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:mod:`collections` --- High-performance container datatypes
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===========================================================
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.. module:: collections
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:synopsis: High-performance datatypes
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.. moduleauthor:: Raymond Hettinger <python@rcn.com>
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.. sectionauthor:: Raymond Hettinger <python@rcn.com>
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from collections import *
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__name__ = '<doctest>'
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**Source code:** :source:`Lib/collections.py` and :source:`Lib/_abcoll.py`
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This module implements specialized container datatypes providing alternatives to
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Python's general purpose built-in containers, :class:`dict`, :class:`list`,
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:class:`set`, and :class:`tuple`.
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===================== ==================================================================== ===========================
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:func:`namedtuple` factory function for creating tuple subclasses with named fields .. versionadded:: 2.6
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:class:`deque` list-like container with fast appends and pops on either end .. versionadded:: 2.4
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:class:`Counter` dict subclass for counting hashable objects .. versionadded:: 2.7
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:class:`OrderedDict` dict subclass that remembers the order entries were added .. versionadded:: 2.7
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:class:`defaultdict` dict subclass that calls a factory function to supply missing values .. versionadded:: 2.5
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===================== ==================================================================== ===========================
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In addition to the concrete container classes, the collections module provides
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:ref:`abstract base classes <collections-abstract-base-classes>` that can be
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used to test whether a class provides a particular interface, for example,
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whether it is hashable or a mapping.
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:class:`Counter` objects
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------------------------
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A counter tool is provided to support convenient and rapid tallies.
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>>> # Tally occurrences of words in a list
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>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
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Counter({'blue': 3, 'red': 2, 'green': 1})
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>>> # Find the ten most common words in Hamlet
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>>> words = re.findall(r'\w+', open('hamlet.txt').read().lower())
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>>> Counter(words).most_common(10)
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[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),
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('you', 554), ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]
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.. class:: Counter([iterable-or-mapping])
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A :class:`Counter` is a :class:`dict` subclass for counting hashable objects.
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It is an unordered collection where elements are stored as dictionary keys
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and their counts are stored as dictionary values. Counts are allowed to be
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any integer value including zero or negative counts. The :class:`Counter`
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class is similar to bags or multisets in other languages.
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Elements are counted from an *iterable* or initialized from another
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*mapping* (or counter):
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>>> c = Counter() # a new, empty counter
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>>> c = Counter('gallahad') # a new counter from an iterable
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>>> c = Counter({'red': 4, 'blue': 2}) # a new counter from a mapping
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>>> c = Counter(cats=4, dogs=8) # a new counter from keyword args
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Counter objects have a dictionary interface except that they return a zero
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count for missing items instead of raising a :exc:`KeyError`:
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>>> c = Counter(['eggs', 'ham'])
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>>> c['bacon'] # count of a missing element is zero
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Setting a count to zero does not remove an element from a counter.
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Use ``del`` to remove it entirely:
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>>> c['sausage'] = 0 # counter entry with a zero count
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>>> del c['sausage'] # del actually removes the entry
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Counter objects support three methods beyond those available for all
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.. method:: elements()
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Return an iterator over elements repeating each as many times as its
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count. Elements are returned in arbitrary order. If an element's count
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is less than one, :meth:`elements` will ignore it.
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>>> c = Counter(a=4, b=2, c=0, d=-2)
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>>> list(c.elements())
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['a', 'a', 'a', 'a', 'b', 'b']
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.. method:: most_common([n])
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Return a list of the *n* most common elements and their counts from the
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most common to the least. If *n* is omitted or ``None``,
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:func:`most_common` returns *all* elements in the counter.
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Elements with equal counts are ordered arbitrarily:
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>>> Counter('abracadabra').most_common(3)
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[('a', 5), ('r', 2), ('b', 2)]
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.. method:: subtract([iterable-or-mapping])
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Elements are subtracted from an *iterable* or from another *mapping*
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(or counter). Like :meth:`dict.update` but subtracts counts instead
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of replacing them. Both inputs and outputs may be zero or negative.
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>>> c = Counter(a=4, b=2, c=0, d=-2)
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>>> d = Counter(a=1, b=2, c=3, d=4)
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Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
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The usual dictionary methods are available for :class:`Counter` objects
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except for two which work differently for counters.
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.. method:: fromkeys(iterable)
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This class method is not implemented for :class:`Counter` objects.
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.. method:: update([iterable-or-mapping])
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Elements are counted from an *iterable* or added-in from another
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*mapping* (or counter). Like :meth:`dict.update` but adds counts
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instead of replacing them. Also, the *iterable* is expected to be a
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sequence of elements, not a sequence of ``(key, value)`` pairs.
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Common patterns for working with :class:`Counter` objects::
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sum(c.values()) # total of all counts
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c.clear() # reset all counts
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list(c) # list unique elements
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set(c) # convert to a set
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dict(c) # convert to a regular dictionary
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c.items() # convert to a list of (elem, cnt) pairs
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Counter(dict(list_of_pairs)) # convert from a list of (elem, cnt) pairs
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c.most_common()[:-n-1:-1] # n least common elements
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c += Counter() # remove zero and negative counts
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Several mathematical operations are provided for combining :class:`Counter`
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objects to produce multisets (counters that have counts greater than zero).
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Addition and subtraction combine counters by adding or subtracting the counts
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of corresponding elements. Intersection and union return the minimum and
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maximum of corresponding counts. Each operation can accept inputs with signed
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counts, but the output will exclude results with counts of zero or less.
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>>> c = Counter(a=3, b=1)
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>>> d = Counter(a=1, b=2)
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>>> c + d # add two counters together: c[x] + d[x]
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Counter({'a': 4, 'b': 3})
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>>> c - d # subtract (keeping only positive counts)
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>>> c & d # intersection: min(c[x], d[x])
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Counter({'a': 1, 'b': 1})
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>>> c | d # union: max(c[x], d[x])
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Counter({'a': 3, 'b': 2})
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Counters were primarily designed to work with positive integers to represent
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running counts; however, care was taken to not unnecessarily preclude use
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cases needing other types or negative values. To help with those use cases,
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this section documents the minimum range and type restrictions.
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* The :class:`Counter` class itself is a dictionary subclass with no
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restrictions on its keys and values. The values are intended to be numbers
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representing counts, but you *could* store anything in the value field.
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* The :meth:`most_common` method requires only that the values be orderable.
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* For in-place operations such as ``c[key] += 1``, the value type need only
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support addition and subtraction. So fractions, floats, and decimals would
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work and negative values are supported. The same is also true for
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:meth:`update` and :meth:`subtract` which allow negative and zero values
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for both inputs and outputs.
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* The multiset methods are designed only for use cases with positive values.
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The inputs may be negative or zero, but only outputs with positive values
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are created. There are no type restrictions, but the value type needs to
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support addition, subtraction, and comparison.
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* The :meth:`elements` method requires integer counts. It ignores zero and
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* `Counter class <http://code.activestate.com/recipes/576611/>`_
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adapted for Python 2.5 and an early `Bag recipe
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<http://code.activestate.com/recipes/259174/>`_ for Python 2.4.
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* `Bag class <http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html>`_
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* Wikipedia entry for `Multisets <http://en.wikipedia.org/wiki/Multiset>`_.
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* `C++ multisets <http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm>`_
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tutorial with examples.
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* For mathematical operations on multisets and their use cases, see
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*Knuth, Donald. The Art of Computer Programming Volume II,
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Section 4.6.3, Exercise 19*.
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* To enumerate all distinct multisets of a given size over a given set of
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elements, see :func:`itertools.combinations_with_replacement`.
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map(Counter, combinations_with_replacement('ABC', 2)) --> AA AB AC BB BC CC
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:class:`deque` objects
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----------------------
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.. class:: deque([iterable[, maxlen]])
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Returns a new deque object initialized left-to-right (using :meth:`append`) with
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data from *iterable*. If *iterable* is not specified, the new deque is empty.
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Deques are a generalization of stacks and queues (the name is pronounced "deck"
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and is short for "double-ended queue"). Deques support thread-safe, memory
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efficient appends and pops from either side of the deque with approximately the
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same O(1) performance in either direction.
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Though :class:`list` objects support similar operations, they are optimized for
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fast fixed-length operations and incur O(n) memory movement costs for
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``pop(0)`` and ``insert(0, v)`` operations which change both the size and
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position of the underlying data representation.
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.. versionadded:: 2.4
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If *maxlen* is not specified or is *None*, deques may grow to an
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arbitrary length. Otherwise, the deque is bounded to the specified maximum
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length. Once a bounded length deque is full, when new items are added, a
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corresponding number of items are discarded from the opposite end. Bounded
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length deques provide functionality similar to the ``tail`` filter in
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Unix. They are also useful for tracking transactions and other pools of data
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where only the most recent activity is of interest.
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.. versionchanged:: 2.6
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Added *maxlen* parameter.
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Deque objects support the following methods:
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.. method:: append(x)
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Add *x* to the right side of the deque.
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.. method:: appendleft(x)
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Add *x* to the left side of the deque.
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Remove all elements from the deque leaving it with length 0.
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Count the number of deque elements equal to *x*.
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.. versionadded:: 2.7
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.. method:: extend(iterable)
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Extend the right side of the deque by appending elements from the iterable
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.. method:: extendleft(iterable)
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Extend the left side of the deque by appending elements from *iterable*.
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Note, the series of left appends results in reversing the order of
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elements in the iterable argument.
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Remove and return an element from the right side of the deque. If no
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elements are present, raises an :exc:`IndexError`.
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.. method:: popleft()
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Remove and return an element from the left side of the deque. If no
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elements are present, raises an :exc:`IndexError`.
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.. method:: remove(value)
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Removed the first occurrence of *value*. If not found, raises a
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.. versionadded:: 2.5
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.. method:: reverse()
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Reverse the elements of the deque in-place and then return ``None``.
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.. versionadded:: 2.7
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.. method:: rotate(n)
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Rotate the deque *n* steps to the right. If *n* is negative, rotate to
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the left. Rotating one step to the right is equivalent to:
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``d.appendleft(d.pop())``.
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Deque objects also provide one read-only attribute:
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.. attribute:: maxlen
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Maximum size of a deque or *None* if unbounded.
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.. versionadded:: 2.7
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In addition to the above, deques support iteration, pickling, ``len(d)``,
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``reversed(d)``, ``copy.copy(d)``, ``copy.deepcopy(d)``, membership testing with
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the :keyword:`in` operator, and subscript references such as ``d[-1]``. Indexed
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access is O(1) at both ends but slows to O(n) in the middle. For fast random
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access, use lists instead.
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>>> from collections import deque
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>>> d = deque('ghi') # make a new deque with three items
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>>> for elem in d: # iterate over the deque's elements
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... print elem.upper()
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>>> d.append('j') # add a new entry to the right side
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>>> d.appendleft('f') # add a new entry to the left side
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>>> d # show the representation of the deque
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deque(['f', 'g', 'h', 'i', 'j'])
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>>> d.pop() # return and remove the rightmost item
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>>> d.popleft() # return and remove the leftmost item
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>>> list(d) # list the contents of the deque
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>>> d[0] # peek at leftmost item
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>>> d[-1] # peek at rightmost item
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>>> list(reversed(d)) # list the contents of a deque in reverse
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>>> 'h' in d # search the deque
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>>> d.extend('jkl') # add multiple elements at once
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deque(['g', 'h', 'i', 'j', 'k', 'l'])
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>>> d.rotate(1) # right rotation
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deque(['l', 'g', 'h', 'i', 'j', 'k'])
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>>> d.rotate(-1) # left rotation
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deque(['g', 'h', 'i', 'j', 'k', 'l'])
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>>> deque(reversed(d)) # make a new deque in reverse order
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deque(['l', 'k', 'j', 'i', 'h', 'g'])
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>>> d.clear() # empty the deque
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>>> d.pop() # cannot pop from an empty deque
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Traceback (most recent call last):
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File "<pyshell#6>", line 1, in -toplevel-
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IndexError: pop from an empty deque
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>>> d.extendleft('abc') # extendleft() reverses the input order
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deque(['c', 'b', 'a'])
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:class:`deque` Recipes
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^^^^^^^^^^^^^^^^^^^^^^
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This section shows various approaches to working with deques.
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Bounded length deques provide functionality similar to the ``tail`` filter
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def tail(filename, n=10):
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'Return the last n lines of a file'
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return deque(open(filename), n)
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Another approach to using deques is to maintain a sequence of recently
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added elements by appending to the right and popping to the left::
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def moving_average(iterable, n=3):
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# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
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# http://en.wikipedia.org/wiki/Moving_average
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d = deque(itertools.islice(it, n-1))
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s += elem - d.popleft()
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The :meth:`rotate` method provides a way to implement :class:`deque` slicing and
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deletion. For example, a pure Python implementation of ``del d[n]`` relies on
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the :meth:`rotate` method to position elements to be popped::
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def delete_nth(d, n):
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To implement :class:`deque` slicing, use a similar approach applying
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:meth:`rotate` to bring a target element to the left side of the deque. Remove
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old entries with :meth:`popleft`, add new entries with :meth:`extend`, and then
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reverse the rotation.
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With minor variations on that approach, it is easy to implement Forth style
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stack manipulations such as ``dup``, ``drop``, ``swap``, ``over``, ``pick``,
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``rot``, and ``roll``.
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:class:`defaultdict` objects
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----------------------------
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.. class:: defaultdict([default_factory[, ...]])
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Returns a new dictionary-like object. :class:`defaultdict` is a subclass of the
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built-in :class:`dict` class. It overrides one method and adds one writable
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instance variable. The remaining functionality is the same as for the
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:class:`dict` class and is not documented here.
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The first argument provides the initial value for the :attr:`default_factory`
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attribute; it defaults to ``None``. All remaining arguments are treated the same
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as if they were passed to the :class:`dict` constructor, including keyword
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.. versionadded:: 2.5
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:class:`defaultdict` objects support the following method in addition to the
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standard :class:`dict` operations:
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.. method:: __missing__(key)
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If the :attr:`default_factory` attribute is ``None``, this raises a
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:exc:`KeyError` exception with the *key* as argument.
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If :attr:`default_factory` is not ``None``, it is called without arguments
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to provide a default value for the given *key*, this value is inserted in
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the dictionary for the *key*, and returned.
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If calling :attr:`default_factory` raises an exception this exception is
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propagated unchanged.
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This method is called by the :meth:`__getitem__` method of the
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:class:`dict` class when the requested key is not found; whatever it
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returns or raises is then returned or raised by :meth:`__getitem__`.
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Note that :meth:`__missing__` is *not* called for any operations besides
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:meth:`__getitem__`. This means that :meth:`get` will, like normal
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dictionaries, return ``None`` as a default rather than using
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:attr:`default_factory`.
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:class:`defaultdict` objects support the following instance variable:
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.. attribute:: default_factory
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This attribute is used by the :meth:`__missing__` method; it is
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initialized from the first argument to the constructor, if present, or to
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:class:`defaultdict` Examples
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Using :class:`list` as the :attr:`default_factory`, it is easy to group a
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sequence of key-value pairs into a dictionary of lists:
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>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
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>>> d = defaultdict(list)
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[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
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When each key is encountered for the first time, it is not already in the
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mapping; so an entry is automatically created using the :attr:`default_factory`
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function which returns an empty :class:`list`. The :meth:`list.append`
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operation then attaches the value to the new list. When keys are encountered
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again, the look-up proceeds normally (returning the list for that key) and the
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:meth:`list.append` operation adds another value to the list. This technique is
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simpler and faster than an equivalent technique using :meth:`dict.setdefault`:
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... d.setdefault(k, []).append(v)
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[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
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Setting the :attr:`default_factory` to :class:`int` makes the
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:class:`defaultdict` useful for counting (like a bag or multiset in other
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>>> s = 'mississippi'
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>>> d = defaultdict(int)
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[('i', 4), ('p', 2), ('s', 4), ('m', 1)]
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When a letter is first encountered, it is missing from the mapping, so the
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:attr:`default_factory` function calls :func:`int` to supply a default count of
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zero. The increment operation then builds up the count for each letter.
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The function :func:`int` which always returns zero is just a special case of
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constant functions. A faster and more flexible way to create constant functions
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is to use :func:`itertools.repeat` which can supply any constant value (not just
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>>> def constant_factory(value):
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... return itertools.repeat(value).next
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>>> d = defaultdict(constant_factory('<missing>'))
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>>> d.update(name='John', action='ran')
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>>> '%(name)s %(action)s to %(object)s' % d
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'John ran to <missing>'
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Setting the :attr:`default_factory` to :class:`set` makes the
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:class:`defaultdict` useful for building a dictionary of sets:
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>>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
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>>> d = defaultdict(set)
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[('blue', set([2, 4])), ('red', set([1, 3]))]
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:func:`namedtuple` Factory Function for Tuples with Named Fields
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----------------------------------------------------------------
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Named tuples assign meaning to each position in a tuple and allow for more readable,
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self-documenting code. They can be used wherever regular tuples are used, and
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they add the ability to access fields by name instead of position index.
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.. function:: namedtuple(typename, field_names, [verbose=False], [rename=False])
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Returns a new tuple subclass named *typename*. The new subclass is used to
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create tuple-like objects that have fields accessible by attribute lookup as
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well as being indexable and iterable. Instances of the subclass also have a
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helpful docstring (with typename and field_names) and a helpful :meth:`__repr__`
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method which lists the tuple contents in a ``name=value`` format.
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The *field_names* are a sequence of strings such as ``['x', 'y']``.
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Alternatively, *field_names* can be a single string with each fieldname
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separated by whitespace and/or commas, for example ``'x y'`` or ``'x, y'``.
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Any valid Python identifier may be used for a fieldname except for names
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starting with an underscore. Valid identifiers consist of letters, digits,
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and underscores but do not start with a digit or underscore and cannot be
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a :mod:`keyword` such as *class*, *for*, *return*, *global*, *pass*, *print*,
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If *rename* is true, invalid fieldnames are automatically replaced
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with positional names. For example, ``['abc', 'def', 'ghi', 'abc']`` is
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converted to ``['abc', '_1', 'ghi', '_3']``, eliminating the keyword
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``def`` and the duplicate fieldname ``abc``.
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If *verbose* is true, the class definition is printed just before being built.
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Named tuple instances do not have per-instance dictionaries, so they are
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lightweight and require no more memory than regular tuples.
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.. versionadded:: 2.6
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.. versionchanged:: 2.7
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added support for *rename*.
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:options: +NORMALIZE_WHITESPACE
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>>> Point = namedtuple('Point', ['x', 'y'], verbose=True)
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def __new__(_cls, x, y):
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'Create a new instance of Point(x, y)'
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return _tuple.__new__(_cls, (x, y))
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def _make(cls, iterable, new=tuple.__new__, len=len):
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'Make a new Point object from a sequence or iterable'
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result = new(cls, iterable)
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raise TypeError('Expected 2 arguments, got %d' % len(result))
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'Return a nicely formatted representation string'
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return 'Point(x=%r, y=%r)' % self
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'Return a new OrderedDict which maps field names to their values'
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return OrderedDict(zip(self._fields, self))
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def _replace(_self, **kwds):
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'Return a new Point object replacing specified fields with new values'
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result = _self._make(map(kwds.pop, ('x', 'y'), _self))
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raise ValueError('Got unexpected field names: %r' % kwds.keys())
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def __getnewargs__(self):
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'Return self as a plain tuple. Used by copy and pickle.'
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__dict__ = _property(_asdict)
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def __getstate__(self):
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'Exclude the OrderedDict from pickling'
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x = _property(_itemgetter(0), doc='Alias for field number 0')
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y = _property(_itemgetter(1), doc='Alias for field number 1')
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>>> p = Point(11, y=22) # instantiate with positional or keyword arguments
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>>> p[0] + p[1] # indexable like the plain tuple (11, 22)
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>>> x, y = p # unpack like a regular tuple
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>>> p.x + p.y # fields also accessible by name
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>>> p # readable __repr__ with a name=value style
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Named tuples are especially useful for assigning field names to result tuples returned
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by the :mod:`csv` or :mod:`sqlite3` modules::
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EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
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for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
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print emp.name, emp.title
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conn = sqlite3.connect('/companydata')
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cursor = conn.cursor()
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cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
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for emp in map(EmployeeRecord._make, cursor.fetchall()):
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print emp.name, emp.title
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In addition to the methods inherited from tuples, named tuples support
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three additional methods and one attribute. To prevent conflicts with
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field names, the method and attribute names start with an underscore.
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.. classmethod:: somenamedtuple._make(iterable)
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Class method that makes a new instance from an existing sequence or iterable.
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.. method:: somenamedtuple._asdict()
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Return a new :class:`OrderedDict` which maps field names to their corresponding
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>>> p = Point(x=11, y=22)
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OrderedDict([('x', 11), ('y', 22)])
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.. versionchanged:: 2.7
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Returns an :class:`OrderedDict` instead of a regular :class:`dict`.
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.. method:: somenamedtuple._replace(kwargs)
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Return a new instance of the named tuple replacing specified fields with new
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>>> p = Point(x=11, y=22)
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>>> for partnum, record in inventory.items():
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inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())
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.. attribute:: somenamedtuple._fields
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Tuple of strings listing the field names. Useful for introspection
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and for creating new named tuple types from existing named tuples.
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>>> p._fields # view the field names
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>>> Color = namedtuple('Color', 'red green blue')
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>>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)
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>>> Pixel(11, 22, 128, 255, 0)
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Pixel(x=11, y=22, red=128, green=255, blue=0)
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To retrieve a field whose name is stored in a string, use the :func:`getattr`
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To convert a dictionary to a named tuple, use the double-star-operator
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(as described in :ref:`tut-unpacking-arguments`):
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>>> d = {'x': 11, 'y': 22}
746
Since a named tuple is a regular Python class, it is easy to add or change
747
functionality with a subclass. Here is how to add a calculated field and
748
a fixed-width print format:
750
>>> class Point(namedtuple('Point', 'x y')):
754
return (self.x ** 2 + self.y ** 2) ** 0.5
756
return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
758
>>> for p in Point(3, 4), Point(14, 5/7.):
760
Point: x= 3.000 y= 4.000 hypot= 5.000
761
Point: x=14.000 y= 0.714 hypot=14.018
763
The subclass shown above sets ``__slots__`` to an empty tuple. This helps
764
keep memory requirements low by preventing the creation of instance dictionaries.
766
Subclassing is not useful for adding new, stored fields. Instead, simply
767
create a new named tuple type from the :attr:`_fields` attribute:
769
>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
771
Default values can be implemented by using :meth:`_replace` to
772
customize a prototype instance:
774
>>> Account = namedtuple('Account', 'owner balance transaction_count')
775
>>> default_account = Account('<owner name>', 0.0, 0)
776
>>> johns_account = default_account._replace(owner='John')
778
Enumerated constants can be implemented with named tuples, but it is simpler
779
and more efficient to use a simple class declaration:
781
>>> Status = namedtuple('Status', 'open pending closed')._make(range(3))
782
>>> Status.open, Status.pending, Status.closed
785
open, pending, closed = range(3)
789
`Named tuple recipe <http://code.activestate.com/recipes/500261/>`_
790
adapted for Python 2.4.
793
:class:`OrderedDict` objects
794
----------------------------
796
Ordered dictionaries are just like regular dictionaries but they remember the
797
order that items were inserted. When iterating over an ordered dictionary,
798
the items are returned in the order their keys were first added.
800
.. class:: OrderedDict([items])
802
Return an instance of a dict subclass, supporting the usual :class:`dict`
803
methods. An *OrderedDict* is a dict that remembers the order that keys
804
were first inserted. If a new entry overwrites an existing entry, the
805
original insertion position is left unchanged. Deleting an entry and
806
reinserting it will move it to the end.
808
.. versionadded:: 2.7
810
.. method:: OrderedDict.popitem(last=True)
812
The :meth:`popitem` method for ordered dictionaries returns and removes
813
a (key, value) pair. The pairs are returned in LIFO order if *last* is
814
true or FIFO order if false.
816
In addition to the usual mapping methods, ordered dictionaries also support
817
reverse iteration using :func:`reversed`.
819
Equality tests between :class:`OrderedDict` objects are order-sensitive
820
and are implemented as ``list(od1.items())==list(od2.items())``.
821
Equality tests between :class:`OrderedDict` objects and other
822
:class:`Mapping` objects are order-insensitive like regular
823
dictionaries. This allows :class:`OrderedDict` objects to be substituted
824
anywhere a regular dictionary is used.
826
The :class:`OrderedDict` constructor and :meth:`update` method both accept
827
keyword arguments, but their order is lost because Python's function call
828
semantics pass-in keyword arguments using a regular unordered dictionary.
832
`Equivalent OrderedDict recipe <http://code.activestate.com/recipes/576693/>`_
833
that runs on Python 2.4 or later.
835
:class:`OrderedDict` Examples and Recipes
836
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
838
Since an ordered dictionary remembers its insertion order, it can be used
839
in conjuction with sorting to make a sorted dictionary::
841
>>> # regular unsorted dictionary
842
>>> d = {'banana': 3, 'apple':4, 'pear': 1, 'orange': 2}
844
>>> # dictionary sorted by key
845
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
846
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])
848
>>> # dictionary sorted by value
849
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
850
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])
852
>>> # dictionary sorted by length of the key string
853
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
854
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])
856
The new sorted dictionaries maintain their sort order when entries
857
are deleted. But when new keys are added, the keys are appended
858
to the end and the sort is not maintained.
860
It is also straight-forward to create an ordered dictionary variant
861
that remembers the order the keys were *last* inserted.
862
If a new entry overwrites an existing entry, the
863
original insertion position is changed and moved to the end::
865
class LastUpdatedOrderedDict(OrderedDict):
866
'Store items in the order the keys were last added'
868
def __setitem__(self, key, value):
871
OrderedDict.__setitem__(self, key, value)
873
An ordered dictionary can be combined with the :class:`Counter` class
874
so that the counter remembers the order elements are first encountered::
876
class OrderedCounter(Counter, OrderedDict):
877
'Counter that remembers the order elements are first encountered'
880
return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
882
def __reduce__(self):
883
return self.__class__, (OrderedDict(self),)
886
.. _collections-abstract-base-classes:
888
Collections Abstract Base Classes
889
---------------------------------
891
The collections module offers the following :term:`ABCs <abstract base class>`:
893
========================= ===================== ====================== ====================================================
894
ABC Inherits from Abstract Methods Mixin Methods
895
========================= ===================== ====================== ====================================================
896
:class:`Container` ``__contains__``
897
:class:`Hashable` ``__hash__``
898
:class:`Iterable` ``__iter__``
899
:class:`Iterator` :class:`Iterable` ``next`` ``__iter__``
900
:class:`Sized` ``__len__``
901
:class:`Callable` ``__call__``
903
:class:`Sequence` :class:`Sized`, ``__getitem__``, ``__contains__``, ``__iter__``, ``__reversed__``,
904
:class:`Iterable`, ``__len__`` ``index``, and ``count``
907
:class:`MutableSequence` :class:`Sequence` ``__getitem__``, Inherited :class:`Sequence` methods and
908
``__setitem__``, ``append``, ``reverse``, ``extend``, ``pop``,
909
``__delitem__``, ``remove``, and ``__iadd__``
913
:class:`Set` :class:`Sized`, ``__contains__``, ``__le__``, ``__lt__``, ``__eq__``, ``__ne__``,
914
:class:`Iterable`, ``__iter__``, ``__gt__``, ``__ge__``, ``__and__``, ``__or__``,
915
:class:`Container` ``__len__`` ``__sub__``, ``__xor__``, and ``isdisjoint``
917
:class:`MutableSet` :class:`Set` ``__contains__``, Inherited :class:`Set` methods and
918
``__iter__``, ``clear``, ``pop``, ``remove``, ``__ior__``,
919
``__len__``, ``__iand__``, ``__ixor__``, and ``__isub__``
923
:class:`Mapping` :class:`Sized`, ``__getitem__``, ``__contains__``, ``keys``, ``items``, ``values``,
924
:class:`Iterable`, ``__iter__``, ``get``, ``__eq__``, and ``__ne__``
925
:class:`Container` ``__len__``
927
:class:`MutableMapping` :class:`Mapping` ``__getitem__``, Inherited :class:`Mapping` methods and
928
``__setitem__``, ``pop``, ``popitem``, ``clear``, ``update``,
929
``__delitem__``, and ``setdefault``
934
:class:`MappingView` :class:`Sized` ``__len__``
935
:class:`ItemsView` :class:`MappingView`, ``__contains__``,
936
:class:`Set` ``__iter__``
937
:class:`KeysView` :class:`MappingView`, ``__contains__``,
938
:class:`Set` ``__iter__``
939
:class:`ValuesView` :class:`MappingView` ``__contains__``, ``__iter__``
940
========================= ===================== ====================== ====================================================
948
ABCs for classes that provide respectively the methods :meth:`__contains__`,
949
:meth:`__hash__`, :meth:`__len__`, and :meth:`__call__`.
953
ABC for classes that provide the :meth:`__iter__` method.
954
See also the definition of :term:`iterable`.
958
ABC for classes that provide the :meth:`~iterator.__iter__` and
959
:meth:`~iterator.next` methods. See also the definition of :term:`iterator`.
964
ABCs for read-only and mutable :term:`sequences <sequence>`.
969
ABCs for read-only and mutable sets.
974
ABCs for read-only and mutable :term:`mappings <mapping>`.
976
.. class:: MappingView
981
ABCs for mapping, items, keys, and values :term:`views <dictionary view>`.
984
These ABCs allow us to ask classes or instances if they provide
985
particular functionality, for example::
988
if isinstance(myvar, collections.Sized):
991
Several of the ABCs are also useful as mixins that make it easier to develop
992
classes supporting container APIs. For example, to write a class supporting
993
the full :class:`Set` API, it only necessary to supply the three underlying
994
abstract methods: :meth:`__contains__`, :meth:`__iter__`, and :meth:`__len__`.
995
The ABC supplies the remaining methods such as :meth:`__and__` and
996
:meth:`isdisjoint` ::
998
class ListBasedSet(collections.Set):
999
''' Alternate set implementation favoring space over speed
1000
and not requiring the set elements to be hashable. '''
1001
def __init__(self, iterable):
1002
self.elements = lst = []
1003
for value in iterable:
1004
if value not in lst:
1007
return iter(self.elements)
1008
def __contains__(self, value):
1009
return value in self.elements
1011
return len(self.elements)
1013
s1 = ListBasedSet('abcdef')
1014
s2 = ListBasedSet('defghi')
1015
overlap = s1 & s2 # The __and__() method is supported automatically
1017
Notes on using :class:`Set` and :class:`MutableSet` as a mixin:
1020
Since some set operations create new sets, the default mixin methods need
1021
a way to create new instances from an iterable. The class constructor is
1022
assumed to have a signature in the form ``ClassName(iterable)``.
1023
That assumption is factored-out to an internal classmethod called
1024
:meth:`_from_iterable` which calls ``cls(iterable)`` to produce a new set.
1025
If the :class:`Set` mixin is being used in a class with a different
1026
constructor signature, you will need to override :meth:`_from_iterable`
1027
with a classmethod that can construct new instances from
1028
an iterable argument.
1031
To override the comparisons (presumably for speed, as the
1032
semantics are fixed), redefine :meth:`__le__` and :meth:`__ge__`,
1033
then the other operations will automatically follow suit.
1036
The :class:`Set` mixin provides a :meth:`_hash` method to compute a hash value
1037
for the set; however, :meth:`__hash__` is not defined because not all sets
1038
are hashable or immutable. To add set hashabilty using mixins,
1039
inherit from both :meth:`Set` and :meth:`Hashable`, then define
1040
``__hash__ = Set._hash``.
1044
* `OrderedSet recipe <http://code.activestate.com/recipes/576694/>`_ for an
1045
example built on :class:`MutableSet`.
1047
* For more about ABCs, see the :mod:`abc` module and :pep:`3119`.
added
removed
python-2.7.11-docs-html/_sources/library/collections.txt
