:mod:`collections` --- High-performance container datatypes =========================================================== .. module:: collections :synopsis: High-performance datatypes .. moduleauthor:: Raymond Hettinger .. sectionauthor:: Raymond Hettinger This module implements high-performance container datatypes. Currently, there are two datatypes, :class:`deque` and :class:`defaultdict`, and one datatype factory function, :func:`namedtuple`. Python already includes built-in containers, :class:`dict`, :class:`list`, :class:`set`, and :class:`tuple`. In addition, the optional :mod:`bsddb` module has a :meth:`bsddb.btopen` method that can be used to create in-memory or file based ordered dictionaries with string keys. Future editions of the standard library may include balanced trees and ordered dictionaries. 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. The ABCs provided include those in the following table: ===================================== ======================================== ABC Notes ===================================== ======================================== :class:`collections.Container` Defines ``__contains__()`` :class:`collections.Hashable` Defines ``__hash__()`` :class:`collections.Iterable` Defines ``__iter__()`` :class:`collections.Iterator` Derived from :class:`Iterable` and in addition defines ``__next__()`` :class:`collections.Mapping` Derived from :class:`Container`, :class:`Iterable`, and :class:`Sized`, and in addition defines ``__getitem__()``, ``get()``, ``__contains__()``, ``__len__()``, ``__iter__()``, ``keys()``, ``items()``, and ``values()`` :class:`collections.MutableMapping` Derived from :class:`Mapping` :class:`collections.MutableSequence` Derived from :class:`Sequence` :class:`collections.MutableSet` Derived from :class:`Set` and in addition defines ``add()``, ``clear()``, ``discard()``, ``pop()``, and ``toggle()`` :class:`collections.Sequence` Derived from :class:`Container`, :class:`Iterable`, and :class:`Sized`, and in addition defines ``__getitem__()`` :class:`collections.Set` Derived from :class:`Container`, :class:`Iterable`, and :class:`Sized` :class:`collections.Sized` Defines ``__len__()`` ===================================== ======================================== .. XXX Have not included them all and the notes are imcomplete .. Deliberately did one row wide to get a neater output These ABCs allow us to ask classes or instances if they provide particular functionality, for example:: from collections import Sized size = None if isinstance(myvar, Sized): size = len(myvar) (For more about ABCs, see the :mod:`abc` module and :pep:`3119`.) .. _deque-objects: :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. .. versionchanged:: 2.6 Added *maxlen* Deque objects support the following methods: .. method:: deque.append(x) Add *x* to the right side of the deque. .. method:: deque.appendleft(x) Add *x* to the left side of the deque. .. method:: deque.clear() Remove all elements from the deque leaving it with length 0. .. method:: deque.extend(iterable) Extend the right side of the deque by appending elements from the iterable argument. .. method:: deque.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:: deque.pop() Remove and return an element from the right side of the deque. If no elements are present, raises an :exc:`IndexError`. .. method:: deque.popleft() Remove and return an element from the left side of the deque. If no elements are present, raises an :exc:`IndexError`. .. method:: deque.remove(value) Removed the first occurrence of *value*. If not found, raises a :exc:`ValueError`. .. method:: deque.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]``. 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 "", 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: :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) .. _defaultdict-objects: :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 an :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. .. _defaultdict-examples: :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]))] .. _named-tuple-factory: :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, fieldnames, [verbose]) Returns a new tuple subclass named *typename*. The new subclass is used to create tuple-like objects that have fields accessable by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with typename and fieldnames) and a helpful :meth:`__repr__` method which lists the tuple contents in a ``name=value`` format. The *fieldnames* are a single string with each fieldname separated by whitespace and/or commas (for example 'x y' or 'x, y'). Alternatively, the *fieldnames* can be specified as a list of strings (such as ['x', 'y']). Any valid Python identifier may be used for a fieldname except for names starting and ending with double underscores. Valid identifiers consist of letters, digits, and underscores but do not start with a digit and cannot be a :mod:`keyword` such as *class*, *for*, *return*, *global*, *pass*, *print*, or *raise*. If *verbose* is true, will print the class definition. 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)) def __repr__(self): return 'Point(x=%r, y=%r)' % self def __asdict__(self): 'Return a new dict mapping field names to their values' return dict(zip(('x', 'y'), self)) def __replace__(self, **kwds): 'Return a new Point object replacing specified fields with new values' return Point(**dict(zip(('x', 'y'), self) + kwds.items())) 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 regular tuple (11, 22) 33 >>> x, y = p # unpack like a regular tuple >>> x, y (11, 22) >>> p.x + p.y # fields also accessable 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') from itertools import starmap import csv for record in starmap(EmployeeRecord, 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 starmap(EmployeeRecord, cursor.fetchall()): print emp.name, emp.title When casting a single record to a named tuple, use the star-operator [#]_ to unpack the values:: >>> t = [11, 22] >>> Point(*t) # the star-operator unpacks any iterable object Point(x=11, y=22) When casting a dictionary to a named tuple, use the double-star-operator:: >>> d = {'x': 11, 'y': 22} >>> Point(**d) Point(x=11, y=22) In addition to the methods inherited from tuples, named tuples support two additonal methods and a read-only attribute. .. 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], updated=time.now()) .. attribute:: somenamedtuple.__fields__ Return a tuple of strings listing the field names. This is 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)' Since a named tuple is a regular Python class, it is easy to add or change functionality. For example, the display format can be changed by overriding the :meth:`__repr__` method: :: >>> Point = namedtuple('Point', 'x y') >>> Point.__repr__ = lambda self: 'Point(%.3f, %.3f)' % self >>> Point(x=10, y=20) Point(10.000, 20.000) Default values can be implemented by starting with a prototype instance and customizing it with :meth:`__replace__`: :: >>> Account = namedtuple('Account', 'owner balance transaction_count') >>> model_account = Account('', 0.0, 0) >>> johns_account = model_account.__replace__(owner='John') .. rubric:: Footnotes .. [#] For information on the star-operator see :ref:`tut-unpacking-arguments` and :ref:`calls`.