collections — 容器数据类型 ¶

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 ) ¶

返回新的 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:]) .

Note, the iteration order of a ChainMap() is determined by scanning the mappings last to first:

>>> baseline = {'music': 'bach', 'art': 'rembrandt'}
>>> adjustments = {'art': 'van gogh', 'opera': 'carmen'}
>>> list(ChainMap(adjustments, baseline))
['music', 'art', 'opera']
										

This gives the same ordering as a series of dict.update() calls starting with the last mapping:

>>> combined = baseline.copy()
>>> combined.update(adjustments)
>>> list(combined)
['music', 'art', 'opera']
										

3.9 版改变： 添加支持 | and |= operators, specified in PEP 584 .

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 is not None}
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'] = 1            # Set value in current context
d['x']                # Get first key in the chain of contexts
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'})
									

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 a 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。

3.7 版改变： As a dict 子类， Counter Inherited the capability to remember insertion order. Math operations on Counter objects also preserve order. Results are ordered according to when an element is first encountered in the left operand and then by the order encountered in the right operand.

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 the order first encountered. 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 in the order first encountered:

>>> Counter('abracadabra').most_common(3)
[('a', 5), ('b', 2), ('r', 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]) 
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})
								

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 未指定或是 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 ， 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[0] to access the first element. 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 roundrobin(*iterables):
    "roundrobin('ABC', 'D', 'EF') --> A D E B F C"
    iterators = deque(map(iter, iterables))
    while iterators:
        try:
            while True:
                yield next(iterators[0])
                iterators.rotate(-1)
        except StopIteration:
            # Remove an exhausted iterator.
            iterators.popleft()
									

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

defaultdict 对象 ¶

class collections. defaultdict ( default_factory=None , / [ , ... ] ) ¶

返回新的像字典对象。 defaultdict 是子类化的内置 dict 类。它覆盖一种方法并添加一个可写实例变量。其余功能如同 dict 类且这里未文档化。

第一个自变量提供初始值为 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 操作：

__missing__ ( key ) ¶

若 default_factory 属性为 None ，这引发 KeyError 异常采用 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.

若调用 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 对象支持以下实例变量：

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.

3.9 版改变： 添加合并 ( | ) 和更新 ( |= ) 运算符，指定在 PEP 584 .

>>> 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})]
									

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 , * , rename=False , defaults=None , 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 譬如 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 .

defaults 可以是 None or an iterable of default values. Since fields with a default value must come after any fields without a default, the defaults are applied to the rightmost parameters. For example, if the fieldnames are ['x', 'y', 'z'] and the defaults are (1, 2) ，那么 x will be a required argument, y will default to 1 ，和 z will default to 2 .

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.

To support pickling, the named tuple class should be assigned to a variable that matches typename .

3.1 版改变： 添加支持 rename .

3.6 版改变： verbose and rename 参数变为 仅关键词自变量 .

3.6 版改变： 添加 模块 参数。

3.7 版改变： 移除 verbose 参数和 _source 属性。

3.7 版改变： 添加 defaults 参数和 _field_defaults 属性。

>>> # 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 ( ) ¶

返回新的 dict which maps field names to their corresponding values:

>>> p = Point(x=11, y=22)
>>> p._asdict()
{'x': 11, 'y': 22}
										

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

3.8 版改变： Returns a regular dict instead of an OrderedDict . As of Python 3.7, regular dicts are guaranteed to be ordered. If the extra features of OrderedDict are required, the suggested remediation is to cast the result to the desired type: OrderedDict(nt._asdict()) .

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. _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)
										

somenamedtuple. _field_defaults ¶

Dictionary mapping field names to default values.

>>> Account = namedtuple('Account', ['type', 'balance'], defaults=[0])
>>> Account._field_defaults
{'balance': 0}
>>> Account('premium')
Account(type='premium', balance=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 解包自变量列表 ):

>>> 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 属性：

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

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

>>> 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'
								

OrderedDict 对象 ¶

有序字典就像普通字典，但拥有一些排序操作相关的额外能力。现在它们变得不太重要了，内置 dict 类获得了记住插入次序的能力 (这种新行为在 Python 3.7 获得保证)。

一些差异同 dict 仍然保留：

• 常规 dict 被设计成非常擅长映射操作。跟踪插入次序是次要的。

• OrderedDict was designed to be good at reordering operations. Space efficiency, iteration speed, and the performance of update operations were secondary.

• 从算法上讲， OrderedDict can handle frequent reordering operations better than dict . This makes it suitable for tracking recent accesses (for example in an LRU cache ).

• The equality operation for OrderedDict checks for matching order.

• popitem() 方法的 OrderedDict has a different signature. It accepts an optional argument to specify which item is popped.

• OrderedDict 拥有 move_to_end() method to efficiently reposition an element to an endpoint.

• 直到 Python 3.8， dict 缺乏 __reversed__() 方法。

class collections. OrderedDict ( [ items ] ) ¶

返回实例化的 dict 子类拥有专用于重新排列字典次序的方法。

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 对象要在任何地方使用常规字典被代入。

class LastUpdatedOrderedDict(OrderedDict):
    'Store items in the order the keys were last added'
    def __setitem__(self, key, value):
        super().__setitem__(key, value)
        self.move_to_end(key)
									

class LRU:
    def __init__(self, func, maxsize=128):
        self.func = func
        self.maxsize = maxsize
        self.cache = OrderedDict()
    def __call__(self, *args):
        if args in self.cache:
            value = self.cache[args]
            self.cache.move_to_end(args)
            return value
        value = self.func(*args)
        if len(self.cache) >= self.maxsize:
            self.cache.popitem(False)
        self.cache[args] = value
        return value
									

UserDict 对象 ¶

类 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 实例。若 initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it to 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。

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.

UserString 对象 ¶

类 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 ( seq ) ¶

Class that simulates a 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 seq 。 seq argument can be any object which can be converted into a string using the built-in str() 函数。

In addition to supporting the methods and operations of strings, UserString instances provide the following attribute:

data ¶

A real str object used to store the contents of the UserString 类。

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