660 lines
24 KiB
ReStructuredText
660 lines
24 KiB
ReStructuredText
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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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.. versionadded:: 2.4
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This module implements high-performance container datatypes. Currently,
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there are two datatypes, :class:`deque` and :class:`defaultdict`, and
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one datatype factory function, :func:`namedtuple`.
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.. versionchanged:: 2.5
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Added :class:`defaultdict`.
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.. versionchanged:: 2.6
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Added :func:`namedtuple`.
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The specialized containers provided in this module provide alternatives
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to Python's general purpose built-in containers, :class:`dict`,
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:class:`list`, :class:`set`, and :class:`tuple`.
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Besides the containers provided here, the optional :mod:`bsddb`
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module offers the ability to create in-memory or file based ordered
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dictionaries with string keys using the :meth:`bsddb.btopen` method.
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In addition to containers, the collections module provides some ABCs
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(abstract base classes) that can be used to test whether a class
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provides a particular interface, for example, is it hashable or
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a mapping.
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.. versionchanged:: 2.6
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Added abstract base classes.
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ABCs - abstract base classes
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----------------------------
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The collections module offers the following ABCs:
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========================= ==================== ====================== ====================================================
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ABC Inherits Abstract Methods Mixin Methods
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========================= ==================== ====================== ====================================================
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:class:`Container` ``__contains__``
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:class:`Hashable` ``__hash__``
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:class:`Iterable` ``__iter__``
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:class:`Iterator` :class:`Iterable` ``__next__`` ``__iter__``
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:class:`Sized` ``__len__``
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:class:`Mapping` :class:`Sized`, ``__getitem__``, ``__contains__``, ``keys``, ``items``, ``values``,
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:class:`Iterable`, ``__len__``. and ``get``, ``__eq__``, and ``__ne__``
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:class:`Container` ``__iter__``
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:class:`MutableMapping` :class:`Mapping` ``__getitem__`` Inherited Mapping methods and
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``__setitem__``, ``pop``, ``popitem``, ``clear``, ``update``,
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``__delitem__``, and ``setdefault``
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``__iter__``, and
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``__len__``
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:class:`Sequence` :class:`Sized`, ``__getitem__`` ``__contains__``. ``__iter__``, ``__reversed__``.
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:class:`Iterable`, and ``__len__`` ``index``, and ``count``
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:class:`Container`
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:class:`MutableSequnce` :class:`Sequence` ``__getitem__`` Inherited Sequence methods and
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``__delitem__``, ``append``, ``reverse``, ``extend``, ``pop``,
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``insert``, ``remove``, and ``__iadd__``
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and ``__len__``
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:class:`Set` :class:`Sized`, ``__len__``, ``__le__``, ``__lt__``, ``__eq__``, ``__ne__``,
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:class:`Iterable`, ``__iter__``, and ``__gt__``, ``__ge__``, ``__and__``, ``__or__``
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:class:`Container` ``__contains__`` ``__sub__``, ``__xor__``, and ``isdisjoint``
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:class:`MutableSet` :class:`Set` ``add`` and Inherited Set methods and
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``discard`` ``clear``, ``pop``, ``remove``, ``__ior__``,
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``__iand__``, ``__ixor__``, and ``__isub__``
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========================= ==================== ====================== ====================================================
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These ABCs allow us to ask classes or instances if they provide
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particular functionality, for example::
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size = None
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if isinstance(myvar, collections.Sized):
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size = len(myvar)
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Several of the ABCs are also useful as mixins that make it easier to develop
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classes supporting container APIs. For example, to write a class supporting
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the full :class:`Set` API, it only necessary to supply the three underlying
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abstract methods: :meth:`__contains__`, :meth:`__iter__`, and :meth:`__len__`.
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The ABC supplies the remaining methods such as :meth:`__and__` and
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:meth:`isdisjoint` ::
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class ListBasedSet(collections.Set):
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''' Alternate set implementation favoring space over speed
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and not requiring the set elements to be hashable. '''
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def __init__(self, iterable):
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self.elements = lst = []
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for value in iterable:
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if value not in lst:
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lst.append(value)
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def __iter__(self):
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return iter(self.elements)
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def __contains__(self, value):
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return value in self.elements
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def __len__(self):
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return len(self.elements)
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s1 = ListBasedSet('abcdef')
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s2 = ListBasedSet('defghi')
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overlap = s1 & s2 # The __and__() method is supported automatically
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Notes on using :class:`Set` and :class:`MutableSet` as a mixin:
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(1)
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Since some set operations create new sets, the default mixin methods need
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a way to create new instances from an iterable. The class constructor is
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assumed to have a signature in the form ``ClassName(iterable)``.
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That assumption is factored-out to a singleinternal classmethod called
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:meth:`_from_iterable` which calls ``cls(iterable)`` to produce a new set.
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If the :class:`Set` mixin is being used in a class with a different
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constructor signature, you will need to override :meth:`from_iterable`
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with a classmethod that can construct new instances from
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an iterable argument.
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(2)
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To override the comparisons (presumably for speed, as the
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semantics are fixed), redefine :meth:`__le__` and
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then the other operations will automatically follow suit.
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(3)
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The :class:`Set` mixin provides a :meth:`_hash` method to compute a hash value
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for the set; however, :meth:`__hash__` is not defined because not all sets
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are hashable or immutable. To add set hashabilty using mixins,
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inherit from both :meth:`Set` and :meth:`Hashable`, then define
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``__hash__ = Set._hash``.
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(For more about ABCs, see the :mod:`abc` module and :pep:`3119`.)
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.. _deque-objects:
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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:: deque.append(x)
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Add *x* to the right side of the deque.
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.. method:: deque.appendleft(x)
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Add *x* to the left side of the deque.
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.. method:: deque.clear()
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Remove all elements from the deque leaving it with length 0.
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.. method:: deque.extend(iterable)
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Extend the right side of the deque by appending elements from the iterable
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argument.
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.. method:: deque.extendleft(iterable)
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Extend the left side of the deque by appending elements from *iterable*. Note,
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the series of left appends results in reversing the order of elements in the
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iterable argument.
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.. method:: deque.pop()
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Remove and return an element from the right side of the deque. If no elements
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are present, raises an :exc:`IndexError`.
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.. method:: deque.popleft()
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Remove and return an element from the left side of the deque. If no elements are
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present, raises an :exc:`IndexError`.
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.. method:: deque.remove(value)
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Removed the first occurrence of *value*. If not found, raises a
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:exc:`ValueError`.
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.. versionadded:: 2.5
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.. method:: deque.rotate(n)
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Rotate the deque *n* steps to the right. If *n* is negative, rotate to the
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left. Rotating one step to the right is equivalent to:
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``d.appendleft(d.pop())``.
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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]``.
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Example::
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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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G
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H
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I
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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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'j'
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>>> d.popleft() # return and remove the leftmost item
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'f'
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>>> list(d) # list the contents of the deque
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['g', 'h', 'i']
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>>> d[0] # peek at leftmost item
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'g'
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>>> d[-1] # peek at rightmost item
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'i'
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>>> list(reversed(d)) # list the contents of a deque in reverse
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['i', 'h', 'g']
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>>> 'h' in d # search the deque
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True
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>>> d.extend('jkl') # add multiple elements at once
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>>> d
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deque(['g', 'h', 'i', 'j', 'k', 'l'])
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>>> d.rotate(1) # right rotation
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>>> d
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deque(['l', 'g', 'h', 'i', 'j', 'k'])
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>>> d.rotate(-1) # left rotation
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>>> d
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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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d.pop()
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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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>>> d
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deque(['c', 'b', 'a'])
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.. _deque-recipes:
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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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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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d.rotate(-n)
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d.popleft()
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d.rotate(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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Multi-pass data reduction algorithms can be succinctly expressed and efficiently
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coded by extracting elements with multiple calls to :meth:`popleft`, applying
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a reduction function, and calling :meth:`append` to add the result back to the
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deque.
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For example, building a balanced binary tree of nested lists entails reducing
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two adjacent nodes into one by grouping them in a list::
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>>> def maketree(iterable):
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... d = deque(iterable)
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... while len(d) > 1:
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... pair = [d.popleft(), d.popleft()]
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... d.append(pair)
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... return list(d)
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...
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>>> print maketree('abcdefgh')
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[[[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]]
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Bounded length deques provide functionality similar to the ``tail`` filter
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in Unix::
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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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.. _defaultdict-objects:
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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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builtin :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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arguments.
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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:: defaultdict.__missing__(key)
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If the :attr:`default_factory` attribute is ``None``, this raises an
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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 to
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provide a default value for the given *key*, this value is inserted in the
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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 :class:`dict`
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class when the requested key is not found; whatever it returns or raises is then
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returned or raised by :meth:`__getitem__`.
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:class:`defaultdict` objects support the following instance variable:
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.. attribute:: defaultdict.default_factory
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This attribute is used by the :meth:`__missing__` method; it is initialized from
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the first argument to the constructor, if present, or to ``None``, if absent.
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.. _defaultdict-examples:
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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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>>> for k, v in s:
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... d[k].append(v)
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...
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>>> d.items()
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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 = {}
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>>> for k, v in s:
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... d.setdefault(k, []).append(v)
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...
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>>> d.items()
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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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languages)::
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>>> s = 'mississippi'
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>>> d = defaultdict(int)
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>>> for k in s:
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... d[k] += 1
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...
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>>> d.items()
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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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zero)::
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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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>>> for k, v in s:
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... d[k].add(v)
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...
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>>> d.items()
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[('blue', set([2, 4])), ('red', set([1, 3]))]
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.. _named-tuple-factory:
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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, fieldnames, [verbose])
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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 accessable 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 fieldnames) 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 *fieldnames* are a single string with each fieldname separated by whitespace
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and/or commas, for example ``'x y'`` or ``'x, y'``. Alternatively, *fieldnames*
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can be a sequence of strings such as ``['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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or *raise*.
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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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Example::
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>>> Point = namedtuple('Point', 'x y', verbose=True)
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class Point(tuple):
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'Point(x, y)'
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__slots__ = ()
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_fields = ('x', 'y')
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def __new__(cls, x, y):
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return tuple.__new__(cls, (x, y))
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@classmethod
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def _make(cls, iterable):
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'Make a new Point object from a sequence or iterable'
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result = tuple.__new__(cls, iterable)
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if len(result) != 2:
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raise TypeError('Expected 2 arguments, got %d' % len(result))
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return result
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def __repr__(self):
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return 'Point(x=%r, y=%r)' % self
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def _asdict(t):
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'Return a new dict which maps field names to their values'
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return {'x': t[0], 'y': t[1]}
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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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if kwds:
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raise ValueError('Got unexpected field names: %r' % kwds.keys())
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return result
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x = property(itemgetter(0))
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y = property(itemgetter(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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33
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>>> x, y = p # unpack like a regular tuple
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>>> x, y
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(11, 22)
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>>> p.x + p.y # fields also accessable by name
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33
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>>> p # readable __repr__ with a name=value style
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Point(x=11, y=22)
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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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import csv
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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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import sqlite3
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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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.. method:: somenamedtuple._make(iterable)
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Class method that makes a new instance from an existing sequence or iterable.
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::
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>>> t = [11, 22]
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>>> Point._make(t)
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Point(x=11, y=22)
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.. method:: somenamedtuple._asdict()
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Return a new dict which maps field names to their corresponding values:
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::
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>>> p._asdict()
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{'x': 11, 'y': 22}
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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 values:
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::
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>>> p = Point(x=11, y=22)
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>>> p._replace(x=33)
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Point(x=33, 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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::
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>>> p._fields # view the field names
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('x', 'y')
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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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function::
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>>> getattr(p, 'x')
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11
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To convert a dictionary to a named tuple, use the double-star-operator [#]_::
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>>> d = {'x': 11, 'y': 22}
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>>> Point(**d)
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Point(x=11, y=22)
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Since a named tuple is a regular Python class, it is easy to add or change
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functionality with a subclass. Here is how to add a calculated field and
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a fixed-width print format::
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>>> class Point(namedtuple('Point', 'x y')):
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... __slots__ = ()
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... @property
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... def hypot(self):
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... return (self.x ** 2 + self.y ** 2) ** 0.5
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... def __str__(self):
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... return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
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>>> for p in Point(3, 4), Point(14, 5/7.):
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... print p
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Point: x= 3.000 y= 4.000 hypot= 5.000
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Point: x=14.000 y= 0.714 hypot=14.018
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The subclass shown above sets ``__slots__`` to an empty tuple. This keeps
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keep memory requirements low by preventing the creation of instance dictionaries.
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Subclassing is not useful for adding new, stored fields. Instead, simply
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create a new named tuple type from the :attr:`_fields` attribute::
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>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
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Default values can be implemented by using :meth:`_replace` to
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customize a prototype instance::
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>>> Account = namedtuple('Account', 'owner balance transaction_count')
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>>> default_account = Account('<owner name>', 0.0, 0)
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>>> johns_account = default_account._replace(owner='John')
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.. rubric:: Footnotes
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.. [#] For information on the double-star-operator see
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:ref:`tut-unpacking-arguments` and :ref:`calls`.
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