sandbox/ncoghlan / Doc / library / collections.rst

:mod:`collections` --- Container datatypes

Source code: :source:`Lib/collections/`

This module implements specialized container datatypes providing alternatives to Python's general purpose built-in containers, :class:`dict`, :class:`list`, :class:`set`, and :class:`tuple`.

:func:`namedtuple` factory function for creating tuple subclasses with named fields
:class:`deque` list-like container with fast appends and pops on either end
:class:`ChainMap` dict-like class for creating a single view of multiple mappings
:class:`Counter` dict subclass for counting hashable objects
:class:`OrderedDict` dict subclass that remembers the order entries were added
:class:`defaultdict` dict subclass that calls a factory function to supply missing values
:class:`UserDict` wrapper around dictionary objects for easier dict subclassing
:class:`UserList` wrapper around list objects for easier list subclassing
:class:`UserString` wrapper around string objects for easier string subclassing

:class:`ChainMap` objects

A :class:`ChainMap` class is provided for quickly linking a number of mappings so they can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple :meth:`~dict.update` calls.

The class can be used to simulate nested scopes and is useful in templating.

A :class:`ChainMap` groups multiple dicts or other mappings together to create a single, updateable view. If no maps are specified, a single empty dictionary is provided so that a new chain always has at least one mapping.

The underlying mappings are stored in a list. That list is public and can accessed or updated using the maps attribute. There is no other state.

Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping.

A :class:`ChainMap` incorporates the underlying mappings by reference. So, if one of the underlying mappings gets updated, those changes will be reflected in :class:`ChainMap`.

All of the usual dictionary methods are supported. In addition, there is a maps attribute, a method for creating new subcontexts, and a property for accessing all but the first mapping:

Example of simulating Python's internal lookup chain:

import builtins
pylookup = ChainMap(locals(), globals(), vars(builtins))

Example of letting user specified values take precedence over environment variables which in turn take precedence over default values:

import os, argparse
defaults = {'color': 'red', 'user': guest}
parser = argparse.ArgumentParser()
parser.add_argument('-u', '--user')
parser.add_argument('-c', '--color')
user_specified = vars(parser.parse_args())
combined = ChainMap(user_specified, os.environ, defaults)

Example patterns for using the :class:`ChainMap` class to simulate nested contexts:

c = ChainMap()          Create root context
d = c.new_child()       Create nested child context
e = c.new_child()       Child of c, independent from d
e.maps[0]               Current context dictionary -- like Python's locals()
e.maps[-1]              Root context -- like Python's globals()
e.parents               Enclosing context chain -- like Python's nonlocals

d['x']                  Get first key in the chain of contexts
d['x'] = 1              Set value in current context
del['x']                Delete from current context
list(d)                 All nested values
k in d                  Check all nested values
len(d)                  Number of nested values
d.items()               All nested items
dict(d)                 Flatten into a regular dictionary

:class:`Counter` objects

A counter tool is provided to support convenient and rapid tallies. For example:

>>> # Tally occurrences of words in a list
>>> cnt = Counter()
>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
...     cnt[word] += 1
>>> cnt
Counter({'blue': 3, 'red': 2, 'green': 1})

>>> # Find the ten most common words in Hamlet
>>> import re
>>> words = re.findall('\w+', open('hamlet.txt').read().lower())
>>> Counter(words).most_common(10)
[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),
 ('you', 554),  ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]

A :class:`Counter` is a :class:`dict` subclass for counting hashable objects. It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. The :class:`Counter` class is similar to bags or multisets in other languages.

Elements are counted from an iterable or initialized from another mapping (or counter):

>>> c = Counter()                           # a new, empty counter
>>> c = Counter('gallahad')                 # a new counter from an iterable
>>> c = Counter({'red': 4, 'blue': 2})      # a new counter from a mapping
>>> c = Counter(cats=4, dogs=8)             # a new counter from keyword args

Counter objects have a dictionary interface except that they return a zero count for missing items instead of raising a :exc:`KeyError`:

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

Setting a count to zero does not remove an element from a counter. Use del to remove it entirely:

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

Counter objects support three methods beyond those available for all dictionaries:

The usual dictionary methods are available for :class:`Counter` objects except for two which work differently for counters.

Common patterns for working with :class:`Counter` objects:

sum(c.values())                 # total of all counts
c.clear()                       # reset all counts
list(c)                         # list unique elements
set(c)                          # convert to a set
dict(c)                         # convert to a regular dictionary
c.items()                       # convert to a list of (elem, cnt) pairs
Counter(dict(list_of_pairs))    # convert from a list of (elem, cnt) pairs
c.most_common()[:-n:-1]         # n least common elements
+c                              # remove zero and negative counts

Several mathematical operations are provided for combining :class:`Counter` objects to produce multisets (counters that have counts greater than zero). Addition and subtraction combine counters by adding or subtracting the counts of corresponding elements. Intersection and union return the minimum and maximum of corresponding counts. Each operation can accept inputs with signed counts, but the output will exclude results with counts of zero or less.

>>> c = Counter(a=3, b=1)
>>> d = Counter(a=1, b=2)
>>> c + d                       # add two counters together:  c[x] + d[x]
Counter({'a': 4, 'b': 3})
>>> c - d                       # subtract (keeping only positive counts)
Counter({'a': 2})
>>> c & d                       # intersection:  min(c[x], d[x])
Counter({'a': 1, 'b': 1})
>>> c | d                       # union:  max(c[x], d[x])
Counter({'a': 3, 'b': 2})

Unary addition and substraction are shortcuts for adding an empty counter or subtracting from an empty counter.

>>> c = Counter(a=2, b=-4)
>>> +c
Counter({'a': 2})
>>> -c
Counter({'b': 4})


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

  • The :class:`Counter` class itself is a dictionary subclass with no restrictions on its keys and values. The values are intended to be numbers representing counts, but you could store anything in the value field.
  • The :meth:`most_common` method requires only that the values be orderable.
  • For in-place operations such as c[key] += 1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for :meth:`update` and :meth:`subtract` which allow negative and zero values for both inputs and outputs.
  • The multiset methods are designed only for use cases with positive values. The inputs may be negative or zero, but only outputs with positive values are created. There are no type restrictions, but the value type needs to support addition, subtraction, and comparison.
  • The :meth:`elements` method requires integer counts. It ignores zero and negative counts.

:class:`deque` objects

Returns a new deque object initialized left-to-right (using :meth:`append`) with data from iterable. If iterable is not specified, the new deque is empty.

Deques are a generalization of stacks and queues (the name is pronounced "deck" and is short for "double-ended queue"). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.

Though :class:`list` objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0, v) operations which change both the size and position of the underlying data representation.

If maxlen is not specified or is None, deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to the tail filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest.

Deque objects support the following methods:

Deque objects also provide one read-only attribute:

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.


: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
    it = iter(iterable)
    d = deque(itertools.islice(it, n-1))
    s = sum(d)
    for elem in it:
        s += elem - d.popleft()
        yield s / n

The :meth:`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 :meth:`rotate` method to position elements to be popped:

def delete_nth(d, n):

To implement :class:`deque` slicing, use a similar approach applying :meth:`rotate` to bring a target element to the left side of the deque. Remove old entries with :meth:`popleft`, add new entries with :meth:`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` Examples

Using :class:`list` as the :attr:`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)
>>> list(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:`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)
>>> list(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]

Setting the :attr:`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
>>> list(d.items())
[('i', 4), ('p', 2), ('s', 4), ('m', 1)]

When a letter is first encountered, it is missing from the mapping, so the :attr:`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:`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)
>>> list(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.

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.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.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.

>>> 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],

To retrieve a field whose name is stored in a string, use the :func:`getattr` function:

>>> getattr(p, 'x')

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:

>>> class Point(namedtuple('Point', 'x y')):
        __slots__ = ()
        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):
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:`_fields` attribute:

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

Default values can be implemented by using :meth:`_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')

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

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

:class:`OrderedDict` objects

Ordered dictionaries are just like regular dictionaries but they remember the order that items were inserted. When iterating over an ordered dictionary, the items are returned in the order their keys were first added.

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.

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:`Mapping` objects are order-insensitive like regular dictionaries. This allows :class:`OrderedDict` objects to be substituted anywhere a regular dictionary is used.

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

:class:`OrderedDict` Examples and Recipes

Since an ordered dictionary remembers its insertion order, it can be used in conjuction 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 the 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 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:

: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 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:

Subclassing requirements: Subclasses of :class:`UserList` are expect 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 that simulates a string or a Unicode 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 sequence. The sequence can be an instance of :class:`bytes`, :class:`str`, :class:`UserString` (or a subclass) or an arbitrary sequence which can be converted into a string using the built-in :func:`str` function.