1223 lines
46 KiB
ReStructuredText
1223 lines
46 KiB
ReStructuredText
:mod:`collections` --- Container datatypes
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==========================================
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.. module:: collections
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:synopsis: Container 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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**Source code:** :source:`Lib/collections/__init__.py`
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.. testsetup:: *
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from collections import *
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import itertools
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__name__ = '<doctest>'
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--------------
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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
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:class:`deque` list-like container with fast appends and pops on either end
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:class:`ChainMap` dict-like class for creating a single view of multiple mappings
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:class:`Counter` dict subclass for counting hashable objects
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:class:`OrderedDict` dict subclass that remembers the order entries were added
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:class:`defaultdict` dict subclass that calls a factory function to supply missing values
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:class:`UserDict` wrapper around dictionary objects for easier dict subclassing
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:class:`UserList` wrapper around list objects for easier list subclassing
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:class:`UserString` wrapper around string objects for easier string subclassing
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===================== ====================================================================
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.. versionchanged:: 3.3
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Moved :ref:`collections-abstract-base-classes` to the :mod:`collections.abc` module.
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For backwards compatibility, they continue to be visible in this module through
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Python 3.7. Subsequently, they will be removed entirely.
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:class:`ChainMap` objects
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-------------------------
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.. versionadded:: 3.3
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A :class:`ChainMap` class is provided for quickly linking a number of mappings
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so they can be treated as a single unit. It is often much faster than creating
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a new dictionary and running multiple :meth:`~dict.update` calls.
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The class can be used to simulate nested scopes and is useful in templating.
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.. class:: ChainMap(*maps)
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A :class:`ChainMap` groups multiple dicts or other mappings together to
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create a single, updateable view. If no *maps* are specified, a single empty
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dictionary is provided so that a new chain always has at least one mapping.
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The underlying mappings are stored in a list. That list is public and can
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be accessed or updated using the *maps* attribute. There is no other state.
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Lookups search the underlying mappings successively until a key is found. In
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contrast, writes, updates, and deletions only operate on the first mapping.
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A :class:`ChainMap` incorporates the underlying mappings by reference. So, if
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one of the underlying mappings gets updated, those changes will be reflected
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in :class:`ChainMap`.
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All of the usual dictionary methods are supported. In addition, there is a
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*maps* attribute, a method for creating new subcontexts, and a property for
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accessing all but the first mapping:
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.. attribute:: maps
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A user updateable list of mappings. The list is ordered from
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first-searched to last-searched. It is the only stored state and can
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be modified to change which mappings are searched. The list should
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always contain at least one mapping.
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.. method:: new_child(m=None)
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Returns a new :class:`ChainMap` containing a new map followed by
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all of the maps in the current instance. If ``m`` is specified,
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it becomes the new map at the front of the list of mappings; if not
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specified, an empty dict is used, so that a call to ``d.new_child()``
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is equivalent to: ``ChainMap({}, *d.maps)``. This method is used for
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creating subcontexts that can be updated without altering values in any
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of the parent mappings.
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.. versionchanged:: 3.4
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The optional ``m`` parameter was added.
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.. attribute:: parents
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Property returning a new :class:`ChainMap` containing all of the maps in
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the current instance except the first one. This is useful for skipping
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the first map in the search. Use cases are similar to those for the
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:keyword:`nonlocal` keyword used in :term:`nested scopes <nested
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scope>`. The use cases also parallel those for the built-in
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:func:`super` function. A reference to ``d.parents`` is equivalent to:
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``ChainMap(*d.maps[1:])``.
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.. seealso::
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* The `MultiContext class
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<https://github.com/enthought/codetools/blob/4.0.0/codetools/contexts/multi_context.py>`_
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in the Enthought `CodeTools package
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<https://github.com/enthought/codetools>`_ has options to support
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writing to any mapping in the chain.
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* Django's `Context class
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<https://github.com/django/django/blob/master/django/template/context.py>`_
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for templating is a read-only chain of mappings. It also features
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pushing and popping of contexts similar to the
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:meth:`~collections.ChainMap.new_child` method and the
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:attr:`~collections.ChainMap.parents` property.
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* The `Nested Contexts recipe
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<https://code.activestate.com/recipes/577434/>`_ has options to control
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whether writes and other mutations apply only to the first mapping or to
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any mapping in the chain.
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* A `greatly simplified read-only version of Chainmap
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<https://code.activestate.com/recipes/305268/>`_.
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:class:`ChainMap` Examples and Recipes
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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This section shows various approaches to working with chained maps.
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Example of simulating Python's internal lookup chain::
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import builtins
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pylookup = ChainMap(locals(), globals(), vars(builtins))
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Example of letting user specified command-line arguments take precedence over
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environment variables which in turn take precedence over default values::
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import os, argparse
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defaults = {'color': 'red', 'user': 'guest'}
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parser = argparse.ArgumentParser()
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parser.add_argument('-u', '--user')
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parser.add_argument('-c', '--color')
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namespace = parser.parse_args()
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command_line_args = {k:v for k, v in vars(namespace).items() if v}
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combined = ChainMap(command_line_args, os.environ, defaults)
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print(combined['color'])
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print(combined['user'])
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Example patterns for using the :class:`ChainMap` class to simulate nested
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contexts::
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c = ChainMap() # Create root context
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d = c.new_child() # Create nested child context
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e = c.new_child() # Child of c, independent from d
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e.maps[0] # Current context dictionary -- like Python's locals()
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e.maps[-1] # Root context -- like Python's globals()
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e.parents # Enclosing context chain -- like Python's nonlocals
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d['x'] # Get first key in the chain of contexts
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d['x'] = 1 # Set value in current context
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del d['x'] # Delete from current context
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list(d) # All nested values
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k in d # Check all nested values
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len(d) # Number of nested values
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d.items() # All nested items
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dict(d) # Flatten into a regular dictionary
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The :class:`ChainMap` class only makes updates (writes and deletions) to the
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first mapping in the chain while lookups will search the full chain. However,
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if deep writes and deletions are desired, it is easy to make a subclass that
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updates keys found deeper in the chain::
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class DeepChainMap(ChainMap):
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'Variant of ChainMap that allows direct updates to inner scopes'
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def __setitem__(self, key, value):
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for mapping in self.maps:
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if key in mapping:
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mapping[key] = value
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return
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self.maps[0][key] = value
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def __delitem__(self, key):
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for mapping in self.maps:
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if key in mapping:
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del mapping[key]
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return
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raise KeyError(key)
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>>> d = DeepChainMap({'zebra': 'black'}, {'elephant': 'blue'}, {'lion': 'yellow'})
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>>> d['lion'] = 'orange' # update an existing key two levels down
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>>> d['snake'] = 'red' # new keys get added to the topmost dict
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>>> del d['elephant'] # remove an existing key one level down
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DeepChainMap({'zebra': 'black', 'snake': 'red'}, {}, {'lion': 'orange'})
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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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For example::
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>>> # Tally occurrences of words in a list
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>>> cnt = Counter()
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>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
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... cnt[word] += 1
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>>> cnt
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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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>>> import re
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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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0
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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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.. versionadded:: 3.1
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Counter objects support three methods beyond those available for all
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dictionaries:
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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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>>> sorted(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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:meth:`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) # doctest: +SKIP
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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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>>> c.subtract(d)
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>>> c
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Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
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.. versionadded:: 3.2
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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 # 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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Counter({'a': 2})
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>>> c & d # intersection: min(c[x], d[x]) # doctest: +SKIP
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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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Unary addition and subtraction are shortcuts for adding an empty counter
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or subtracting from an empty counter.
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>>> c = Counter(a=2, b=-4)
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>>> +c
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Counter({'a': 2})
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>>> -c
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Counter({'b': 4})
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.. versionadded:: 3.3
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Added support for unary plus, unary minus, and in-place multiset operations.
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.. note::
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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:`~Counter.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:`~Counter.update` and :meth:`~Counter.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:`~Counter.elements` method requires integer counts. It ignores zero and
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negative counts.
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.. seealso::
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* `Bag class <https://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html>`_
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in Smalltalk.
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* Wikipedia entry for `Multisets <https://en.wikipedia.org/wiki/Multiset>`_.
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* `C++ multisets <http://www.java2s.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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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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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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.. method:: clear()
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Remove all elements from the deque leaving it with length 0.
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.. method:: copy()
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Create a shallow copy of the deque.
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.. versionadded:: 3.5
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.. method:: count(x)
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Count the number of deque elements equal to *x*.
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.. versionadded:: 3.2
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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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argument.
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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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.. method:: index(x[, start[, stop]])
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Return the position of *x* in the deque (at or after index *start*
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and before index *stop*). Returns the first match or raises
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:exc:`ValueError` if not found.
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.. versionadded:: 3.5
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.. method:: insert(i, x)
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Insert *x* into the deque at position *i*.
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If the insertion would cause a bounded deque to grow beyond *maxlen*,
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an :exc:`IndexError` is raised.
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.. versionadded:: 3.5
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.. method:: pop()
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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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Remove the first occurrence of *value*. If not found, raises a
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:exc:`ValueError`.
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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:: 3.2
|
|
|
|
|
|
.. method:: rotate(n=1)
|
|
|
|
Rotate the deque *n* steps to the right. If *n* is negative, rotate
|
|
to the left.
|
|
|
|
When the deque is not empty, rotating one step to the right is equivalent
|
|
to ``d.appendleft(d.pop())``, and rotating one step to the left is
|
|
equivalent to ``d.append(d.popleft())``.
|
|
|
|
|
|
Deque objects also provide one read-only attribute:
|
|
|
|
.. attribute:: maxlen
|
|
|
|
Maximum size of a deque or ``None`` if unbounded.
|
|
|
|
.. versionadded:: 3.1
|
|
|
|
|
|
In addition to the above, deques support iteration, pickling, ``len(d)``,
|
|
``reversed(d)``, ``copy.copy(d)``, ``copy.deepcopy(d)``, membership testing with
|
|
the :keyword:`in` operator, and subscript references such as ``d[-1]``. Indexed
|
|
access is O(1) at both ends but slows to O(n) in the middle. For fast random
|
|
access, use lists instead.
|
|
|
|
Starting in version 3.5, deques support ``__add__()``, ``__mul__()``,
|
|
and ``__imul__()``.
|
|
|
|
Example:
|
|
|
|
.. doctest::
|
|
|
|
>>> from collections import deque
|
|
>>> d = deque('ghi') # make a new deque with three items
|
|
>>> for elem in d: # iterate over the deque's elements
|
|
... print(elem.upper())
|
|
G
|
|
H
|
|
I
|
|
|
|
>>> d.append('j') # add a new entry to the right side
|
|
>>> d.appendleft('f') # add a new entry to the left side
|
|
>>> d # show the representation of the deque
|
|
deque(['f', 'g', 'h', 'i', 'j'])
|
|
|
|
>>> d.pop() # return and remove the rightmost item
|
|
'j'
|
|
>>> d.popleft() # return and remove the leftmost item
|
|
'f'
|
|
>>> list(d) # list the contents of the deque
|
|
['g', 'h', 'i']
|
|
>>> d[0] # peek at leftmost item
|
|
'g'
|
|
>>> d[-1] # peek at rightmost item
|
|
'i'
|
|
|
|
>>> list(reversed(d)) # list the contents of a deque in reverse
|
|
['i', 'h', 'g']
|
|
>>> 'h' in d # search the deque
|
|
True
|
|
>>> d.extend('jkl') # add multiple elements at once
|
|
>>> d
|
|
deque(['g', 'h', 'i', 'j', 'k', 'l'])
|
|
>>> d.rotate(1) # right rotation
|
|
>>> d
|
|
deque(['l', 'g', 'h', 'i', 'j', 'k'])
|
|
>>> d.rotate(-1) # left rotation
|
|
>>> d
|
|
deque(['g', 'h', 'i', 'j', 'k', 'l'])
|
|
|
|
>>> deque(reversed(d)) # make a new deque in reverse order
|
|
deque(['l', 'k', 'j', 'i', 'h', 'g'])
|
|
>>> d.clear() # empty the deque
|
|
>>> d.pop() # cannot pop from an empty deque
|
|
Traceback (most recent call last):
|
|
File "<pyshell#6>", line 1, in -toplevel-
|
|
d.pop()
|
|
IndexError: pop from an empty deque
|
|
|
|
>>> d.extendleft('abc') # extendleft() reverses the input order
|
|
>>> d
|
|
deque(['c', 'b', 'a'])
|
|
|
|
|
|
:class:`deque` Recipes
|
|
^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
This section shows various approaches to working with deques.
|
|
|
|
Bounded length deques provide functionality similar to the ``tail`` filter
|
|
in Unix::
|
|
|
|
def tail(filename, n=10):
|
|
'Return the last n lines of a file'
|
|
with open(filename) as f:
|
|
return deque(f, n)
|
|
|
|
Another approach to using deques is to maintain a sequence of recently
|
|
added elements by appending to the right and popping to the left::
|
|
|
|
def moving_average(iterable, n=3):
|
|
# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
|
|
# http://en.wikipedia.org/wiki/Moving_average
|
|
it = iter(iterable)
|
|
d = deque(itertools.islice(it, n-1))
|
|
d.appendleft(0)
|
|
s = sum(d)
|
|
for elem in it:
|
|
s += elem - d.popleft()
|
|
d.append(elem)
|
|
yield s / n
|
|
|
|
A `round-robin scheduler
|
|
<https://en.wikipedia.org/wiki/Round-robin_scheduling>`_ can be implemented with
|
|
input iterators stored in a :class:`deque`. Values are yielded from the active
|
|
iterator in position zero. If that iterator is exhausted, it can be removed
|
|
with :meth:`~deque.popleft`; otherwise, it can be cycled back to the end with
|
|
the :meth:`~deque.rotate` method::
|
|
|
|
def roundrobin(*iterables):
|
|
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
|
|
iterators = deque(map(iter, iterables))
|
|
while iterators:
|
|
try:
|
|
while True:
|
|
yield next(iterators[0])
|
|
iterators.rotate(-1)
|
|
except StopIteration:
|
|
# Remove an exhausted iterator.
|
|
iterators.popleft()
|
|
|
|
The :meth:`~deque.rotate` method provides a way to implement :class:`deque` slicing and
|
|
deletion. For example, a pure Python implementation of ``del d[n]`` relies on
|
|
the ``rotate()`` method to position elements to be popped::
|
|
|
|
def delete_nth(d, n):
|
|
d.rotate(-n)
|
|
d.popleft()
|
|
d.rotate(n)
|
|
|
|
To implement :class:`deque` slicing, use a similar approach applying
|
|
:meth:`~deque.rotate` to bring a target element to the left side of the deque. Remove
|
|
old entries with :meth:`~deque.popleft`, add new entries with :meth:`~deque.extend`, and then
|
|
reverse the rotation.
|
|
With minor variations on that approach, it is easy to implement Forth style
|
|
stack manipulations such as ``dup``, ``drop``, ``swap``, ``over``, ``pick``,
|
|
``rot``, and ``roll``.
|
|
|
|
|
|
:class:`defaultdict` objects
|
|
----------------------------
|
|
|
|
.. class:: defaultdict([default_factory[, ...]])
|
|
|
|
Returns a new dictionary-like object. :class:`defaultdict` is a subclass of the
|
|
built-in :class:`dict` class. It overrides one method and adds one writable
|
|
instance variable. The remaining functionality is the same as for the
|
|
:class:`dict` class and is not documented here.
|
|
|
|
The first argument provides the initial value for the :attr:`default_factory`
|
|
attribute; it defaults to ``None``. All remaining arguments are treated the same
|
|
as if they were passed to the :class:`dict` constructor, including keyword
|
|
arguments.
|
|
|
|
|
|
:class:`defaultdict` objects support the following method in addition to the
|
|
standard :class:`dict` operations:
|
|
|
|
.. method:: __missing__(key)
|
|
|
|
If the :attr:`default_factory` attribute is ``None``, this raises a
|
|
:exc:`KeyError` exception with the *key* as argument.
|
|
|
|
If :attr:`default_factory` is not ``None``, it is called without arguments
|
|
to provide a default value for the given *key*, this value is inserted in
|
|
the dictionary for the *key*, and returned.
|
|
|
|
If calling :attr:`default_factory` raises an exception this exception is
|
|
propagated unchanged.
|
|
|
|
This method is called by the :meth:`__getitem__` method of the
|
|
:class:`dict` class when the requested key is not found; whatever it
|
|
returns or raises is then returned or raised by :meth:`__getitem__`.
|
|
|
|
Note that :meth:`__missing__` is *not* called for any operations besides
|
|
:meth:`__getitem__`. This means that :meth:`get` will, like normal
|
|
dictionaries, return ``None`` as a default rather than using
|
|
:attr:`default_factory`.
|
|
|
|
|
|
:class:`defaultdict` objects support the following instance variable:
|
|
|
|
|
|
.. attribute:: default_factory
|
|
|
|
This attribute is used by the :meth:`__missing__` method; it is
|
|
initialized from the first argument to the constructor, if present, or to
|
|
``None``, if absent.
|
|
|
|
|
|
:class:`defaultdict` Examples
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
Using :class:`list` as the :attr:`~defaultdict.default_factory`, it is easy to group a
|
|
sequence of key-value pairs into a dictionary of lists:
|
|
|
|
>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
|
|
>>> d = defaultdict(list)
|
|
>>> for k, v in s:
|
|
... d[k].append(v)
|
|
...
|
|
>>> sorted(d.items())
|
|
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
|
|
|
|
When each key is encountered for the first time, it is not already in the
|
|
mapping; so an entry is automatically created using the :attr:`~defaultdict.default_factory`
|
|
function which returns an empty :class:`list`. The :meth:`list.append`
|
|
operation then attaches the value to the new list. When keys are encountered
|
|
again, the look-up proceeds normally (returning the list for that key) and the
|
|
:meth:`list.append` operation adds another value to the list. This technique is
|
|
simpler and faster than an equivalent technique using :meth:`dict.setdefault`:
|
|
|
|
>>> d = {}
|
|
>>> for k, v in s:
|
|
... d.setdefault(k, []).append(v)
|
|
...
|
|
>>> sorted(d.items())
|
|
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
|
|
|
|
Setting the :attr:`~defaultdict.default_factory` to :class:`int` makes the
|
|
:class:`defaultdict` useful for counting (like a bag or multiset in other
|
|
languages):
|
|
|
|
>>> s = 'mississippi'
|
|
>>> d = defaultdict(int)
|
|
>>> for k in s:
|
|
... d[k] += 1
|
|
...
|
|
>>> sorted(d.items())
|
|
[('i', 4), ('m', 1), ('p', 2), ('s', 4)]
|
|
|
|
When a letter is first encountered, it is missing from the mapping, so the
|
|
:attr:`~defaultdict.default_factory` function calls :func:`int` to supply a default count of
|
|
zero. The increment operation then builds up the count for each letter.
|
|
|
|
The function :func:`int` which always returns zero is just a special case of
|
|
constant functions. A faster and more flexible way to create constant functions
|
|
is to use a lambda function which can supply any constant value (not just
|
|
zero):
|
|
|
|
>>> def constant_factory(value):
|
|
... return lambda: value
|
|
>>> d = defaultdict(constant_factory('<missing>'))
|
|
>>> d.update(name='John', action='ran')
|
|
>>> '%(name)s %(action)s to %(object)s' % d
|
|
'John ran to <missing>'
|
|
|
|
Setting the :attr:`~defaultdict.default_factory` to :class:`set` makes the
|
|
:class:`defaultdict` useful for building a dictionary of sets:
|
|
|
|
>>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
|
|
>>> d = defaultdict(set)
|
|
>>> for k, v in s:
|
|
... d[k].add(v)
|
|
...
|
|
>>> sorted(d.items())
|
|
[('blue', {2, 4}), ('red', {1, 3})]
|
|
|
|
|
|
:func:`namedtuple` Factory Function for Tuples with Named Fields
|
|
----------------------------------------------------------------
|
|
|
|
Named tuples assign meaning to each position in a tuple and allow for more readable,
|
|
self-documenting code. They can be used wherever regular tuples are used, and
|
|
they add the ability to access fields by name instead of position index.
|
|
|
|
.. function:: namedtuple(typename, field_names, *, rename=False, defaults=None, module=None)
|
|
|
|
Returns a new tuple subclass named *typename*. The new subclass is used to
|
|
create tuple-like objects that have fields accessible by attribute lookup as
|
|
well as being indexable and iterable. Instances of the subclass also have a
|
|
helpful docstring (with typename and field_names) and a helpful :meth:`__repr__`
|
|
method which lists the tuple contents in a ``name=value`` format.
|
|
|
|
The *field_names* are a sequence of strings such as ``['x', 'y']``.
|
|
Alternatively, *field_names* can be a single string with each fieldname
|
|
separated by whitespace and/or commas, for example ``'x y'`` or ``'x, y'``.
|
|
|
|
Any valid Python identifier may be used for a fieldname except for names
|
|
starting with an underscore. Valid identifiers consist of letters, digits,
|
|
and underscores but do not start with a digit or underscore and cannot be
|
|
a :mod:`keyword` such as *class*, *for*, *return*, *global*, *pass*,
|
|
or *raise*.
|
|
|
|
If *rename* is true, invalid fieldnames are automatically replaced
|
|
with positional names. For example, ``['abc', 'def', 'ghi', 'abc']`` is
|
|
converted to ``['abc', '_1', 'ghi', '_3']``, eliminating the keyword
|
|
``def`` and the duplicate fieldname ``abc``.
|
|
|
|
*defaults* can be ``None`` or an :term:`iterable` of default values.
|
|
Since fields with a default value must come after any fields without a
|
|
default, the *defaults* are applied to the rightmost parameters. For
|
|
example, if the fieldnames are ``['x', 'y', 'z']`` and the defaults are
|
|
``(1, 2)``, then ``x`` will be a required argument, ``y`` will default to
|
|
``1``, and ``z`` will default to ``2``.
|
|
|
|
If *module* is defined, the ``__module__`` attribute of the named tuple is
|
|
set to that value.
|
|
|
|
Named tuple instances do not have per-instance dictionaries, so they are
|
|
lightweight and require no more memory than regular tuples.
|
|
|
|
.. versionchanged:: 3.1
|
|
Added support for *rename*.
|
|
|
|
.. versionchanged:: 3.6
|
|
The *verbose* and *rename* parameters became
|
|
:ref:`keyword-only arguments <keyword-only_parameter>`.
|
|
|
|
.. versionchanged:: 3.6
|
|
Added the *module* parameter.
|
|
|
|
.. versionchanged:: 3.7
|
|
Remove the *verbose* parameter and the :attr:`_source` attribute.
|
|
|
|
.. versionchanged:: 3.7
|
|
Added the *defaults* parameter and the :attr:`_field_defaults`
|
|
attribute.
|
|
|
|
.. doctest::
|
|
:options: +NORMALIZE_WHITESPACE
|
|
|
|
>>> # Basic example
|
|
>>> Point = namedtuple('Point', ['x', 'y'])
|
|
>>> p = Point(11, y=22) # instantiate with positional or keyword arguments
|
|
>>> p[0] + p[1] # indexable like the plain tuple (11, 22)
|
|
33
|
|
>>> x, y = p # unpack like a regular tuple
|
|
>>> x, y
|
|
(11, 22)
|
|
>>> p.x + p.y # fields also accessible by name
|
|
33
|
|
>>> p # readable __repr__ with a name=value style
|
|
Point(x=11, y=22)
|
|
|
|
Named tuples are especially useful for assigning field names to result tuples returned
|
|
by the :mod:`csv` or :mod:`sqlite3` modules::
|
|
|
|
EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
|
|
|
|
import csv
|
|
for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
|
|
print(emp.name, emp.title)
|
|
|
|
import sqlite3
|
|
conn = sqlite3.connect('/companydata')
|
|
cursor = conn.cursor()
|
|
cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
|
|
for emp in map(EmployeeRecord._make, cursor.fetchall()):
|
|
print(emp.name, emp.title)
|
|
|
|
In addition to the methods inherited from tuples, named tuples support
|
|
three additional methods and two attributes. To prevent conflicts with
|
|
field names, the method and attribute names start with an underscore.
|
|
|
|
.. classmethod:: somenamedtuple._make(iterable)
|
|
|
|
Class method that makes a new instance from an existing sequence or iterable.
|
|
|
|
.. doctest::
|
|
|
|
>>> t = [11, 22]
|
|
>>> Point._make(t)
|
|
Point(x=11, y=22)
|
|
|
|
.. method:: somenamedtuple._asdict()
|
|
|
|
Return a new :class:`OrderedDict` which maps field names to their corresponding
|
|
values:
|
|
|
|
.. doctest::
|
|
|
|
>>> p = Point(x=11, y=22)
|
|
>>> p._asdict()
|
|
OrderedDict([('x', 11), ('y', 22)])
|
|
|
|
.. versionchanged:: 3.1
|
|
Returns an :class:`OrderedDict` instead of a regular :class:`dict`.
|
|
|
|
.. method:: somenamedtuple._replace(**kwargs)
|
|
|
|
Return a new instance of the named tuple replacing specified fields with new
|
|
values::
|
|
|
|
>>> p = Point(x=11, y=22)
|
|
>>> p._replace(x=33)
|
|
Point(x=33, y=22)
|
|
|
|
>>> for partnum, record in inventory.items():
|
|
... inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())
|
|
|
|
.. attribute:: somenamedtuple._fields
|
|
|
|
Tuple of strings listing the field names. Useful for introspection
|
|
and for creating new named tuple types from existing named tuples.
|
|
|
|
.. doctest::
|
|
|
|
>>> p._fields # view the field names
|
|
('x', 'y')
|
|
|
|
>>> Color = namedtuple('Color', 'red green blue')
|
|
>>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)
|
|
>>> Pixel(11, 22, 128, 255, 0)
|
|
Pixel(x=11, y=22, red=128, green=255, blue=0)
|
|
|
|
.. attribute:: somenamedtuple._fields_defaults
|
|
|
|
Dictionary mapping field names to default values.
|
|
|
|
.. doctest::
|
|
|
|
>>> Account = namedtuple('Account', ['type', 'balance'], defaults=[0])
|
|
>>> Account._fields_defaults
|
|
{'balance': 0}
|
|
>>> Account('premium')
|
|
Account(type='premium', balance=0)
|
|
|
|
To retrieve a field whose name is stored in a string, use the :func:`getattr`
|
|
function:
|
|
|
|
>>> getattr(p, 'x')
|
|
11
|
|
|
|
To convert a dictionary to a named tuple, use the double-star-operator
|
|
(as described in :ref:`tut-unpacking-arguments`):
|
|
|
|
>>> d = {'x': 11, 'y': 22}
|
|
>>> Point(**d)
|
|
Point(x=11, y=22)
|
|
|
|
Since a named tuple is a regular Python class, it is easy to add or change
|
|
functionality with a subclass. Here is how to add a calculated field and
|
|
a fixed-width print format:
|
|
|
|
.. doctest::
|
|
|
|
>>> class Point(namedtuple('Point', ['x', 'y'])):
|
|
... __slots__ = ()
|
|
... @property
|
|
... def hypot(self):
|
|
... return (self.x ** 2 + self.y ** 2) ** 0.5
|
|
... def __str__(self):
|
|
... return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
|
|
|
|
>>> for p in Point(3, 4), Point(14, 5/7):
|
|
... print(p)
|
|
Point: x= 3.000 y= 4.000 hypot= 5.000
|
|
Point: x=14.000 y= 0.714 hypot=14.018
|
|
|
|
The subclass shown above sets ``__slots__`` to an empty tuple. This helps
|
|
keep memory requirements low by preventing the creation of instance dictionaries.
|
|
|
|
Subclassing is not useful for adding new, stored fields. Instead, simply
|
|
create a new named tuple type from the :attr:`~somenamedtuple._fields` attribute:
|
|
|
|
>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
|
|
|
|
Docstrings can be customized by making direct assignments to the ``__doc__``
|
|
fields:
|
|
|
|
>>> Book = namedtuple('Book', ['id', 'title', 'authors'])
|
|
>>> Book.__doc__ += ': Hardcover book in active collection'
|
|
>>> Book.id.__doc__ = '13-digit ISBN'
|
|
>>> Book.title.__doc__ = 'Title of first printing'
|
|
>>> Book.authors.__doc__ = 'List of authors sorted by last name'
|
|
|
|
.. versionchanged:: 3.5
|
|
Property docstrings became writeable.
|
|
|
|
Default values can be implemented by using :meth:`~somenamedtuple._replace` to
|
|
customize a prototype instance:
|
|
|
|
>>> Account = namedtuple('Account', 'owner balance transaction_count')
|
|
>>> default_account = Account('<owner name>', 0.0, 0)
|
|
>>> johns_account = default_account._replace(owner='John')
|
|
>>> janes_account = default_account._replace(owner='Jane')
|
|
|
|
|
|
.. seealso::
|
|
|
|
* `Recipe for named tuple abstract base class with a metaclass mix-in
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<https://code.activestate.com/recipes/577629-namedtupleabc-abstract-base-class-mix-in-for-named/>`_
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by Jan Kaliszewski. Besides providing an :term:`abstract base class` for
|
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named tuples, it also supports an alternate :term:`metaclass`-based
|
|
constructor that is convenient for use cases where named tuples are being
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subclassed.
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* See :meth:`types.SimpleNamespace` for a mutable namespace based on an
|
|
underlying dictionary instead of a tuple.
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|
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* See :meth:`typing.NamedTuple` for a way to add type hints for named tuples.
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|
:class:`OrderedDict` objects
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|
----------------------------
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|
|
|
Ordered dictionaries are just like regular dictionaries but they remember the
|
|
order that items were inserted. When iterating over an ordered dictionary,
|
|
the items are returned in the order their keys were first added.
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|
|
|
.. class:: OrderedDict([items])
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|
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|
Return an instance of a dict subclass, supporting the usual :class:`dict`
|
|
methods. An *OrderedDict* is a dict that remembers the order that keys
|
|
were first inserted. If a new entry overwrites an existing entry, the
|
|
original insertion position is left unchanged. Deleting an entry and
|
|
reinserting it will move it to the end.
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|
|
|
.. versionadded:: 3.1
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|
|
|
.. method:: popitem(last=True)
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|
|
|
The :meth:`popitem` method for ordered dictionaries returns and removes a
|
|
(key, value) pair. The pairs are returned in
|
|
:abbr:`LIFO (last-in, first-out)` order if *last* is true
|
|
or :abbr:`FIFO (first-in, first-out)` order if false.
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|
|
|
.. method:: move_to_end(key, last=True)
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|
Move an existing *key* to either end of an ordered dictionary. The item
|
|
is moved to the right end if *last* is true (the default) or to the
|
|
beginning if *last* is false. Raises :exc:`KeyError` if the *key* does
|
|
not exist::
|
|
|
|
>>> d = OrderedDict.fromkeys('abcde')
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|
>>> d.move_to_end('b')
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|
>>> ''.join(d.keys())
|
|
'acdeb'
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|
>>> d.move_to_end('b', last=False)
|
|
>>> ''.join(d.keys())
|
|
'bacde'
|
|
|
|
.. versionadded:: 3.2
|
|
|
|
In addition to the usual mapping methods, ordered dictionaries also support
|
|
reverse iteration using :func:`reversed`.
|
|
|
|
Equality tests between :class:`OrderedDict` objects are order-sensitive
|
|
and are implemented as ``list(od1.items())==list(od2.items())``.
|
|
Equality tests between :class:`OrderedDict` objects and other
|
|
:class:`~collections.abc.Mapping` objects are order-insensitive like regular
|
|
dictionaries. This allows :class:`OrderedDict` objects to be substituted
|
|
anywhere a regular dictionary is used.
|
|
|
|
.. versionchanged:: 3.5
|
|
The items, keys, and values :term:`views <dictionary view>`
|
|
of :class:`OrderedDict` now support reverse iteration using :func:`reversed`.
|
|
|
|
.. versionchanged:: 3.6
|
|
With the acceptance of :pep:`468`, order is retained for keyword arguments
|
|
passed to the :class:`OrderedDict` constructor and its :meth:`update`
|
|
method.
|
|
|
|
:class:`OrderedDict` Examples and Recipes
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
Since an ordered dictionary remembers its insertion order, it can be used
|
|
in conjunction with sorting to make a sorted dictionary::
|
|
|
|
>>> # regular unsorted dictionary
|
|
>>> d = {'banana': 3, 'apple': 4, 'pear': 1, 'orange': 2}
|
|
|
|
>>> # dictionary sorted by key
|
|
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
|
|
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])
|
|
|
|
>>> # dictionary sorted by value
|
|
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
|
|
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])
|
|
|
|
>>> # dictionary sorted by length of the key string
|
|
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
|
|
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])
|
|
|
|
The new sorted dictionaries maintain their sort order when entries
|
|
are deleted. But when new keys are added, the keys are appended
|
|
to the end and the sort is not maintained.
|
|
|
|
It is also straight-forward to create an ordered dictionary variant
|
|
that remembers the order the keys were *last* inserted.
|
|
If a new entry overwrites an existing entry, the
|
|
original insertion position is changed and moved to the end::
|
|
|
|
class LastUpdatedOrderedDict(OrderedDict):
|
|
'Store items in the order the keys were last added'
|
|
|
|
def __setitem__(self, key, value):
|
|
if key in self:
|
|
del self[key]
|
|
OrderedDict.__setitem__(self, key, value)
|
|
|
|
An ordered dictionary can be combined with the :class:`Counter` class
|
|
so that the counter remembers the order elements are first encountered::
|
|
|
|
class OrderedCounter(Counter, OrderedDict):
|
|
'Counter that remembers the order elements are first encountered'
|
|
|
|
def __repr__(self):
|
|
return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
|
|
|
|
def __reduce__(self):
|
|
return self.__class__, (OrderedDict(self),)
|
|
|
|
|
|
:class:`UserDict` objects
|
|
-------------------------
|
|
|
|
The class, :class:`UserDict` acts as a wrapper around dictionary objects.
|
|
The need for this class has been partially supplanted by the ability to
|
|
subclass directly from :class:`dict`; however, this class can be easier
|
|
to work with because the underlying dictionary is accessible as an
|
|
attribute.
|
|
|
|
.. class:: UserDict([initialdata])
|
|
|
|
Class that simulates a dictionary. The instance's contents are kept in a
|
|
regular dictionary, which is accessible via the :attr:`data` attribute of
|
|
:class:`UserDict` instances. If *initialdata* is provided, :attr:`data` is
|
|
initialized with its contents; note that a reference to *initialdata* will not
|
|
be kept, allowing it be used for other purposes.
|
|
|
|
In addition to supporting the methods and operations of mappings,
|
|
:class:`UserDict` instances provide the following attribute:
|
|
|
|
.. attribute:: data
|
|
|
|
A real dictionary used to store the contents of the :class:`UserDict`
|
|
class.
|
|
|
|
|
|
|
|
:class:`UserList` objects
|
|
-------------------------
|
|
|
|
This class acts as a wrapper around list objects. It is a useful base class
|
|
for your own list-like classes which can inherit from them and override
|
|
existing methods or add new ones. In this way, one can add new behaviors to
|
|
lists.
|
|
|
|
The need for this class has been partially supplanted by the ability to
|
|
subclass directly from :class:`list`; however, this class can be easier
|
|
to work with because the underlying list is accessible as an attribute.
|
|
|
|
.. class:: UserList([list])
|
|
|
|
Class that simulates a list. The instance's contents are kept in a regular
|
|
list, which is accessible via the :attr:`data` attribute of :class:`UserList`
|
|
instances. The instance's contents are initially set to a copy of *list*,
|
|
defaulting to the empty list ``[]``. *list* can be any iterable, for
|
|
example a real Python list or a :class:`UserList` object.
|
|
|
|
In addition to supporting the methods and operations of mutable sequences,
|
|
:class:`UserList` instances provide the following attribute:
|
|
|
|
.. attribute:: data
|
|
|
|
A real :class:`list` object used to store the contents of the
|
|
:class:`UserList` class.
|
|
|
|
**Subclassing requirements:** Subclasses of :class:`UserList` are expected to
|
|
offer a constructor which can be called with either no arguments or one
|
|
argument. List operations which return a new sequence attempt to create an
|
|
instance of the actual implementation class. To do so, it assumes that the
|
|
constructor can be called with a single parameter, which is a sequence object
|
|
used as a data source.
|
|
|
|
If a derived class does not wish to comply with this requirement, all of the
|
|
special methods supported by this class will need to be overridden; please
|
|
consult the sources for information about the methods which need to be provided
|
|
in that case.
|
|
|
|
:class:`UserString` objects
|
|
---------------------------
|
|
|
|
The class, :class:`UserString` acts as a wrapper around string objects.
|
|
The need for this class has been partially supplanted by the ability to
|
|
subclass directly from :class:`str`; however, this class can be easier
|
|
to work with because the underlying string is accessible as an
|
|
attribute.
|
|
|
|
.. class:: UserString(seq)
|
|
|
|
Class that simulates a string object. The instance's
|
|
content is kept in a regular string object, which is accessible via the
|
|
:attr:`data` attribute of :class:`UserString` instances. The instance's
|
|
contents are initially set to a copy of *seq*. The *seq* argument can
|
|
be any object which can be converted into a string using the built-in
|
|
:func:`str` function.
|
|
|
|
In addition to supporting the methods and operations of strings,
|
|
:class:`UserString` instances provide the following attribute:
|
|
|
|
.. attribute:: data
|
|
|
|
A real :class:`str` object used to store the contents of the
|
|
:class:`UserString` class.
|
|
|
|
.. versionchanged:: 3.5
|
|
New methods ``__getnewargs__``, ``__rmod__``, ``casefold``,
|
|
``format_map``, ``isprintable``, and ``maketrans``.
|