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:mod:`collections` --- High-performance container datatypes
===========================================================
.. module:: collections
:synopsis: High-performance datatypes
.. moduleauthor:: Raymond Hettinger <python@rcn.com>
.. sectionauthor:: Raymond Hettinger <python@rcn.com>
.. versionadded:: 2.4
.. testsetup:: *
from collections import *
import itertools
__name__ = '<doctest>'
This module implements high-performance container datatypes. Currently,
there are three datatypes, :class:`Counter`, :class:`deque` and
:class:`defaultdict`, and one datatype factory function, :func:`namedtuple`.
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`.
.. versionchanged:: 2.4
Added :class:`deque`.
.. versionchanged:: 2.5
Added :class:`defaultdict`.
.. versionchanged:: 2.6
Added :func:`namedtuple` and added abstract base classes.
.. versionchanged:: 2.7
Added :class:`Counter`.
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, whether it is hashable or
a mapping.
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`, ``index``, and ``count``
:class:`Container`
:class:`MutableSequence` :class:`Sequence` ``__setitem__`` Inherited Sequence methods and
``__delitem__``, ``append``, ``reverse``, ``extend``, ``pop``,
and ``insert`` ``remove``, and ``__iadd__``
:class:`Set` :class:`Sized`, ``__le__``, ``__lt__``, ``__eq__``, ``__ne__``,
:class:`Iterable`, ``__gt__``, ``__ge__``, ``__and__``, ``__or__``
:class:`Container` ``__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`, ``get``, ``__eq__``, and ``__ne__``
:class:`Container`
:class:`MutableMapping` :class:`Mapping` ``__setitem__`` and Inherited Mapping methods and
``__delitem__`` ``pop``, ``popitem``, ``clear``, ``update``,
and ``setdefault``
: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(cats=4, dogs=8) # a new counter from keyword args
Counter objects have a dictionary interface except that they return a zero
count for missing items instead of raising a :exc:`KeyError`::
>>> c = Counter(['eggs', 'ham'])
>>> c['bacon'] # count of a missing element is zero
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, :func:`most_common`
returns *all* elements in the counter. 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 counts
instead of replacing them. Also, the *iterable* is expected to be a
sequence of elements, not a sequence of ``(key, value)`` pairs.
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 mathematical operations are provided for combining :class:`Counter`
objects to produce multisets (counters that have counts greater than zero).
Addition and subtraction combine counters by adding or subtracting the counts
of corresponding elements. Intersection and union return the minimum and
maximum of corresponding counts. Each operation can accept inputs with signed
counts, but the output will exclude results with counts of zero or less.
>>> c = Counter(a=3, b=1)
>>> d = Counter(a=1, b=2)
>>> c + d # add two counters together: c[x] + d[x]
Counter({'a': 4, 'b': 3})
>>> c - d # subtract (keeping only positive counts)
Counter({'a': 2})
>>> c & d # intersection: min(c[x], d[x])
Counter({'a': 1, 'b': 1})
>>> c | d # union: max(c[x], d[x])
Counter({'a': 3, 'b': 2})
.. seealso::
* `Counter class <http://code.activestate.com/recipes/576611/>`_
adapted for Python 2.5 and an early `Bag recipe
<http://code.activestate.com/recipes/259174/>`_ for Python 2.4.
* `Bag class <http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html>`_
in Smalltalk.
* Wikipedia entry for `Multisets <http://en.wikipedia.org/wiki/Multiset>`_\.
* `C++ multisets <http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm>`_
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 :func:`itertools.combinations_with_replacement`.
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.
.. versionadded:: 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.
.. versionchanged:: 2.6
Added *maxlen* parameter.
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`.
.. versionadded:: 2.5
.. 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 "<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'])
: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.
.. versionadded:: 2.5
: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 :func:`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 :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], [rename])
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*, *print*,
or *raise*.
If *rename* is true, invalid fieldnames are automatically replaced
with positional names. For example, ``['abc', 'def', 'ghi', 'abc']`` is
converted to ``['abc', '_2', 'ghi', '_4']``, eliminating the keyword
``def`` and the duplicate fieldname ``abc``.
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.
.. versionadded:: 2.6
.. versionchanged:: 2.7
added support for *rename*.
Example:
.. doctest::
:options: +NORMALIZE_WHITESPACE
>>> Point = namedtuple('Point', 'x y', verbose=True)
class Point(tuple):
'Point(x, y)'
<BLANKLINE>
__slots__ = ()
<BLANKLINE>
_fields = ('x', 'y')
<BLANKLINE>
def __new__(cls, x, y):
return tuple.__new__(cls, (x, y))
<BLANKLINE>
@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
<BLANKLINE>
def __repr__(self):
return 'Point(x=%r, y=%r)' % self
<BLANKLINE>
def _asdict(t):
'Return a new dict which maps field names to their values'
return {'x': t[0], 'y': t[1]}
<BLANKLINE>
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
<BLANKLINE>
def __getnewargs__(self):
return tuple(self)
<BLANKLINE>
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
(as described in :ref:`tut-unpacking-arguments`):
>>> d = {'x': 11, 'y': 22}
>>> Point(**d)
Point(x=11, y=22)
Since a named tuple is a regular Python class, it is easy to add or change
functionality with a subclass. Here is how to add a calculated field and
a fixed-width print format:
>>> class Point(namedtuple('Point', 'x y')):
... __slots__ = ()
... @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('<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)
.. seealso::
`Named tuple recipe <http://code.activestate.com/recipes/500261/>`_
adapted for Python 2.4.
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