1 

     2 :mod:`collections` --- High-performance container datatypes

     3 ===========================================================

     4 

     5 .. module:: collections

     6    :synopsis: High-performance datatypes

     7 .. moduleauthor:: Raymond Hettinger <python@rcn.com>

     8 .. sectionauthor:: Raymond Hettinger <python@rcn.com>

     9 

    10 .. versionadded:: 2.4

    11 

    12 .. testsetup:: *

    13 

    14    from collections import *

    15    import itertools

    16    __name__ = '<doctest>'

    17 

    18 This module implements high-performance container datatypes.  Currently,

    19 there are two datatypes, :class:`deque` and :class:`defaultdict`, and

    20 one datatype factory function, :func:`namedtuple`.

    21 

    22 .. versionchanged:: 2.5

    23    Added :class:`defaultdict`.

    24 

    25 .. versionchanged:: 2.6

    26    Added :func:`namedtuple`.

    27 

    28 The specialized containers provided in this module provide alternatives

    29 to Python's general purpose built-in containers, :class:`dict`,

    30 :class:`list`, :class:`set`, and :class:`tuple`.

    31 

    32 Besides the containers provided here, the optional :mod:`bsddb`

    33 module offers the ability to create in-memory or file based ordered

    34 dictionaries with string keys using the :meth:`bsddb.btopen` method.

    35 

    36 In addition to containers, the collections module provides some ABCs

    37 (abstract base classes) that can be used to test whether a class

    38 provides a particular interface, for example, is it hashable or

    39 a mapping.

    40 

    41 .. versionchanged:: 2.6

    42    Added abstract base classes.

    43 

    44 ABCs - abstract base classes

    45 ----------------------------

    46 

    47 The collections module offers the following ABCs:

    48 

    49 =========================  =====================  ======================  ====================================================

    50 ABC                        Inherits               Abstract Methods        Mixin Methods

    51 =========================  =====================  ======================  ====================================================

    52 :class:`Container`                                ``__contains__``

    53 :class:`Hashable`                                 ``__hash__``

    54 :class:`Iterable`                                 ``__iter__``

    55 :class:`Iterator`          :class:`Iterable`      ``__next__``            ``__iter__``

    56 :class:`Sized`          			  ``__len__``

    57 :class:`Callable`                                 ``__call__``

    58                                                   

    59 :class:`Sequence`          :class:`Sized`,        ``__getitem__``         ``__contains__``. ``__iter__``, ``__reversed__``.

    60                            :class:`Iterable`,     and ``__len__``         ``index``, and ``count``

    61                            :class:`Container`     

    62                                                   

    63 :class:`MutableSequnce`    :class:`Sequence`      ``__getitem__``         Inherited Sequence methods and

    64                                                   ``__delitem__``,        ``append``, ``reverse``, ``extend``, ``pop``,

    65                                                   ``insert``,             ``remove``, and ``__iadd__``

    66                                                   and ``__len__``

    67                                                   

    68 :class:`Set`               :class:`Sized`,        ``__len__``,            ``__le__``, ``__lt__``, ``__eq__``, ``__ne__``,

    69                            :class:`Iterable`,     ``__iter__``, and       ``__gt__``, ``__ge__``, ``__and__``, ``__or__``

    70                            :class:`Container`     ``__contains__``        ``__sub__``, ``__xor__``, and ``isdisjoint``

    71                                                   

    72 :class:`MutableSet`        :class:`Set`           ``add`` and             Inherited Set methods and

    73                                                   ``discard``             ``clear``, ``pop``, ``remove``, ``__ior__``,

    74                                                                           ``__iand__``, ``__ixor__``, and ``__isub__``

    75                                                   

    76 :class:`Mapping`           :class:`Sized`,        ``__getitem__``,        ``__contains__``, ``keys``, ``items``, ``values``,

    77                            :class:`Iterable`,     ``__len__``. and        ``get``, ``__eq__``, and ``__ne__``

    78                            :class:`Container`     ``__iter__``

    79                                                   

    80 :class:`MutableMapping`    :class:`Mapping`       ``__getitem__``         Inherited Mapping methods and

    81                                                   ``__setitem__``,        ``pop``, ``popitem``, ``clear``, ``update``,

    82                                                   ``__delitem__``,        and ``setdefault``

    83 						  ``__iter__``, and

    84                                                   ``__len__``

    85                                                   

    86 :class:`MappingView`       :class:`Sized`                                 ``__len__``

    87 :class:`KeysView`          :class:`MappingView`,                          ``__contains__``,

    88                            :class:`Set`                                   ``__iter__``

    89 :class:`ItemsView`         :class:`MappingView`,                          ``__contains__``,

    90                            :class:`Set`                                   ``__iter__``

    91 :class:`ValuesView`        :class:`MappingView`                           ``__contains__``, ``__iter__``

    92 =========================  =====================  ======================  ====================================================

    93 

    94 These ABCs allow us to ask classes or instances if they provide

    95 particular functionality, for example::

    96 

    97     size = None

    98     if isinstance(myvar, collections.Sized):

    99 	size = len(myvar)

   100 

   101 Several of the ABCs are also useful as mixins that make it easier to develop

   102 classes supporting container APIs.  For example, to write a class supporting

   103 the full :class:`Set` API, it only necessary to supply the three underlying

   104 abstract methods: :meth:`__contains__`, :meth:`__iter__`, and :meth:`__len__`.

   105 The ABC supplies the remaining methods such as :meth:`__and__` and

   106 :meth:`isdisjoint` ::

   107 

   108     class ListBasedSet(collections.Set):

   109          ''' Alternate set implementation favoring space over speed

   110              and not requiring the set elements to be hashable. '''

   111          def __init__(self, iterable):

   112              self.elements = lst = []

   113              for value in iterable:

   114                  if value not in lst:

   115                      lst.append(value)

   116          def __iter__(self):

   117              return iter(self.elements)

   118          def __contains__(self, value):

   119              return value in self.elements

   120          def __len__(self):

   121              return len(self.elements)

   122 

   123     s1 = ListBasedSet('abcdef')

   124     s2 = ListBasedSet('defghi')

   125     overlap = s1 & s2            # The __and__() method is supported automatically

   126 

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

   128 

   129 (1)

   130    Since some set operations create new sets, the default mixin methods need

   131    a way to create new instances from an iterable. The class constructor is

   132    assumed to have a signature in the form ``ClassName(iterable)``.

   133    That assumption is factored-out to an internal classmethod called

   134    :meth:`_from_iterable` which calls ``cls(iterable)`` to produce a new set.

   135    If the :class:`Set` mixin is being used in a class with a different

   136    constructor signature, you will need to override :meth:`from_iterable`

   137    with a classmethod that can construct new instances from

   138    an iterable argument.

   139 

   140 (2)

   141    To override the comparisons (presumably for speed, as the

   142    semantics are fixed), redefine :meth:`__le__` and

   143    then the other operations will automatically follow suit.

   144 

   145 (3)

   146    The :class:`Set` mixin provides a :meth:`_hash` method to compute a hash value

   147    for the set; however, :meth:`__hash__` is not defined because not all sets

   148    are hashable or immutable.  To add set hashabilty using mixins,

   149    inherit from both :meth:`Set` and :meth:`Hashable`, then define

   150    ``__hash__ = Set._hash``.

   151 

   152 (For more about ABCs, see the :mod:`abc` module and :pep:`3119`.)

   153 

   154 

   155 

   156 .. _deque-objects:

   157 

   158 :class:`deque` objects

   159 ----------------------

   160 

   161 

   162 .. class:: deque([iterable[, maxlen]])

   163 

   164    Returns a new deque object initialized left-to-right (using :meth:`append`) with

   165    data from *iterable*.  If *iterable* is not specified, the new deque is empty.

   166 

   167    Deques are a generalization of stacks and queues (the name is pronounced "deck"

   168    and is short for "double-ended queue").  Deques support thread-safe, memory

   169    efficient appends and pops from either side of the deque with approximately the

   170    same O(1) performance in either direction.

   171 

   172    Though :class:`list` objects support similar operations, they are optimized for

   173    fast fixed-length operations and incur O(n) memory movement costs for

   174    ``pop(0)`` and ``insert(0, v)`` operations which change both the size and

   175    position of the underlying data representation.

   176 

   177    .. versionadded:: 2.4

   178 

   179    If *maxlen* is not specified or is *None*, deques may grow to an

   180    arbitrary length.  Otherwise, the deque is bounded to the specified maximum

   181    length.  Once a bounded length deque is full, when new items are added, a

   182    corresponding number of items are discarded from the opposite end.  Bounded

   183    length deques provide functionality similar to the ``tail`` filter in

   184    Unix. They are also useful for tracking transactions and other pools of data

   185    where only the most recent activity is of interest.

   186 

   187    .. versionchanged:: 2.6

   188       Added *maxlen* parameter.

   189 

   190    Deque objects support the following methods:

   191 

   192 

   193    .. method:: append(x)

   194 

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

   196 

   197 

   198    .. method:: appendleft(x)

   199 

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

   201 

   202 

   203    .. method:: clear()

   204 

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

   206 

   207 

   208    .. method:: extend(iterable)

   209 

   210       Extend the right side of the deque by appending elements from the iterable

   211       argument.

   212 

   213 

   214    .. method:: extendleft(iterable)

   215 

   216       Extend the left side of the deque by appending elements from *iterable*.

   217       Note, the series of left appends results in reversing the order of

   218       elements in the iterable argument.

   219 

   220 

   221    .. method:: pop()

   222 

   223       Remove and return an element from the right side of the deque. If no

   224       elements are present, raises an :exc:`IndexError`.

   225 

   226 

   227    .. method:: popleft()

   228 

   229       Remove and return an element from the left side of the deque. If no

   230       elements are present, raises an :exc:`IndexError`.

   231 

   232 

   233    .. method:: remove(value)

   234 

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

   236       :exc:`ValueError`.

   237 

   238       .. versionadded:: 2.5

   239 

   240 

   241    .. method:: rotate(n)

   242 

   243       Rotate the deque *n* steps to the right.  If *n* is negative, rotate to

   244       the left.  Rotating one step to the right is equivalent to:

   245       ``d.appendleft(d.pop())``.

   246 

   247 

   248 In addition to the above, deques support iteration, pickling, ``len(d)``,

   249 ``reversed(d)``, ``copy.copy(d)``, ``copy.deepcopy(d)``, membership testing with

   250 the :keyword:`in` operator, and subscript references such as ``d[-1]``.  Indexed

   251 access is O(1) at both ends but slows to O(n) in the middle.  For fast random

   252 access, use lists instead.

   253 

   254 Example:

   255 

   256 .. doctest::

   257 

   258    >>> from collections import deque

   259    >>> d = deque('ghi')                 # make a new deque with three items

   260    >>> for elem in d:                   # iterate over the deque's elements

   261    ...     print elem.upper()

   262    G

   263    H

   264    I

   265 

   266    >>> d.append('j')                    # add a new entry to the right side

   267    >>> d.appendleft('f')                # add a new entry to the left side

   268    >>> d                                # show the representation of the deque

   269    deque(['f', 'g', 'h', 'i', 'j'])

   270 

   271    >>> d.pop()                          # return and remove the rightmost item

   272    'j'

   273    >>> d.popleft()                      # return and remove the leftmost item

   274    'f'

   275    >>> list(d)                          # list the contents of the deque

   276    ['g', 'h', 'i']

   277    >>> d[0]                             # peek at leftmost item

   278    'g'

   279    >>> d[-1]                            # peek at rightmost item

   280    'i'

   281 

   282    >>> list(reversed(d))                # list the contents of a deque in reverse

   283    ['i', 'h', 'g']

   284    >>> 'h' in d                         # search the deque

   285    True

   286    >>> d.extend('jkl')                  # add multiple elements at once

   287    >>> d

   288    deque(['g', 'h', 'i', 'j', 'k', 'l'])

   289    >>> d.rotate(1)                      # right rotation

   290    >>> d

   291    deque(['l', 'g', 'h', 'i', 'j', 'k'])

   292    >>> d.rotate(-1)                     # left rotation

   293    >>> d

   294    deque(['g', 'h', 'i', 'j', 'k', 'l'])

   295 

   296    >>> deque(reversed(d))               # make a new deque in reverse order

   297    deque(['l', 'k', 'j', 'i', 'h', 'g'])

   298    >>> d.clear()                        # empty the deque

   299    >>> d.pop()                          # cannot pop from an empty deque

   300    Traceback (most recent call last):

   301      File "<pyshell#6>", line 1, in -toplevel-

   302        d.pop()

   303    IndexError: pop from an empty deque

   304 

   305    >>> d.extendleft('abc')              # extendleft() reverses the input order

   306    >>> d

   307    deque(['c', 'b', 'a'])

   308 

   309 

   310 .. _deque-recipes:

   311 

   312 :class:`deque` Recipes

   313 ^^^^^^^^^^^^^^^^^^^^^^

   314 

   315 This section shows various approaches to working with deques.

   316 

   317 The :meth:`rotate` method provides a way to implement :class:`deque` slicing and

   318 deletion.  For example, a pure python implementation of ``del d[n]`` relies on

   319 the :meth:`rotate` method to position elements to be popped::

   320 

   321    def delete_nth(d, n):

   322        d.rotate(-n)

   323        d.popleft()

   324        d.rotate(n)

   325 

   326 To implement :class:`deque` slicing, use a similar approach applying

   327 :meth:`rotate` to bring a target element to the left side of the deque. Remove

   328 old entries with :meth:`popleft`, add new entries with :meth:`extend`, and then

   329 reverse the rotation.

   330 With minor variations on that approach, it is easy to implement Forth style

   331 stack manipulations such as ``dup``, ``drop``, ``swap``, ``over``, ``pick``,

   332 ``rot``, and ``roll``.

   333 

   334 Multi-pass data reduction algorithms can be succinctly expressed and efficiently

   335 coded by extracting elements with multiple calls to :meth:`popleft`, applying

   336 a reduction function, and calling :meth:`append` to add the result back to the

   337 deque.

   338 

   339 For example, building a balanced binary tree of nested lists entails reducing

   340 two adjacent nodes into one by grouping them in a list:

   341 

   342    >>> def maketree(iterable):

   343    ...     d = deque(iterable)

   344    ...     while len(d) > 1:

   345    ...         pair = [d.popleft(), d.popleft()]

   346    ...         d.append(pair)

   347    ...     return list(d)

   348    ...

   349    >>> print maketree('abcdefgh')

   350    [[[['a', 'b'], ['c', 'd']], [['e', 'f'], ['g', 'h']]]]

   351 

   352 Bounded length deques provide functionality similar to the ``tail`` filter

   353 in Unix::

   354 

   355    def tail(filename, n=10):

   356        'Return the last n lines of a file'

   357        return deque(open(filename), n)

   358 

   359 .. _defaultdict-objects:

   360 

   361 :class:`defaultdict` objects

   362 ----------------------------

   363 

   364 

   365 .. class:: defaultdict([default_factory[, ...]])

   366 

   367    Returns a new dictionary-like object.  :class:`defaultdict` is a subclass of the

   368    builtin :class:`dict` class.  It overrides one method and adds one writable

   369    instance variable.  The remaining functionality is the same as for the

   370    :class:`dict` class and is not documented here.

   371 

   372    The first argument provides the initial value for the :attr:`default_factory`

   373    attribute; it defaults to ``None``. All remaining arguments are treated the same

   374    as if they were passed to the :class:`dict` constructor, including keyword

   375    arguments.

   376 

   377    .. versionadded:: 2.5

   378 

   379    :class:`defaultdict` objects support the following method in addition to the

   380    standard :class:`dict` operations:

   381 

   382 

   383    .. method:: defaultdict.__missing__(key)

   384 

   385       If the :attr:`default_factory` attribute is ``None``, this raises a

   386       :exc:`KeyError` exception with the *key* as argument.

   387 

   388       If :attr:`default_factory` is not ``None``, it is called without arguments

   389       to provide a default value for the given *key*, this value is inserted in

   390       the dictionary for the *key*, and returned.

   391 

   392       If calling :attr:`default_factory` raises an exception this exception is

   393       propagated unchanged.

   394 

   395       This method is called by the :meth:`__getitem__` method of the

   396       :class:`dict` class when the requested key is not found; whatever it

   397       returns or raises is then returned or raised by :meth:`__getitem__`.

   398 

   399 

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

   401 

   402 

   403    .. attribute:: defaultdict.default_factory

   404 

   405       This attribute is used by the :meth:`__missing__` method; it is

   406       initialized from the first argument to the constructor, if present, or to

   407       ``None``, if absent.

   408 

   409 

   410 .. _defaultdict-examples:

   411 

   412 :class:`defaultdict` Examples

   413 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

   414 

   415 Using :class:`list` as the :attr:`default_factory`, it is easy to group a

   416 sequence of key-value pairs into a dictionary of lists:

   417 

   418    >>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]

   419    >>> d = defaultdict(list)

   420    >>> for k, v in s:

   421    ...     d[k].append(v)

   422    ...

   423    >>> d.items()

   424    [('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]

   425 

   426 When each key is encountered for the first time, it is not already in the

   427 mapping; so an entry is automatically created using the :attr:`default_factory`

   428 function which returns an empty :class:`list`.  The :meth:`list.append`

   429 operation then attaches the value to the new list.  When keys are encountered

   430 again, the look-up proceeds normally (returning the list for that key) and the

   431 :meth:`list.append` operation adds another value to the list. This technique is

   432 simpler and faster than an equivalent technique using :meth:`dict.setdefault`:

   433 

   434    >>> d = {}

   435    >>> for k, v in s:

   436    ...     d.setdefault(k, []).append(v)

   437    ...

   438    >>> d.items()

   439    [('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]

   440 

   441 Setting the :attr:`default_factory` to :class:`int` makes the

   442 :class:`defaultdict` useful for counting (like a bag or multiset in other

   443 languages):

   444 

   445    >>> s = 'mississippi'

   446    >>> d = defaultdict(int)

   447    >>> for k in s:

   448    ...     d[k] += 1

   449    ...

   450    >>> d.items()

   451    [('i', 4), ('p', 2), ('s', 4), ('m', 1)]

   452 

   453 When a letter is first encountered, it is missing from the mapping, so the

   454 :attr:`default_factory` function calls :func:`int` to supply a default count of

   455 zero.  The increment operation then builds up the count for each letter.

   456 

   457 The function :func:`int` which always returns zero is just a special case of

   458 constant functions.  A faster and more flexible way to create constant functions

   459 is to use :func:`itertools.repeat` which can supply any constant value (not just

   460 zero):

   461 

   462    >>> def constant_factory(value):

   463    ...     return itertools.repeat(value).next

   464    >>> d = defaultdict(constant_factory('<missing>'))

   465    >>> d.update(name='John', action='ran')

   466    >>> '%(name)s %(action)s to %(object)s' % d

   467    'John ran to <missing>'

   468 

   469 Setting the :attr:`default_factory` to :class:`set` makes the

   470 :class:`defaultdict` useful for building a dictionary of sets:

   471 

   472    >>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]

   473    >>> d = defaultdict(set)

   474    >>> for k, v in s:

   475    ...     d[k].add(v)

   476    ...

   477    >>> d.items()

   478    [('blue', set([2, 4])), ('red', set([1, 3]))]

   479 

   480 

   481 .. _named-tuple-factory:

   482 

   483 :func:`namedtuple` Factory Function for Tuples with Named Fields

   484 ----------------------------------------------------------------

   485 

   486 Named tuples assign meaning to each position in a tuple and allow for more readable,

   487 self-documenting code.  They can be used wherever regular tuples are used, and

   488 they add the ability to access fields by name instead of position index.

   489 

   490 .. function:: namedtuple(typename, fieldnames, [verbose])

   491 

   492    Returns a new tuple subclass named *typename*.  The new subclass is used to

   493    create tuple-like objects that have fields accessible by attribute lookup as

   494    well as being indexable and iterable.  Instances of the subclass also have a

   495    helpful docstring (with typename and fieldnames) and a helpful :meth:`__repr__`

   496    method which lists the tuple contents in a ``name=value`` format.

   497 

   498    The *fieldnames* are a single string with each fieldname separated by whitespace

   499    and/or commas, for example ``'x y'`` or ``'x, y'``.  Alternatively, *fieldnames*

   500    can be a sequence of strings such as ``['x', 'y']``.

   501 

   502    Any valid Python identifier may be used for a fieldname except for names

   503    starting with an underscore.  Valid identifiers consist of letters, digits,

   504    and underscores but do not start with a digit or underscore and cannot be

   505    a :mod:`keyword` such as *class*, *for*, *return*, *global*, *pass*, *print*,

   506    or *raise*.

   507 

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

   509 

   510    Named tuple instances do not have per-instance dictionaries, so they are

   511    lightweight and require no more memory than regular tuples.

   512 

   513    .. versionadded:: 2.6

   514 

   515 Example:

   516 

   517 .. doctest::

   518    :options: +NORMALIZE_WHITESPACE

   519 

   520    >>> Point = namedtuple('Point', 'x y', verbose=True)

   521    class Point(tuple):

   522            'Point(x, y)'

   523    <BLANKLINE>

   524            __slots__ = ()

   525    <BLANKLINE>

   526            _fields = ('x', 'y')

   527    <BLANKLINE>

   528            def __new__(cls, x, y):

   529                return tuple.__new__(cls, (x, y))

   530    <BLANKLINE>

   531            @classmethod

   532            def _make(cls, iterable, new=tuple.__new__, len=len):

   533                'Make a new Point object from a sequence or iterable'

   534                result = new(cls, iterable)

   535                if len(result) != 2:

   536                    raise TypeError('Expected 2 arguments, got %d' % len(result))

   537                return result

   538    <BLANKLINE>

   539            def __repr__(self):

   540                return 'Point(x=%r, y=%r)' % self

   541    <BLANKLINE>

   542            def _asdict(t):

   543                'Return a new dict which maps field names to their values'

   544                return {'x': t[0], 'y': t[1]}

   545    <BLANKLINE>

   546            def _replace(self, **kwds):

   547                'Return a new Point object replacing specified fields with new values'

   548                result = self._make(map(kwds.pop, ('x', 'y'), self))

   549                if kwds:

   550                    raise ValueError('Got unexpected field names: %r' % kwds.keys())

   551                return result

   552    <BLANKLINE>            

   553            def __getnewargs__(self): 

   554                return tuple(self)

   555    <BLANKLINE>

   556            x = property(itemgetter(0))

   557            y = property(itemgetter(1))

   558 

   559    >>> p = Point(11, y=22)     # instantiate with positional or keyword arguments

   560    >>> p[0] + p[1]             # indexable like the plain tuple (11, 22)

   561    33

   562    >>> x, y = p                # unpack like a regular tuple

   563    >>> x, y

   564    (11, 22)

   565    >>> p.x + p.y               # fields also accessible by name

   566    33

   567    >>> p                       # readable __repr__ with a name=value style

   568    Point(x=11, y=22)

   569 

   570 Named tuples are especially useful for assigning field names to result tuples returned

   571 by the :mod:`csv` or :mod:`sqlite3` modules::

   572 

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

   574 

   575    import csv

   576    for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):

   577        print emp.name, emp.title

   578 

   579    import sqlite3

   580    conn = sqlite3.connect('/companydata')

   581    cursor = conn.cursor()

   582    cursor.execute('SELECT name, age, title, department, paygrade FROM employees')

   583    for emp in map(EmployeeRecord._make, cursor.fetchall()):

   584        print emp.name, emp.title

   585 

   586 In addition to the methods inherited from tuples, named tuples support

   587 three additional methods and one attribute.  To prevent conflicts with

   588 field names, the method and attribute names start with an underscore.

   589 

   590 .. method:: somenamedtuple._make(iterable)

   591 

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

   593 

   594 .. doctest::

   595 

   596       >>> t = [11, 22]

   597       >>> Point._make(t)

   598       Point(x=11, y=22)

   599 

   600 .. method:: somenamedtuple._asdict()

   601 

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

   603 

   604       >>> p._asdict()

   605       {'x': 11, 'y': 22}

   606 

   607 .. method:: somenamedtuple._replace(kwargs)

   608 

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

   610    values:

   611 

   612 ::

   613 

   614       >>> p = Point(x=11, y=22)

   615       >>> p._replace(x=33)

   616       Point(x=33, y=22)

   617 

   618       >>> for partnum, record in inventory.items():

   619       ...     inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())

   620 

   621 .. attribute:: somenamedtuple._fields

   622 

   623    Tuple of strings listing the field names.  Useful for introspection

   624    and for creating new named tuple types from existing named tuples.

   625 

   626 .. doctest::

   627 

   628       >>> p._fields            # view the field names

   629       ('x', 'y')

   630 

   631       >>> Color = namedtuple('Color', 'red green blue')

   632       >>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)

   633       >>> Pixel(11, 22, 128, 255, 0)

   634       Pixel(x=11, y=22, red=128, green=255, blue=0)

   635 

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

   637 function:

   638 

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

   640     11

   641 

   642 To convert a dictionary to a named tuple, use the double-star-operator [#]_:

   643 

   644    >>> d = {'x': 11, 'y': 22}

   645    >>> Point(**d)

   646    Point(x=11, y=22)

   647 

   648 Since a named tuple is a regular Python class, it is easy to add or change

   649 functionality with a subclass.  Here is how to add a calculated field and

   650 a fixed-width print format:

   651 

   652     >>> class Point(namedtuple('Point', 'x y')):

   653     ...     __slots__ = ()

   654     ...     @property

   655     ...     def hypot(self):

   656     ...         return (self.x ** 2 + self.y ** 2) ** 0.5

   657     ...     def __str__(self):

   658     ...         return 'Point: x=%6.3f  y=%6.3f  hypot=%6.3f' % (self.x, self.y, self.hypot)

   659 

   660     >>> for p in Point(3, 4), Point(14, 5/7.):

   661     ...     print p

   662     Point: x= 3.000  y= 4.000  hypot= 5.000

   663     Point: x=14.000  y= 0.714  hypot=14.018

   664 

   665 The subclass shown above sets ``__slots__`` to an empty tuple.  This keeps

   666 keep memory requirements low by preventing the creation of instance dictionaries.

   667 

   668 Subclassing is not useful for adding new, stored fields.  Instead, simply

   669 create a new named tuple type from the :attr:`_fields` attribute:

   670 

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

   672 

   673 Default values can be implemented by using :meth:`_replace` to

   674 customize a prototype instance:

   675 

   676     >>> Account = namedtuple('Account', 'owner balance transaction_count')

   677     >>> default_account = Account('<owner name>', 0.0, 0)

   678     >>> johns_account = default_account._replace(owner='John')

   679 

   680 Enumerated constants can be implemented with named tuples, but it is simpler

   681 and more efficient to use a simple class declaration:

   682 

   683     >>> Status = namedtuple('Status', 'open pending closed')._make(range(3))

   684     >>> Status.open, Status.pending, Status.closed

   685     (0, 1, 2)

   686     >>> class Status:

   687     ...     open, pending, closed = range(3)

   688 

   689 .. rubric:: Footnotes

   690 

   691 .. [#] For information on the double-star-operator see

   692    :ref:`tut-unpacking-arguments` and :ref:`calls`.