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# 8.3. `collections` — High-performance container datatypes

2.4 版新加入.

Source code: Lib/collections.py and Lib/_abcoll.py

這個模組實作了一些特別的容器資料型態，用來替代 Python 一般內建的容器，例如 `dict` 、 `list` 、 `set` 和 `tuple` 。

`namedtuple()` | 用來建立一個欄位擁有名字的 tuple 子類別的函數 | 2.6 版新加入.
`deque` | 一個類似 list 的容器，可以快速的在頭尾加入元素與取出元素。 | 2.4 版新加入.
`Counter` | dict 的子類別，用來計算可 hash 物件的數量。 | 2.7 版新加入.
`OrderedDict` | dict 的子類別，會記錄物件被加入的順序。 | 2.7 版新加入.
`defaultdict` | dict 的子類別，在值不存在 dict 當中時會呼叫一個產生函式。 | 2.5 版新加入.

In addition to the concrete container classes, the collections module provides abstract base classes that can be used to test whether a class provides a particular interface, for example, whether it is hashable or a mapping.

## 8.3.1. `Counter` 物件

提供一個計數工具支援方便且快速的對應 ，舉例：

    >>> # 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])

  `Counter` 是 `dict` 的子類別，用來計算可 hash 物件的數量。他是無序的集合容器，物件被當作 dict 的 key 而計數被當作 dict 的 value 儲存。計數可以是包含0與負數的任何整數值。`Counter` 類別類似其他程式語言中的 bags 或 multisets 。

  被計數的元素來自一個 iterable 或是被其他的 mapping (or counter) 初始化。

      >>> 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 物件擁有一個字典的使用介面，除了遇到 Counter 中沒有的值時會回傳計數 0 取代 `KeyError` 這點不同。

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

  將一個值的計數設為 0 並不會真的從 counter 中刪除這個元素，使用 `del` 來刪除元素。

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

  2.7 版新加入.

  除了字典的方法外，Counter 物件額外支援三個新方法。

  `elements`()

    回傳一個每個元素都重複出現計算次數的 iterator 物件，其中元素的排列順序會是隨機的。如果元素的出現次數小於 1 ，`elements()` 方法會忽略這些元素。

        >>> c = Counter(a=4, b=2, c=0, d=-2)
        >>> list(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 is omitted or `None`, `most_common()` returns all elements in the counter. Elements with equal counts are ordered arbitrarily:

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

  `subtract`([iterable-or-mapping])

    Elements are subtracted from an iterable or from another mapping (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})

  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` objects.

  `update`([iterable-or-mapping])

    Elements are counted from an iterable or added-in from another mapping (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` objects:

    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 += Counter()                  # 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})

備註

Counters were primarily designed to work with positive integers to represent running counts; however, care was taken to not unnecessarily preclude use cases needing other types or negative values. To help with those use cases, this section documents the minimum range and type restrictions.

- The `Counter` class itself is a dictionary subclass with no restrictions on its keys and values. The values are intended to be numbers representing counts, but you could store anything in the value field.

- The `most_common()` method requires only that the values be orderable.

- For in-place operations such as `c[key] += 1`, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for `update()` and `subtract()` which allow negative and zero values for both inputs and outputs.

- The multiset methods are designed only for use cases with positive values. The inputs may be negative or zero, but only outputs with positive values are created. There are no type restrictions, but the value type needs to support addition, subtraction, and comparison.

- The `elements()` method requires integer counts. It ignores zero and negative counts.

也參考

- Counter class adapted for Python 2.5 and an early Bag recipe for Python 2.4.

- Bag class in Smalltalk.

- Wikipedia entry for Multisets.

- C++ multisets tutorial with examples.

- For mathematical operations on multisets and their use cases, see Knuth, Donald. The Art of Computer Programming Volume II, Section 4.6.3, Exercise 19.

- To enumerate all distinct multisets of a given size over a given set of elements, see `itertools.combinations_with_replacement()`.

  > map(Counter, combinations_with_replacement(『ABC』, 2)) –> AA AB AC BB BC CC

## 8.3.2. `deque` objects

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

  Returns a new deque object initialized left-to-right (using `append()`) with data from iterable. If 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.

  2.4 版新加入.

  If 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.

  2.6 版更變: Added maxlen parameter.

  Deque objects support the following methods:

  `append`(x)

    Add x to the right side of the deque.

  `appendleft`(x)

    Add x to the left side of the deque.

  `clear`()

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

  `count`(x)

    Count the number of deque elements equal to x.

    2.7 版新加入.

  `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.

  `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`.

    2.5 版新加入.

  `reverse`()

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

    2.7 版新加入.

  `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.

    2.7 版新加入.

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.

Example:

    >>> 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'])

### 8.3.2.1. `deque` Recipes

This section shows various approaches to working with deques.

Bounded length deques provide functionality similar to the `tail` filter in Unix:

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

Another approach to using deques is to maintain a sequence of recently added elements by appending to the right and popping to the left:

    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 / float(n)

The `rotate()` method provides a way to implement `deque` slicing and deletion. For example, a pure Python implementation of `del d[n]` relies on the `rotate()` method to position elements to be popped:

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

To implement `deque` slicing, use a similar approach applying `rotate()` to bring a target element to the left side of the deque. Remove old entries with `popleft()`, add new entries with `extend()`, and then reverse the rotation. With minor variations on that approach, it is easy to implement Forth style stack manipulations such as `dup`, `drop`, `swap`, `over`, `pick`, `rot`, and `roll`.

## 8.3.3. `defaultdict` objects

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

  Returns a new dictionary-like object. `defaultdict` is a subclass of the built-in `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.

  2.5 版新加入.

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

  `__missing__`(key)

    If the `default_factory` attribute is `None`, this raises a `KeyError` exception with the key as argument.

    If `default_factory` is not `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__()` method of the `dict` class when the requested key is not found; whatever it returns or raises is then returned or raised by `__getitem__()`.

    Note that `__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.

### 8.3.3.1. `defaultdict` Examples

Using `list` as the `default_factory`, it is easy to group a sequence of key-value pairs into a dictionary of lists:

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

When each key is encountered for the first time, it is not already in the mapping; so an entry is automatically created using the `default_factory` function which returns an empty `list`. The `list.append()` operation then attaches the value to the new list. When keys are encountered again, the look-up proceeds normally (returning the list for that key) and the `list.append()` operation adds another value to the list. This technique is simpler and faster than an equivalent technique using `dict.setdefault()`:

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

Setting the `default_factory` to `int` makes the `defaultdict` useful for counting (like a bag or multiset in other languages):

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

When a letter is first encountered, it is missing from the mapping, so the `default_factory` function calls `int()` to supply a default count of zero. The increment operation then builds up the count for each letter.

The function `int()` which always returns zero is just a special case of constant functions. A faster and more flexible way to create constant functions is to use `itertools.repeat()` which can supply any constant value (not just zero):

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

Setting the `default_factory` to `set` makes the `defaultdict` useful for building a dictionary of sets:

    >>> 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)
    ...
    >>> d.items()
    [('blue', set([2, 4])), ('red', set([1, 3]))]

## 8.3.4. `namedtuple()` Factory Function for Tuples with Named Fields

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])

  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` format.

  The 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, print, or raise.

  If 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`.

  If verbose is true, the class definition is printed just before being built.

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

  2.6 版新加入.

  2.7 版更變: added support for rename.

Example:

    >>> Point = namedtuple('Point', ['x', 'y'], verbose=True)
    class Point(tuple):
        'Point(x, y)'

        __slots__ = ()

        _fields = ('x', 'y')

        def __new__(_cls, x, y):
            'Create new instance of Point(x, y)'
            return _tuple.__new__(_cls, (x, y))

        @classmethod
        def _make(cls, iterable, new=tuple.__new__, len=len):
            'Make a new Point object from a sequence or iterable'
            result = new(cls, iterable)
            if len(result) != 2:
                raise TypeError('Expected 2 arguments, got %d' % len(result))
            return result

        def __repr__(self):
            'Return a nicely formatted representation string'
            return 'Point(x=%r, y=%r)' % self

        def _asdict(self):
            'Return a new OrderedDict which maps field names to their values'
            return OrderedDict(zip(self._fields, self))

        def _replace(_self, **kwds):
            'Return a new Point object replacing specified fields with new values'
            result = _self._make(map(kwds.pop, ('x', 'y'), _self))
            if kwds:
                raise ValueError('Got unexpected field names: %r' % kwds.keys())
            return result

        def __getnewargs__(self):
            'Return self as a plain tuple.  Used by copy and pickle.'
            return tuple(self)

        __dict__ = _property(_asdict)

        def __getstate__(self):
            'Exclude the OrderedDict from pickling'
            pass

        x = _property(_itemgetter(0), doc='Alias for field number 0')

        y = _property(_itemgetter(1), doc='Alias for field number 1')

    >>> 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 one attribute. 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`()

  Return a new `OrderedDict` which maps field names to their corresponding values:

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

  2.7 版更變: Returns an `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.``_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()` function:

    >>> 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',))

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')

Enumerated constants can be implemented with named tuples, but it is simpler and more efficient to use a simple class declaration:

    >>> Status = namedtuple('Status', 'open pending closed')._make(range(3))
    >>> Status.open, Status.pending, Status.closed
    (0, 1, 2)
    >>> class Status:
    ...     open, pending, closed = range(3)

也參考

Named tuple recipe adapted for Python 2.4.

## 8.3.5. `OrderedDict` objects

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])

  Return an instance of a dict subclass, supporting the usual `dict` methods. An 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.

  2.7 版新加入.

`OrderedDict.``popitem`(last=True)

  The `popitem()` method for ordered dictionaries returns and removes a (key, value) pair. The pairs are returned in LIFO order if last is true or FIFO order if false.

In addition to the usual mapping methods, ordered dictionaries also support reverse iteration using `reversed()`.

Equality tests between `OrderedDict` objects are order-sensitive and are implemented as `list(od1.items())==list(od2.items())`. Equality tests between `OrderedDict` objects and other `Mapping` objects are order-insensitive like regular dictionaries. This allows `OrderedDict` objects to be substituted anywhere a regular dictionary is used.

The `OrderedDict` constructor and `update()` method both accept keyword arguments, but their order is lost because Python’s function call semantics pass-in keyword arguments using a regular unordered dictionary.

也參考

Equivalent OrderedDict recipe that runs on Python 2.4 or later.

### 8.3.5.1. `OrderedDict` Examples and Recipes

Since an ordered dictionary remembers its insertion order, it can be used in conjunction with sorting to make a sorted dictionary:

    >>> # 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)])

The new sorted dictionaries maintain their sort order when entries are deleted. But when new keys are added, the keys are appended to the end and the sort is not maintained.

It is also straight-forward to create an ordered dictionary variant that remembers the order the keys were last inserted. If a new entry overwrites an existing entry, the original insertion position is changed and moved to the end:

    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)

An ordered dictionary can be combined with the `Counter` class so that the counter remembers the order elements are first encountered:

    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),)

## 8.3.6. Collections Abstract Base Classes

The collections module offers the following ABCs:

ABC | Inherits from | Abstract Methods | Mixin Methods
--- | --- | --- | ---
`Container` |  | `__contains__` |
`Hashable` |  | `__hash__` |
`Iterable` |  | `__iter__` |
`Iterator` | `Iterable` | `next` | `__iter__`
`Sized` |  | `__len__` |
`Callable` |  | `__call__` |
`Sequence` | `Sized`, `Iterable`, `Container` | `__getitem__`, `__len__` | `__contains__`, `__iter__`, `__reversed__`, `index`, and `count`
`MutableSequence` | `Sequence` | `__getitem__`, `__setitem__`, `__delitem__`, `__len__`, `insert` | Inherited `Sequence` methods and `append`, `reverse`, `extend`, `pop`, `remove`, and `__iadd__`
`Set` | `Sized`, `Iterable`, `Container` | `__contains__`, `__iter__`, `__len__` | `__le__`, `__lt__`, `__eq__`, `__ne__`, `__gt__`, `__ge__`, `__and__`, `__or__`, `__sub__`, `__xor__`, and `isdisjoint`
`MutableSet` | `Set` | `__contains__`, `__iter__`, `__len__`, `add`, `discard` | Inherited `Set` methods and `clear`, `pop`, `remove`, `__ior__`, `__iand__`, `__ixor__`, and `__isub__`
`Mapping` | `Sized`, `Iterable`, `Container` | `__getitem__`, `__iter__`, `__len__` | `__contains__`, `keys`, `items`, `values`, `get`, `__eq__`, and `__ne__`
`MutableMapping` | `Mapping` | `__getitem__`, `__setitem__`, `__delitem__`, `__iter__`, `__len__` | Inherited `Mapping` methods and `pop`, `popitem`, `clear`, `update`, and `setdefault`
`MappingView` | `Sized` |  | `__len__`
`ItemsView` | `MappingView`, `Set` |  | `__contains__`, `__iter__`
`KeysView` | `MappingView`, `Set` |  | `__contains__`, `__iter__`
`ValuesView` | `MappingView` |  | `__contains__`, `__iter__`

class `collections.``Container`
class `collections.``Hashable`
class `collections.``Sized`
class `collections.``Callable`

  ABCs for classes that provide respectively the methods `__contains__()`, `__hash__()`, `__len__()`, and `__call__()`.

class `collections.``Iterable`

  ABC for classes that provide the `__iter__()` method. See also the definition of iterable.

class `collections.``Iterator`

  ABC for classes that provide the `__iter__()` and `next()` methods. See also the definition of iterator.

class `collections.``Sequence`
class `collections.``MutableSequence`

  ABCs for read-only and mutable sequences.

class `collections.``Set`
class `collections.``MutableSet`

  ABCs for read-only and mutable sets.

class `collections.``Mapping`
class `collections.``MutableMapping`

  ABCs for read-only and mutable mappings.

class `collections.``MappingView`
class `collections.``ItemsView`
class `collections.``KeysView`
class `collections.``ValuesView`

  ABCs for mapping, items, keys, and values views.

These ABCs allow us to ask classes or instances if they provide particular functionality, for example:

    size = None
    if isinstance(myvar, collections.Sized):
        size = len(myvar)

Several of the ABCs are also useful as mixins that make it easier to develop classes supporting container APIs. For example, to write a class supporting the full `Set` API, it only necessary to supply the three underlying abstract methods: `__contains__()`, `__iter__()`, and `__len__()`. The ABC supplies the remaining methods such as `__and__()` and `isdisjoint()`

    class ListBasedSet(collections.Set):
         ''' Alternate set implementation favoring space over speed
             and not requiring the set elements to be hashable. '''
         def __init__(self, iterable):
             self.elements = lst = []
             for value in iterable:
                 if value not in lst:
                     lst.append(value)

         def __iter__(self):
             return iter(self.elements)

         def __contains__(self, value):
             return value in self.elements

         def __len__(self):
             return len(self.elements)

    s1 = ListBasedSet('abcdef')
    s2 = ListBasedSet('defghi')
    overlap = s1 & s2            # The __and__() method is supported automatically

Notes on using `Set` and `MutableSet` as a mixin:

1. Since some set operations create new sets, the default mixin methods need a way to create new instances from an iterable. The class constructor is assumed to have a signature in the form `ClassName(iterable)`. That assumption is factored-out to an internal classmethod called `_from_iterable()` which calls `cls(iterable)` to produce a new set. If the `Set` mixin is being used in a class with a different constructor signature, you will need to override `_from_iterable()` with a classmethod that can construct new instances from an iterable argument.

2. To override the comparisons (presumably for speed, as the semantics are fixed), redefine `__le__()` and `__ge__()`, then the other operations will automatically follow suit.

3. The `Set` mixin provides a `_hash()` method to compute a hash value for the set; however, `__hash__()` is not defined because not all sets are hashable or immutable. To add set hashability using mixins, inherit from both `Set()` and `Hashable()`, then define `__hash__ = Set._hash`.

也參考

- OrderedSet recipe for an example built on `MutableSet`.

- For more about ABCs, see the `abc` module and PEP 3119.