:mod:`collections` --- Container datatypes ========================================== .. module:: collections :synopsis: Container datatypes .. moduleauthor:: Raymond Hettinger .. sectionauthor:: Raymond Hettinger .. testsetup:: * from collections import * import itertools __name__ = '' This module implements high-performance container datatypes. Currently, there are two datatypes, :class:`deque` and :class:`defaultdict`, and one datatype factory function, :func:`namedtuple`. This module also provides the :class:`UserDict` and :class:`UserList` classes which may be useful when inheriting directly from :class:`dict` or :class:`list` isn't convenient. The specialized containers provided in this module provide alternatives to Python's general purpose built-in containers, :class:`dict`, :class:`list`, :class:`set`, and :class:`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 ========================= ===================== ====================== ==================================================== :class:`Container` ``__contains__`` :class:`Hashable` ``__hash__`` :class:`Iterable` ``__iter__`` :class:`Iterator` :class:`Iterable` ``__next__`` ``__iter__`` :class:`Sized` ``__len__`` :class:`Callable` ``__call__`` :class:`Sequence` :class:`Sized`, ``__getitem__`` ``__contains__``. ``__iter__``, ``__reversed__``. :class:`Iterable`, and ``__len__`` ``index``, and ``count`` :class:`Container` :class:`MutableSequence` :class:`Sequence` ``__getitem__`` Inherited Sequence methods and ``__delitem__``, ``append``, ``reverse``, ``extend``, ``pop``, ``insert``, ``remove``, and ``__iadd__`` and ``__len__`` :class:`Set` :class:`Sized`, ``__len__``, ``__le__``, ``__lt__``, ``__eq__``, ``__ne__``, :class:`Iterable`, ``__iter__``, and ``__gt__``, ``__ge__``, ``__and__``, ``__or__`` :class:`Container` ``__contains__`` ``__sub__``, ``__xor__``, and ``isdisjoint`` :class:`MutableSet` :class:`Set` ``add`` and Inherited Set methods and ``discard`` ``clear``, ``pop``, ``remove``, ``__ior__``, ``__iand__``, ``__ixor__``, and ``__isub__`` :class:`Mapping` :class:`Sized`, ``__getitem__``, ``__contains__``, ``keys``, ``items``, ``values``, :class:`Iterable`, ``__len__``. and ``get``, ``__eq__``, and ``__ne__`` :class:`Container` ``__iter__`` :class:`MutableMapping` :class:`Mapping` ``__getitem__`` Inherited Mapping methods and ``__setitem__``, ``pop``, ``popitem``, ``clear``, ``update``, ``__delitem__``, and ``setdefault`` ``__iter__``, and ``__len__`` :class:`MappingView` :class:`Sized` ``__len__`` :class:`KeysView` :class:`MappingView`, ``__contains__``, :class:`Set` ``__iter__`` :class:`ItemsView` :class:`MappingView`, ``__contains__``, :class:`Set` ``__iter__`` :class:`ValuesView` :class:`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 :class:`Set` API, it only necessary to supply the three underlying abstract methods: :meth:`__contains__`, :meth:`__iter__`, and :meth:`__len__`. The ABC supplies the remaining methods such as :meth:`__and__` and :meth:`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 :class:`Set` and :class:`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 :meth:`_from_iterable` which calls ``cls(iterable)`` to produce a new set. If the :class:`Set` mixin is being used in a class with a different constructor signature, you will need to override :meth:`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 :meth:`__le__` and then the other operations will automatically follow suit. (3) The :class:`Set` mixin provides a :meth:`_hash` method to compute a hash value for the set; however, :meth:`__hash__` is not defined because not all sets are hashable or immutable. To add set hashabilty using mixins, inherit from both :meth:`Set` and :meth:`Hashable`, then define ``__hash__ = Set._hash``. (For more about ABCs, see the :mod:`abc` module and :pep:`3119`.) :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)] .. class:: Counter([iterable-or-mapping]) 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(spam=8, eggs=1) # 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 0 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 .. versionadded:: 2.7 Counter objects support two methods beyond those available for all dictionaries: .. method:: elements() Return an iterator over elements repeating each as many times as its count. Elements are returned in arbitrary order. If an element's count is less than one, :meth:`elements` will ignore it. >>> c = Counter(a=4, b=2, c=0, d=-2) >>> list(c.elements()) ['a', 'a', 'a', 'a', 'b', 'b'] .. method:: most_common([n]) Return a list of the *n* most common elements and their counts from the most common to the least. If *n* is not specified or is ``None``, return a list of all element counts in decreasing order of frequency. Elements with equal counts are ordered arbitrarily:: >>> Counter('abracadabra').most_common(3) [('a', 5), ('r', 2), ('b', 2)] The usual dictionary methods are available for :class:`Counter` objects except for two which work differently for counters. .. method:: fromkeys(iterable) This class method is not implemented for :class:`Counter` objects. .. method:: update([iterable-or-mapping]) Elements are counted from an *iterable* or added-in from another *mapping* (or counter). Like :meth:`dict.update` but adds-in counts instead of replacing them. Also, the *iterable* is expected to be a sequence of elements, not a sequence of ``(key, value)`` pairs:: >>> c = Counter('which') >>> c.update('witch') # add elements from another iterable >>> d = Counter('watch') >>> c.update(d) # add elements from another counter >>> c['h'] # four 'h' in which, witch, and watch 4 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 += Counter() # remove zero and negative counts Several multiset mathematical operations are provided for combining :class:`Counter` objects. Multisets are like regular sets but are allowed to contain repeated elements (with counts of one or more). 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. All four multiset operations exclude results with counts less than one:: >>> 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}) .. seealso:: * `Counter class `_ adapted for Python 2.5 and an early `Bag recipe `_ for Python 2.4. * `Bag class `_ in Smalltalk. * Wikipedia entry for `Multisets `_\. * `C++ multisets `_ tutorial with examples. * For mathematical operations on multisets and their use cases, see *Knuth, Donald. The Art of Computer Programming Volume II, Section 4.6.3, Exercise 19*\. * To enumerate all distinct multisets of a given size over a given set of elements, see the :func:`combinations_with_replacement` function in the :ref:`itertools-recipes` for itertools:: map(Counter, combinations_with_replacement('ABC', 2)) --> AA AB AC BB BC CC :class:`deque` objects ---------------------- .. class:: deque([iterable[, maxlen]]) 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: .. method:: append(x) Add *x* to the right side of the deque. .. method:: appendleft(x) Add *x* to the left side of the deque. .. method:: clear() Remove all elements from the deque leaving it with length 0. .. method:: extend(iterable) Extend the right side of the deque by appending elements from the iterable argument. .. method:: 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. .. method:: pop() Remove and return an element from the right side of the deque. If no elements are present, raises an :exc:`IndexError`. .. method:: popleft() Remove and return an element from the left side of the deque. If no elements are present, raises an :exc:`IndexError`. .. method:: remove(value) Removed the first occurrence of *value*. If not found, raises a :exc:`ValueError`. .. method:: 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 :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. 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 "", 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. 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): d.rotate(-n) d.popleft() d.rotate(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``. Multi-pass data reduction algorithms can be succinctly expressed and efficiently coded by extracting elements with multiple calls to :meth:`popleft`, applying a reduction function, and calling :meth:`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) :class:`defaultdict` objects ---------------------------- .. class:: defaultdict([default_factory[, ...]]) Returns a new dictionary-like object. :class:`defaultdict` is a subclass of the builtin :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:: defaultdict.__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__`. :class:`defaultdict` objects support the following instance variable: .. attribute:: defaultdict.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:`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 :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) ... >>> 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 ... >>> 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('')) >>> d.update(name='John', action='ran') >>> '%(name)s %(action)s to %(object)s' % d 'John ran to ' 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) ... >>> d.items() [('blue', set([2, 4])), ('red', set([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, [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 :meth:`__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 :mod:`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: .. doctest:: :options: +NORMALIZE_WHITESPACE >>> 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 :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 one attribute. To prevent conflicts with field names, the method and attribute names start with an underscore. .. method:: 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 dict which maps field names to their corresponding values:: >>> p._asdict() {'x': 11, 'y': 22} .. 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) 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 [#]_: >>> 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 :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('', 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) .. rubric:: Footnotes .. [#] For information on the double-star-operator see :ref:`tut-unpacking-arguments` and :ref:`calls`. :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:: UserDict.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:: UserList.data A real :class:`list` object used to store the contents of the :class:`UserList` class. **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:: 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 :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.