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# `collections` — Container datatypes

This module implements high-performance container datatypes. Currently, there are two datatypes, `deque` and `defaultdict`, and one datatype factory function, `namedtuple()`. This module also provides the `UserDict` and `UserList` classes which may be useful when inheriting directly from `dict` or `list` isn’t convenient.

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

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, and some of them can also be used as mixin 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`, `Iterable`, `Container` | `__getitem__` and `__len__` | `__contains__`. `__iter__`, `__reversed__`. `index`, and `count`
`MutableSequence` | `Sequence` | `__getitem__` `__delitem__`, `insert`, and `__len__` | Inherited Sequence methods and `append`, `reverse`, `extend`, `pop`, `remove`, and `__iadd__`
`Set` | `Sized`, `Iterable`, `Container` | `__len__`, `__iter__`, and `__contains__` | `__le__`, `__lt__`, `__eq__`, `__ne__`, `__gt__`, `__ge__`, `__and__`, `__or__` `__sub__`, `__xor__`, and `isdisjoint`
`MutableSet` | `Set` | `add` and `discard` | Inherited Set methods and `clear`, `pop`, `remove`, `__ior__`, `__iand__`, `__ixor__`, and `__isub__`
`Mapping` | `Sized`, `Iterable`, `Container` | `__getitem__`, `__len__`. and `__iter__` | `__contains__`, `keys`, `items`, `values`, `get`, `__eq__`, and `__ne__`
`MutableMapping` | `Mapping` | `__getitem__` `__setitem__`, `__delitem__`, `__iter__`, and `__len__` | Inherited Mapping methods and `pop`, `popitem`, `clear`, `update`, and `setdefault`
`MappingView` | `Sized` |  | `__len__`
`KeysView` | `MappingView`, `Set` |  | `__contains__`, `__iter__`
`ItemsView` | `MappingView`, `Set` |  | `__contains__`, `__iter__`
`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`.

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

## `deque` objects

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.

  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:

  `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`.
  `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 `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.

  `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 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 `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, 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.

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 [1]:

    >>> 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)

Footnotes

[1] | For information on the double-star-operator see Unpacking Argument Lists and Calls.

## `UserDict` objects

The 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 `dict`; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.

class `collections.``UserDict`([initialdata])
  Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the `data` attribute of `UserDict` instances. If initialdata is provided, `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, `UserDict` instances provide the following attribute:

`UserDict.``data`
  A real dictionary used to store the contents of the `UserDict` 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 `list`; however, this class can be easier to work with because the underlying list is accessible as an attribute.

class `collections.``UserList`([list])
  Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the `data` attribute of `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 `UserList` object.

In addition to supporting the methods and operations of mutable sequences, `UserList` instances provide the following attribute:

`UserList.``data`
  A real `list` object used to store the contents of the `UserList` class.

Subclassing requirements: Subclasses of `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.

## `UserString` objects

The 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 `str`; however, this class can be easier to work with because the underlying string is accessible as an attribute.

class `collections.``UserString`([sequence])
  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 `data` attribute of `UserString` instances. The instance’s contents are initially set to a copy of sequence. The sequence can be an instance of `bytes`, `str`, `UserString` (or a subclass) or an arbitrary sequence which can be converted into a string using the built-in `str()` function.

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