
``collections`` --- High-performance container datatypes
********************************************************

New in version 2.4.

This module implements high-performance container datatypes.
Currently, there are two datatypes, ``deque`` and ``defaultdict``, and
one datatype factory function, ``namedtuple()``.

Changed in version 2.5: Added ``defaultdict``.

Changed in version 2.6: Added ``namedtuple()``.

The specialized containers provided in this module provide
alternatives to Python's general purpose built-in containers,
``dict``, ``list``, ``set``, and ``tuple``.

Besides the containers provided here, the optional ``bsddb`` module
offers the ability to create in-memory or file based ordered
dictionaries with string keys using the ``bsddb.btopen()`` method.

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

Changed in version 2.6: Added abstract base classes.


ABCs - abstract base classes
============================

The collections module offers the following ABCs:

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

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

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

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

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

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

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

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

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

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

See also:

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

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


``deque`` objects
=================

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

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

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

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

   New in version 2.4.

   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.

   Changed in version 2.6: Added *maxlen* parameter.

   Deque objects support the following methods:

   append(x)

      Add *x* to the right side of the deque.

   appendleft(x)

      Add *x* to the left side of the deque.

   clear()

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

   extend(iterable)

      Extend the right side of the deque by appending elements from
      the iterable argument.

   extendleft(iterable)

      Extend the left side of the deque by appending elements from
      *iterable*. Note, the series of left appends results in
      reversing the order of elements in the iterable argument.

   pop()

      Remove and return an element from the right side of the deque.
      If no elements are present, raises an ``IndexError``.

   popleft()

      Remove and return an element from the left side of the deque. If
      no elements are present, raises an ``IndexError``.

   remove(value)

      Removed the first occurrence of *value*.  If not found, raises a
      ``ValueError``.

      New in version 2.5.

   rotate(n)

      Rotate the deque *n* steps to the right.  If *n* is negative,
      rotate to the left.  Rotating one step to the right is
      equivalent to: ``d.appendleft(d.pop())``.

In addition to the above, deques support iteration, pickling,
``len(d)``, ``reversed(d)``, ``copy.copy(d)``, ``copy.deepcopy(d)``,
membership testing with the ``in`` operator, and subscript references
such as ``d[-1]``.  Indexed access is O(1) at both ends but slows to
O(n) in the middle.  For fast random access, use lists instead.

Example:

   >>> from collections import deque
   >>> d = deque('ghi')                 # make a new deque with three items
   >>> for elem in d:                   # iterate over the deque's elements
   ...     print elem.upper()
   G
   H
   I

   >>> d.append('j')                    # add a new entry to the right side
   >>> d.appendleft('f')                # add a new entry to the left side
   >>> d                                # show the representation of the deque
   deque(['f', 'g', 'h', 'i', 'j'])

   >>> d.pop()                          # return and remove the rightmost item
   'j'
   >>> d.popleft()                      # return and remove the leftmost item
   'f'
   >>> list(d)                          # list the contents of the deque
   ['g', 'h', 'i']
   >>> d[0]                             # peek at leftmost item
   'g'
   >>> d[-1]                            # peek at rightmost item
   'i'

   >>> list(reversed(d))                # list the contents of a deque in reverse
   ['i', 'h', 'g']
   >>> 'h' in d                         # search the deque
   True
   >>> d.extend('jkl')                  # add multiple elements at once
   >>> d
   deque(['g', 'h', 'i', 'j', 'k', 'l'])
   >>> d.rotate(1)                      # right rotation
   >>> d
   deque(['l', 'g', 'h', 'i', 'j', 'k'])
   >>> d.rotate(-1)                     # left rotation
   >>> d
   deque(['g', 'h', 'i', 'j', 'k', 'l'])

   >>> deque(reversed(d))               # make a new deque in reverse order
   deque(['l', 'k', 'j', 'i', 'h', 'g'])
   >>> d.clear()                        # empty the deque
   >>> d.pop()                          # cannot pop from an empty deque
   Traceback (most recent call last):
     File "<pyshell#6>", line 1, in -toplevel-
       d.pop()
   IndexError: pop from an empty deque

   >>> d.extendleft('abc')              # extendleft() reverses the input order
   >>> d
   deque(['c', 'b', 'a'])


``deque`` Recipes
-----------------

This section shows various approaches to working with deques.

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

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

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

Multi-pass data reduction algorithms can be succinctly expressed and
efficiently coded by extracting elements with multiple calls to
``popleft()``, applying a reduction function, and calling ``append()``
to add the result back to the deque.

For example, building a balanced binary tree of nested lists entails
reducing two adjacent nodes into one by grouping them in a list:

>>> def maketree(iterable):
...     d = deque(iterable)
...     while len(d) > 1:
...         pair = [d.popleft(), d.popleft()]
...         d.append(pair)
...     return list(d)
...
>>> print maketree('abcdefgh')
[[[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]]

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

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


``defaultdict`` objects
=======================

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

   Returns a new dictionary-like object.  ``defaultdict`` is a
   subclass of the builtin ``dict`` class.  It overrides one method
   and adds one writable instance variable.  The remaining
   functionality is the same as for the ``dict`` class and is not
   documented here.

   The first argument provides the initial value for the
   ``default_factory`` attribute; it defaults to ``None``. All
   remaining arguments are treated the same as if they were passed to
   the ``dict`` constructor, including keyword arguments.

   New in version 2.5.

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

   __missing__(key)

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

      If ``default_factory`` is not ``None``, it is called without
      arguments to provide a default value for the given *key*, this
      value is inserted in the dictionary for the *key*, and returned.

      If calling ``default_factory`` raises an exception this
      exception is propagated unchanged.

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

   ``defaultdict`` objects support the following instance variable:

   default_factory

      This attribute is used by the ``__missing__()`` method; it is
      initialized from the first argument to the constructor, if
      present, or to ``None``, if absent.


``defaultdict`` Examples
------------------------

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

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

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

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

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

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

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

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

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

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

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


``namedtuple()`` Factory Function for Tuples with Named Fields
==============================================================

Named tuples assign meaning to each position in a tuple and allow for
more readable, self-documenting code.  They can be used wherever
regular tuples are used, and they add the ability to access fields by
name instead of position index.

collections.namedtuple(typename, field_names[, verbose])

   Returns a new tuple subclass named *typename*.  The new subclass is
   used to create tuple-like objects that have fields accessible by
   attribute lookup as well as being indexable and iterable.
   Instances of the subclass also have a helpful docstring (with
   typename and field_names) and a helpful ``__repr__()`` method which
   lists the tuple contents in a ``name=value`` format.

   The *field_names* are a single string with each fieldname separated
   by whitespace and/or commas, for example ``'x y'`` or ``'x, y'``.
   Alternatively, *field_names* can be a sequence of strings such as
   ``['x', 'y']``.

   Any valid Python identifier may be used for a fieldname except for
   names starting with an underscore.  Valid identifiers consist of
   letters, digits, and underscores but do not start with a digit or
   underscore and cannot be a ``keyword`` such as *class*, *for*,
   *return*, *global*, *pass*, *print*, or *raise*.

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

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

   New in version 2.6.

Example:

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

           __slots__ = ()

           _fields = ('x', 'y')

           def __new__(cls, x, y):
               return tuple.__new__(cls, (x, y))

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

           def __repr__(self):
               return 'Point(x=%r, y=%r)' % self

           def _asdict(t):
               'Return a new dict which maps field names to their values'
               return {'x': t[0], 'y': t[1]}

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

           def __getnewargs__(self):
               return tuple(self)

           x = property(itemgetter(0))
           y = property(itemgetter(1))

   >>> p = Point(11, y=22)     # instantiate with positional or keyword arguments
   >>> p[0] + p[1]             # indexable like the plain tuple (11, 22)
   33
   >>> x, y = p                # unpack like a regular tuple
   >>> x, y
   (11, 22)
   >>> p.x + p.y               # fields also accessible by name
   33
   >>> p                       # readable __repr__ with a name=value style
   Point(x=11, y=22)

Named tuples are especially useful for assigning field names to result
tuples returned by the ``csv`` or ``sqlite3`` modules:

   EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')

   import csv
   for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
       print emp.name, emp.title

   import sqlite3
   conn = sqlite3.connect('/companydata')
   cursor = conn.cursor()
   cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
   for emp in map(EmployeeRecord._make, cursor.fetchall()):
       print emp.name, emp.title

In addition to the methods inherited from tuples, named tuples support
three additional methods and one attribute.  To prevent conflicts with
field names, the method and attribute names start with an underscore.

somenamedtuple._make(iterable)

   Class method that makes a new instance from an existing sequence or
   iterable.

   >>> t = [11, 22]
   >>> Point._make(t)
   Point(x=11, y=22)

somenamedtuple._asdict()

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

      >>> p._asdict()
      {'x': 11, 'y': 22}

somenamedtuple._replace(kwargs)

   Return a new instance of the named tuple replacing specified fields
   with new values:

   >>> p = Point(x=11, y=22)
   >>> p._replace(x=33)
   Point(x=33, y=22)

   >>> for partnum, record in inventory.items():
   ...     inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())

somenamedtuple._fields

   Tuple of strings listing the field names.  Useful for introspection
   and for creating new named tuple types from existing named tuples.

   >>> p._fields            # view the field names
   ('x', 'y')

   >>> Color = namedtuple('Color', 'red green blue')
   >>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)
   >>> Pixel(11, 22, 128, 255, 0)
   Pixel(x=11, y=22, red=128, green=255, blue=0)

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

>>> getattr(p, 'x')
11

To convert a dictionary to a named tuple, use the double-star-operator
(as described in *Unpacking Argument Lists*):

>>> d = {'x': 11, 'y': 22}
>>> Point(**d)
Point(x=11, y=22)

Since a named tuple is a regular Python class, it is easy to add or
change functionality with a subclass.  Here is how to add a calculated
field and a fixed-width print format:

   >>> class Point(namedtuple('Point', 'x y')):
   ...     __slots__ = ()
   ...     @property
   ...     def hypot(self):
   ...         return (self.x ** 2 + self.y ** 2) ** 0.5
   ...     def __str__(self):
   ...         return 'Point: x=%6.3f  y=%6.3f  hypot=%6.3f' % (self.x, self.y, self.hypot)

   >>> for p in Point(3, 4), Point(14, 5/7.):
   ...     print p
   Point: x= 3.000  y= 4.000  hypot= 5.000
   Point: x=14.000  y= 0.714  hypot=14.018

The subclass shown above sets ``__slots__`` to an empty tuple.  This
keeps keep memory requirements low by preventing the creation of
instance dictionaries.

Subclassing is not useful for adding new, stored fields.  Instead,
simply create a new named tuple type from the ``_fields`` attribute:

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

Default values can be implemented by using ``_replace()`` to customize
a prototype instance:

>>> Account = namedtuple('Account', 'owner balance transaction_count')
>>> default_account = Account('<owner name>', 0.0, 0)
>>> johns_account = default_account._replace(owner='John')

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

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

See also:

   Named tuple recipe adapted for Python 2.4.
