%6.3f' % (self.x, self.y, self.hypot)
>>> for p in Point(3, 4), Point(14, 5/7):
...     print(p)
Point: x= 3.000  y= 4.000  hypot= 5.000
Point: x=14.000  y= 0.714  hypot=14.018
								

The subclass shown above sets __slots__ to an empty tuple. This helps keep memory requirements low by preventing the creation of instance dictionaries.

Subclassing is not useful for adding new, stored fields. Instead, simply create a new named tuple type from the _fields attribute:

>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
								

Docstrings can be customized by making direct assignments to the __doc__ fields:

>>> Book = namedtuple('Book', ['id', 'title', 'authors'])
>>> Book.__doc__ += ': Hardcover book in active collection'
>>> Book.id.__doc__ = '13-digit ISBN'
>>> Book.title.__doc__ = 'Title of first printing'
>>> Book.authors.__doc__ = 'List of authors sorted by last name'
								

Default values can be implemented by using _replace() to customize a prototype instance:

>>> Account = namedtuple('Account', 'owner balance transaction_count')
>>> default_account = Account('<owner name>', 0.0, 0)
>>> johns_account = default_account._replace(owner='John')
>>> janes_account = default_account._replace(owner='Jane')
								

8.3.6. OrderedDict 对象 ¶

Ordered dictionaries are just like regular dictionaries but they remember the order that items were inserted. When iterating over an ordered dictionary, the items are returned in the order their keys were first added.

class collections. OrderedDict ( [ items ] ) ¶

返回 dict 子类实例，支持通常 dict 方法。 OrderedDict is a dict that remembers the order that keys were first inserted. If a new entry overwrites an existing entry, the original insertion position is left unchanged. Deleting an entry and reinserting it will move it to the end.

3.1 版新增。

popitem ( last=True ) ¶

popitem() 方法返回并移除有序词典 (键，值) 对。对被返回按 LIFO 次序若 last 为 True 或 FIFO 次序若 False。

move_to_end ( key , last=True ) ¶

移动现有 key to either end of an ordered dictionary. The item is moved to the right end if last 为 True (默认)，或到开始若 last 为 False。引发 KeyError 若 key 不存在：

>>> d = OrderedDict.fromkeys('abcde')
>>> d.move_to_end('b')
>>> ''.join(d.keys())
'acdeb'
>>> d.move_to_end('b', last=False)
>>> ''.join(d.keys())
'bacde'
												

3.2 版新增。

除通常映射方法外，有序词典还支持反向迭代使用 reversed() .

相等性测试在 OrderedDict 对象是次序敏感的，且被实现为 list(od1.items())==list(od2.items()) 。相等性测试在 OrderedDict 对象和其它 Mapping 对象像常规字典是次序不敏感的。这允许 OrderedDict 对象取代要在任何地方使用的常规字典。

>>> # regular unsorted dictionary
>>> d = {'banana': 3, 'apple': 4, 'pear': 1, 'orange': 2}
>>> # dictionary sorted by key
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])
>>> # dictionary sorted by value
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])
>>> # dictionary sorted by length of the key string
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])
									

class LastUpdatedOrderedDict(OrderedDict):
    'Store items in the order the keys were last added'
    def __setitem__(self, key, value):
        if key in self:
            del self[key]
        OrderedDict.__setitem__(self, key, value)
									

class OrderedCounter(Counter, OrderedDict):
    'Counter that remembers the order elements are first encountered'
    def __repr__(self):
        return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
    def __reduce__(self):
        return self.__class__, (OrderedDict(self),)
									

8.3.7. UserDict 对象 ¶

The class, UserDict acts as a wrapper around dictionary objects. The need for this class has been partially supplanted by the ability to subclass directly from dict ; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.

class collections. UserDict ( [ initialdata ] ) ¶

Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the data attribute of UserDict instances. If initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it be used for other purposes.

In addition to supporting the methods and operations of mappings, UserDict instances provide the following attribute:

data ¶

A real dictionary used to store the contents of the UserDict 类。

8.3.8. UserList 对象 ¶

This class acts as a wrapper around list objects. It is a useful base class for your own list-like classes which can inherit from them and override existing methods or add new ones. In this way, one can add new behaviors to lists.

The need for this class has been partially supplanted by the ability to subclass directly from list ; however, this class can be easier to work with because the underlying list is accessible as an attribute.

class collections. UserList ( [ list ] ) ¶

Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the data attribute of UserList instances. The instance’s contents are initially set to a copy of list , defaulting to the empty list [] . list can be any iterable, for example a real Python list or a UserList 对象。

In addition to supporting the methods and operations of mutable sequences, UserList instances provide the following attribute:

data ¶

A real list object used to store the contents of the UserList 类。

Subclassing requirements: 子类化的 UserList are expected to offer a constructor which can be called with either no arguments or one argument. List operations which return a new sequence attempt to create an instance of the actual implementation class. To do so, it assumes that the constructor can be called with a single parameter, which is a sequence object used as a data source.

If a derived class does not wish to comply with this requirement, all of the special methods supported by this class will need to be overridden; please consult the sources for information about the methods which need to be provided in that case.

8.3.9. UserString 对象 ¶

The class, UserString acts as a wrapper around string objects. The need for this class has been partially supplanted by the ability to subclass directly from str ; however, this class can be easier to work with because the underlying string is accessible as an attribute.

class collections. UserString ( [ sequence ] ) ¶

Class that simulates a string or a Unicode string object. The instance’s content is kept in a regular string object, which is accessible via the data attribute of UserString instances. The instance’s contents are initially set to a copy of sequence 。 sequence can be an instance of bytes , str , UserString (or a subclass) or an arbitrary sequence which can be converted into a string using the built-in str() 函数。

3.5 版改变： New methods __getnewargs__ , __rmod__ , casefold , format_map , isprintable ，和 maketrans .