Glossary

.. glossary::

 ``>>>``
 The default Python prompt of the interactive shell. Often seen for code
 examples which can be executed interactively in the interpreter.

 ``...``
 The default Python prompt of the interactive shell when entering the
 code for an indented code block, when within a pair of matching left and
 right delimiters (parentheses, square brackets, curly braces or triple
 quotes), or after specifying a decorator.

 2to3
 A tool that tries to convert Python 2.x code to Python 3.x code by
 handling most of the incompatibilities which can be detected by parsing the
 source and traversing the parse tree.

 2to3 is available in the standard library as :mod:`lib2to3`; a standalone
 entry point is provided as :file:`Tools/scripts/2to3`. See
 :ref:`2to3-reference`.

 abstract base class
 Abstract base classes complement :term:`duck-typing` by
 providing a way to define interfaces when other techniques like
 :func:`hasattr` would be clumsy or subtly wrong (for example with
 :ref:`magic methods <special-lookup>`). ABCs introduce virtual
 subclasses, which are classes that don't inherit from a class but are
 still recognized by :func:`isinstance` and :func:`issubclass`; see the
 :mod:`abc` module documentation. Python comes with many built-in ABCs for
 data structures (in the :mod:`collections.abc` module), numbers (in the
 :mod:`numbers` module), streams (in the :mod:`io` module), import finders
 and loaders (in the :mod:`importlib.abc` module). You can create your own
 ABCs with the :mod:`abc` module.

 annotation
 A label associated with a variable, a class
 attribute or a function parameter or return value,
 used by convention as a :term:`type hint`.

 Annotations of local variables cannot be accessed at runtime, but
 annotations of global variables, class attributes, and functions
 are stored in the :attr:`__annotations__`
 special attribute of modules, classes, and functions,
 respectively.

 See :term:`variable annotation`, :term:`function annotation`, :pep:`484`
 and :pep:`526`, which describe this functionality.

 argument
 A value passed to a :term:`function` (or :term:`method`) when calling the
 function. There are two kinds of argument:

 * :dfn:`keyword argument`: an argument preceded by an identifier (e.g.
 ``name=``) in a function call or passed as a value in a dictionary
 preceded by ``**``. For example, ``3`` and ``5`` are both keyword
 arguments in the following calls to :func:`complex`::

 complex(real=3, imag=5)
 complex(**{'real': 3, 'imag': 5})

 * :dfn:`positional argument`: an argument that is not a keyword argument.
 Positional arguments can appear at the beginning of an argument list
 and/or be passed as elements of an :term:`iterable` preceded by ``*``.
 For example, ``3`` and ``5`` are both positional arguments in the
 following calls::

 complex(3, 5)
 complex(*(3, 5))

 Arguments are assigned to the named local variables in a function body.
 See the :ref:`calls` section for the rules governing this assignment.
 Syntactically, any expression can be used to represent an argument; the
 evaluated value is assigned to the local variable.

 See also the :term:`parameter` glossary entry, the FAQ question on
 :ref:`the difference between arguments and parameters
 <faq-argument-vs-parameter>`, and :pep:`362`.

 asynchronous context manager
 An object which controls the environment seen in an
 :keyword:`async with` statement by defining :meth:`__aenter__` and
 :meth:`__aexit__` methods. Introduced by :pep:`492`.

 asynchronous generator
 A function which returns an :term:`asynchronous generator iterator`. It
 looks like a coroutine function defined with :keyword:`async def` except
 that it contains :keyword:`yield` expressions for producing a series of
 values usable in an :keyword:`async for` loop.

 Usually refers to an asynchronous generator function, but may refer to an
 *asynchronous generator iterator* in some contexts. In cases where the
 intended meaning isn't clear, using the full terms avoids ambiguity.

 An asynchronous generator function may contain :keyword:`await`
 expressions as well as :keyword:`async for`, and :keyword:`async with`
 statements.

 asynchronous generator iterator
 An object created by a :term:`asynchronous generator` function.

 This is an :term:`asynchronous iterator` which when called using the
 :meth:`__anext__` method returns an awaitable object which will execute
 the body of the asynchronous generator function until the next
 :keyword:`yield` expression.

 Each :keyword:`yield` temporarily suspends processing, remembering the
 location execution state (including local variables and pending
 try-statements). When the *asynchronous generator iterator* effectively
 resumes with another awaitable returned by :meth:`__anext__`, it
 picks up where it left off. See :pep:`492` and :pep:`525`.

 asynchronous iterable
 An object, that can be used in an :keyword:`async for` statement.
 Must return an :term:`asynchronous iterator` from its
 :meth:`__aiter__` method. Introduced by :pep:`492`.

 asynchronous iterator
 An object that implements the :meth:`__aiter__` and :meth:`__anext__`
 methods. ``__anext__`` must return an :term:`awaitable` object.
 :keyword:`async for` resolves the awaitables returned by an asynchronous
 iterator's :meth:`__anext__` method until it raises a
 :exc:`StopAsyncIteration` exception. Introduced by :pep:`492`.

 attribute
 A value associated with an object which is referenced by name using
 dotted expressions. For example, if an object *o* has an attribute
 *a* it would be referenced as *o.a*.

 awaitable
 An object that can be used in an :keyword:`await` expression. Can be
 a :term:`coroutine` or an object with an :meth:`__await__` method.
 See also :pep:`492`.

 BDFL
 Benevolent Dictator For Life, a.k.a. `Guido van Rossum
 <https://gvanrossum.github.io/>`_, Python's creator.

 binary file
 A :term:`file object` able to read and write
 :term:`bytes-like objects <bytes-like object>`.
 Examples of binary files are files opened in binary mode (``'rb'``,
 ``'wb'`` or ``'rb+'``), :data:`sys.stdin.buffer`,
 :data:`sys.stdout.buffer`, and instances of :class:`io.BytesIO` and
 :class:`gzip.GzipFile`.

 See also :term:`text file` for a file object able to read and write
 :class:`str` objects.

 bytes-like object
 An object that supports the :ref:`bufferobjects` and can
 export a C-:term:`contiguous` buffer. This includes all :class:`bytes`,
 :class:`bytearray`, and :class:`array.array` objects, as well as many
 common :class:`memoryview` objects. Bytes-like objects can
 be used for various operations that work with binary data; these include
 compression, saving to a binary file, and sending over a socket.

 Some operations need the binary data to be mutable. The documentation
 often refers to these as "read-write bytes-like objects". Example
 mutable buffer objects include :class:`bytearray` and a
 :class:`memoryview` of a :class:`bytearray`.
 Other operations require the binary data to be stored in
 immutable objects ("read-only bytes-like objects"); examples
 of these include :class:`bytes` and a :class:`memoryview`
 of a :class:`bytes` object.

 bytecode
 Python source code is compiled into bytecode, the internal representation
 of a Python program in the CPython interpreter. The bytecode is also
 cached in ``.pyc`` files so that executing the same file is
 faster the second time (recompilation from source to bytecode can be
 avoided). This "intermediate language" is said to run on a
 :term:`virtual machine` that executes the machine code corresponding to
 each bytecode. Do note that bytecodes are not expected to work between
 different Python virtual machines, nor to be stable between Python
 releases.

 A list of bytecode instructions can be found in the documentation for
 :ref:`the dis module <bytecodes>`.

 class
 A template for creating user-defined objects. Class definitions
 normally contain method definitions which operate on instances of the
 class.

 class variable
 A variable defined in a class and intended to be modified only at
 class level (i.e., not in an instance of the class).

 coercion
 The implicit conversion of an instance of one type to another during an
 operation which involves two arguments of the same type. For example,
 ``int(3.15)`` converts the floating point number to the integer ``3``, but
 in ``3+4.5``, each argument is of a different type (one int, one float),
 and both must be converted to the same type before they can be added or it
 will raise a :exc:`TypeError`. Without coercion, all arguments of even
 compatible types would have to be normalized to the same value by the
 programmer, e.g., ``float(3)+4.5`` rather than just ``3+4.5``.

 complex number
 An extension of the familiar real number system in which all numbers are
 expressed as a sum of a real part and an imaginary part. Imaginary
 numbers are real multiples of the imaginary unit (the square root of
 ``-1``), often written ``i`` in mathematics or ``j`` in
 engineering. Python has built-in support for complex numbers, which are
 written with this latter notation; the imaginary part is written with a
 ``j`` suffix, e.g., ``3+1j``. To get access to complex equivalents of the
 :mod:`math` module, use :mod:`cmath`. Use of complex numbers is a fairly
 advanced mathematical feature. If you're not aware of a need for them,
 it's almost certain you can safely ignore them.

 context manager
 An object which controls the environment seen in a :keyword:`with`
 statement by defining :meth:`__enter__` and :meth:`__exit__` methods.
 See :pep:`343`.

 context variable
 A variable which can have different values depending on its context.
 This is similar to Thread-Local Storage in which each execution
 thread may have a different value for a variable. However, with context
 variables, there may be several contexts in one execution thread and the
 main usage for context variables is to keep track of variables in
 concurrent asynchronous tasks.
 See :mod:`contextvars`.

 contiguous
 .. index:: C-contiguous, Fortran contiguous

 A buffer is considered contiguous exactly if it is either
 *C-contiguous* or *Fortran contiguous*. Zero-dimensional buffers are
 C and Fortran contiguous. In one-dimensional arrays, the items
 must be laid out in memory next to each other, in order of
 increasing indexes starting from zero. In multidimensional
 C-contiguous arrays, the last index varies the fastest when
 visiting items in order of memory address. However, in
 Fortran contiguous arrays, the first index varies the fastest.

 coroutine
 Coroutines is a more generalized form of subroutines. Subroutines are
 entered at one point and exited at another point. Coroutines can be
 entered, exited, and resumed at many different points. They can be
 implemented with the :keyword:`async def` statement. See also
 :pep:`492`.

 coroutine function
 A function which returns a :term:`coroutine` object. A coroutine
 function may be defined with the :keyword:`async def` statement,
 and may contain :keyword:`await`, :keyword:`async for`, and
 :keyword:`async with` keywords. These were introduced
 by :pep:`492`.

 CPython
 The canonical implementation of the Python programming language, as
 distributed on `python.org <https://www.python.org>`_. The term "CPython"
 is used when necessary to distinguish this implementation from others
 such as Jython or IronPython.

 decorator
 A function returning another function, usually applied as a function
 transformation using the ``@wrapper`` syntax. Common examples for
 decorators are :func:`classmethod` and :func:`staticmethod`.

 The decorator syntax is merely syntactic sugar, the following two
 function definitions are semantically equivalent::

 def f(...):
 ...
 f = staticmethod(f)

 @staticmethod
 def f(...):
 ...

 The same concept exists for classes, but is less commonly used there. See
 the documentation for :ref:`function definitions <function>` and
 :ref:`class definitions <class>` for more about decorators.

 descriptor
 Any object which defines the methods :meth:`__get__`, :meth:`__set__`, or
 :meth:`__delete__`. When a class attribute is a descriptor, its special
 binding behavior is triggered upon attribute lookup. Normally, using
 *a.b* to get, set or delete an attribute looks up the object named *b* in
 the class dictionary for *a*, but if *b* is a descriptor, the respective
 descriptor method gets called. Understanding descriptors is a key to a
 deep understanding of Python because they are the basis for many features
 including functions, methods, properties, class methods, static methods,
 and reference to super classes.

 For more information about descriptors' methods, see :ref:`descriptors`.

 dictionary
 An associative array, where arbitrary keys are mapped to values. The
 keys can be any object with :meth:`__hash__` and :meth:`__eq__` methods.
 Called a hash in Perl.

 dictionary view
 The objects returned from :meth:`dict.keys`, :meth:`dict.values`, and
 :meth:`dict.items` are called dictionary views. They provide a dynamic
 view on the dictionary’s entries, which means that when the dictionary
 changes, the view reflects these changes. To force the
 dictionary view to become a full list use ``list(dictview)``. See
 :ref:`dict-views`.

 docstring
 A string literal which appears as the first expression in a class,
 function or module. While ignored when the suite is executed, it is
 recognized by the compiler and put into the :attr:`__doc__` attribute
 of the enclosing class, function or module. Since it is available via
 introspection, it is the canonical place for documentation of the
 object.

 duck-typing
 A programming style which does not look at an object's type to determine
 if it has the right interface; instead, the method or attribute is simply
 called or used ("If it looks like a duck and quacks like a duck, it
 must be a duck.") By emphasizing interfaces rather than specific types,
 well-designed code improves its flexibility by allowing polymorphic
 substitution. Duck-typing avoids tests using :func:`type` or
 :func:`isinstance`. (Note, however, that duck-typing can be complemented
 with :term:`abstract base classes <abstract base class>`.) Instead, it
 typically employs :func:`hasattr` tests or :term:`EAFP` programming.

 EAFP
 Easier to ask for forgiveness than permission. This common Python coding
 style assumes the existence of valid keys or attributes and catches
 exceptions if the assumption proves false. This clean and fast style is
 characterized by the presence of many :keyword:`try` and :keyword:`except`
 statements. The technique contrasts with the :term:`LBYL` style
 common to many other languages such as C.

 expression
 A piece of syntax which can be evaluated to some value. In other words,
 an expression is an accumulation of expression elements like literals,
 names, attribute access, operators or function calls which all return a
 value. In contrast to many other languages, not all language constructs
 are expressions. There are also :term:`statement`\s which cannot be used
 as expressions, such as :keyword:`while`. Assignments are also statements,
 not expressions.

 extension module
 A module written in C or C++, using Python's C API to interact with the
 core and with user code.

 f-string
 String literals prefixed with ``'f'`` or ``'F'`` are commonly called
 "f-strings" which is short for
 :ref:`formatted string literals <f-strings>`. See also :pep:`498`.

 file object
 An object exposing a file-oriented API (with methods such as
 :meth:`read()` or :meth:`write()`) to an underlying resource. Depending
 on the way it was created, a file object can mediate access to a real
 on-disk file or to another type of storage or communication device
 (for example standard input/output, in-memory buffers, sockets, pipes,
 etc.). File objects are also called :dfn:`file-like objects` or
 :dfn:`streams`.

 There are actually three categories of file objects: raw
 :term:`binary files <binary file>`, buffered
 :term:`binary files <binary file>` and :term:`text files <text file>`.
 Their interfaces are defined in the :mod:`io` module. The canonical
 way to create a file object is by using the :func:`open` function.

 file-like object
 A synonym for :term:`file object`.

 finder
 An object that tries to find the :term:`loader` for a module that is
 being imported.

 Since Python 3.3, there are two types of finder: :term:`meta path finders
 <meta path finder>` for use with :data:`sys.meta_path`, and :term:`path
 entry finders <path entry finder>` for use with :data:`sys.path_hooks`.

 See :pep:`302`, :pep:`420` and :pep:`451` for much more detail.

 floor division
 Mathematical division that rounds down to nearest integer. The floor
 division operator is ``//``. For example, the expression ``11 // 4``
 evaluates to ``2`` in contrast to the ``2.75`` returned by float true
 division. Note that ``(-11) // 4`` is ``-3`` because that is ``-2.75``
 rounded *downward*. See :pep:`238`.

 function
 A series of statements which returns some value to a caller. It can also
 be passed zero or more :term:`arguments <argument>` which may be used in
 the execution of the body. See also :term:`parameter`, :term:`method`,
 and the :ref:`function` section.

 function annotation
 An :term:`annotation` of a function parameter or return value.

 Function annotations are usually used for
 :term:`type hints <type hint>`: for example, this function is expected to take two
 :class:`int` arguments and is also expected to have an :class:`int`
 return value::

 def sum_two_numbers(a: int, b: int) -> int:
 return a + b

 Function annotation syntax is explained in section :ref:`function`.

 See :term:`variable annotation` and :pep:`484`,
 which describe this functionality.

 __future__
 A pseudo-module which programmers can use to enable new language features
 which are not compatible with the current interpreter.

 By importing the :mod:`__future__` module and evaluating its variables,
 you can see when a new feature was first added to the language and when it
 becomes the default::

 >>> import __future__
 >>> __future__.division
 _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)

 garbage collection
 The process of freeing memory when it is not used anymore. Python
 performs garbage collection via reference counting and a cyclic garbage
 collector that is able to detect and break reference cycles. The
 garbage collector can be controlled using the :mod:`gc` module.

 .. index:: single: generator

 generator
 A function which returns a :term:`generator iterator`. It looks like a
 normal function except that it contains :keyword:`yield` expressions
 for producing a series of values usable in a for-loop or that can be
 retrieved one at a time with the :func:`next` function.

 Usually refers to a generator function, but may refer to a
 *generator iterator* in some contexts. In cases where the intended
 meaning isn't clear, using the full terms avoids ambiguity.

 generator iterator
 An object created by a :term:`generator` function.

 Each :keyword:`yield` temporarily suspends processing, remembering the
 location execution state (including local variables and pending
 try-statements). When the *generator iterator* resumes, it picks up where
 it left off (in contrast to functions which start fresh on every
 invocation).

 .. index:: single: generator expression

 generator expression
 An expression that returns an iterator. It looks like a normal expression
 followed by a :keyword:`!for` clause defining a loop variable, range,
 and an optional :keyword:`!if` clause. The combined expression
 generates values for an enclosing function::

 >>> sum(i*i for i in range(10)) # sum of squares 0, 1, 4, ... 81
 285

 generic function
 A function composed of multiple functions implementing the same operation
 for different types. Which implementation should be used during a call is
 determined by the dispatch algorithm.

 See also the :term:`single dispatch` glossary entry, the
 :func:`functools.singledispatch` decorator, and :pep:`443`.


 GIL
 See :term:`global interpreter lock`.

 global interpreter lock
 The mechanism used by the :term:`CPython` interpreter to assure that
 only one thread executes Python :term:`bytecode` at a time.
 This simplifies the CPython implementation by making the object model
 (including critical built-in types such as :class:`dict`) implicitly
 safe against concurrent access. Locking the entire interpreter
 makes it easier for the interpreter to be multi-threaded, at the
 expense of much of the parallelism afforded by multi-processor
 machines.

 However, some extension modules, either standard or third-party,
 are designed so as to release the GIL when doing computationally-intensive
 tasks such as compression or hashing. Also, the GIL is always released
 when doing I/O.

 Past efforts to create a "free-threaded" interpreter (one which locks
 shared data at a much finer granularity) have not been successful
 because performance suffered in the common single-processor case. It
 is believed that overcoming this performance issue would make the
 implementation much more complicated and therefore costlier to maintain.


 hash-based pyc
 A bytecode cache file that uses the hash rather than the last-modified
 time of the corresponding source file to determine its validity. See
 :ref:`pyc-invalidation`.

 hashable
 An object is *hashable* if it has a hash value which never changes during
 its lifetime (it needs a :meth:`__hash__` method), and can be compared to
 other objects (it needs an :meth:`__eq__` method). Hashable objects which
 compare equal must have the same hash value.

 Hashability makes an object usable as a dictionary key and a set member,
 because these data structures use the hash value internally.

 Most of Python's immutable built-in objects are hashable; mutable
 containers (such as lists or dictionaries) are not; immutable
 containers (such as tuples and frozensets) are only hashable if
 their elements are hashable. Objects which are
 instances of user-defined classes are hashable by default. They all
 compare unequal (except with themselves), and their hash value is derived
 from their :func:`id`.

 IDLE
 An Integrated Development Environment for Python. IDLE is a basic editor
 and interpreter environment which ships with the standard distribution of
 Python.

 immutable
 An object with a fixed value. Immutable objects include numbers, strings and
 tuples. Such an object cannot be altered. A new object has to
 be created if a different value has to be stored. They play an important
 role in places where a constant hash value is needed, for example as a key
 in a dictionary.

 import path
 A list of locations (or :term:`path entries <path entry>`) that are
 searched by the :term:`path based finder` for modules to import. During
 import, this list of locations usually comes from :data:`sys.path`, but
 for subpackages it may also come from the parent package's ``__path__``
 attribute.

 importing
 The process by which Python code in one module is made available to
 Python code in another module.

 importer
 An object that both finds and loads a module; both a
 :term:`finder` and :term:`loader` object.

 interactive
 Python has an interactive interpreter which means you can enter
 statements and expressions at the interpreter prompt, immediately
 execute them and see their results. Just launch ``python`` with no
 arguments (possibly by selecting it from your computer's main
 menu). It is a very powerful way to test out new ideas or inspect
 modules and packages (remember ``help(x)``).

 interpreted
 Python is an interpreted language, as opposed to a compiled one,
 though the distinction can be blurry because of the presence of the
 bytecode compiler. This means that source files can be run directly
 without explicitly creating an executable which is then run.
 Interpreted languages typically have a shorter development/debug cycle
 than compiled ones, though their programs generally also run more
 slowly. See also :term:`interactive`.

 interpreter shutdown
 When asked to shut down, the Python interpreter enters a special phase
 where it gradually releases all allocated resources, such as modules
 and various critical internal structures. It also makes several calls
 to the :term:`garbage collector <garbage collection>`. This can trigger
 the execution of code in user-defined destructors or weakref callbacks.
 Code executed during the shutdown phase can encounter various
 exceptions as the resources it relies on may not function anymore
 (common examples are library modules or the warnings machinery).

 The main reason for interpreter shutdown is that the ``__main__`` module
 or the script being run has finished executing.

 iterable
 An object capable of returning its members one at a time. Examples of
 iterables include all sequence types (such as :class:`list`, :class:`str`,
 and :class:`tuple`) and some non-sequence types like :class:`dict`,
 :term:`file objects <file object>`, and objects of any classes you define
 with an :meth:`__iter__` method or with a :meth:`__getitem__` method
 that implements :term:`Sequence` semantics.

 Iterables can be
 used in a :keyword:`for` loop and in many other places where a sequence is
 needed (:func:`zip`, :func:`map`, ...). When an iterable object is passed
 as an argument to the built-in function :func:`iter`, it returns an
 iterator for the object. This iterator is good for one pass over the set
 of values. When using iterables, it is usually not necessary to call
 :func:`iter` or deal with iterator objects yourself. The ``for``
 statement does that automatically for you, creating a temporary unnamed
 variable to hold the iterator for the duration of the loop. See also
 :term:`iterator`, :term:`sequence`, and :term:`generator`.

 iterator
 An object representing a stream of data. Repeated calls to the iterator's
 :meth:`~iterator.__next__` method (or passing it to the built-in function
 :func:`next`) return successive items in the stream. When no more data
 are available a :exc:`StopIteration` exception is raised instead. At this
 point, the iterator object is exhausted and any further calls to its
 :meth:`__next__` method just raise :exc:`StopIteration` again. Iterators
 are required to have an :meth:`__iter__` method that returns the iterator
 object itself so every iterator is also iterable and may be used in most
 places where other iterables are accepted. One notable exception is code
 which attempts multiple iteration passes. A container object (such as a
 :class:`list`) produces a fresh new iterator each time you pass it to the
 :func:`iter` function or use it in a :keyword:`for` loop. Attempting this
 with an iterator will just return the same exhausted iterator object used
 in the previous iteration pass, making it appear like an empty container.

 More information can be found in :ref:`typeiter`.

 key function
 A key function or collation function is a callable that returns a value
 used for sorting or ordering. For example, :func:`locale.strxfrm` is
 used to produce a sort key that is aware of locale specific sort
 conventions.

 A number of tools in Python accept key functions to control how elements
 are ordered or grouped. They include :func:`min`, :func:`max`,
 :func:`sorted`, :meth:`list.sort`, :func:`heapq.merge`,
 :func:`heapq.nsmallest`, :func:`heapq.nlargest`, and
 :func:`itertools.groupby`.

 There are several ways to create a key function. For example. the
 :meth:`str.lower` method can serve as a key function for case insensitive
 sorts. Alternatively, a key function can be built from a
 :keyword:`lambda` expression such as ``lambda r: (r[0], r[2])``. Also,
 the :mod:`operator` module provides three key function constructors:
 :func:`~operator.attrgetter`, :func:`~operator.itemgetter`, and
 :func:`~operator.methodcaller`. See the :ref:`Sorting HOW TO
 <sortinghowto>` for examples of how to create and use key functions.

 keyword argument
 See :term:`argument`.

 lambda
 An anonymous inline function consisting of a single :term:`expression`
 which is evaluated when the function is called. The syntax to create
 a lambda function is ``lambda [parameters]: expression``

 LBYL
 Look before you leap. This coding style explicitly tests for
 pre-conditions before making calls or lookups. This style contrasts with
 the :term:`EAFP` approach and is characterized by the presence of many
 :keyword:`if` statements.

 In a multi-threaded environment, the LBYL approach can risk introducing a
 race condition between "the looking" and "the leaping". For example, the
 code, ``if key in mapping: return mapping[key]`` can fail if another
 thread removes *key* from *mapping* after the test, but before the lookup.
 This issue can be solved with locks or by using the EAFP approach.

 list
 A built-in Python :term:`sequence`. Despite its name it is more akin
 to an array in other languages than to a linked list since access to
 elements is O(1).

 list comprehension
 A compact way to process all or part of the elements in a sequence and
 return a list with the results. ``result = ['{:#04x}'.format(x) for x in
 range(256) if x % 2 == 0]`` generates a list of strings containing
 even hex numbers (0x..) in the range from 0 to 255. The :keyword:`if`
 clause is optional. If omitted, all elements in ``range(256)`` are
 processed.

 loader
 An object that loads a module. It must define a method named
 :meth:`load_module`. A loader is typically returned by a
 :term:`finder`. See :pep:`302` for details and
 :class:`importlib.abc.Loader` for an :term:`abstract base class`.

 magic method
 .. index:: pair: magic; method

 An informal synonym for :term:`special method`.

 mapping
 A container object that supports arbitrary key lookups and implements the
 methods specified in the :class:`~collections.abc.Mapping` or
 :class:`~collections.abc.MutableMapping`
 :ref:`abstract base classes <collections-abstract-base-classes>`. Examples
 include :class:`dict`, :class:`collections.defaultdict`,
 :class:`collections.OrderedDict` and :class:`collections.Counter`.

 meta path finder
 A :term:`finder` returned by a search of :data:`sys.meta_path`. Meta path
 finders are related to, but different from :term:`path entry finders
 <path entry finder>`.

 See :class:`importlib.abc.MetaPathFinder` for the methods that meta path
 finders implement.

 metaclass
 The class of a class. Class definitions create a class name, a class
 dictionary, and a list of base classes. The metaclass is responsible for
 taking those three arguments and creating the class. Most object oriented
 programming languages provide a default implementation. What makes Python
 special is that it is possible to create custom metaclasses. Most users
 never need this tool, but when the need arises, metaclasses can provide
 powerful, elegant solutions. They have been used for logging attribute
 access, adding thread-safety, tracking object creation, implementing
 singletons, and many other tasks.

 More information can be found in :ref:`metaclasses`.

 method
 A function which is defined inside a class body. If called as an attribute
 of an instance of that class, the method will get the instance object as
 its first :term:`argument` (which is usually called ``self``).
 See :term:`function` and :term:`nested scope`.

 method resolution order
 Method Resolution Order is the order in which base classes are searched
 for a member during lookup. See `The Python 2.3 Method Resolution Order
 <https://www.python.org/download/releases/2.3/mro/>`_ for details of the
 algorithm used by the Python interpreter since the 2.3 release.

 module
 An object that serves as an organizational unit of Python code. Modules
 have a namespace containing arbitrary Python objects. Modules are loaded
 into Python by the process of :term:`importing`.

 See also :term:`package`.

 module spec
 A namespace containing the import-related information used to load a
 module. An instance of :class:`importlib.machinery.ModuleSpec`.

 MRO
 See :term:`method resolution order`.

 mutable
 Mutable objects can change their value but keep their :func:`id`. See
 also :term:`immutable`.

 named tuple
 Any tuple-like class whose indexable elements are also accessible using
 named attributes (for example, :func:`time.localtime` returns a
 tuple-like object where the *year* is accessible either with an
 index such as ``t[0]`` or with a named attribute like ``t.tm_year``).

 A named tuple can be a built-in type such as :class:`time.struct_time`,
 or it can be created with a regular class definition. A full featured
 named tuple can also be created with the factory function
 :func:`collections.namedtuple`. The latter approach automatically
 provides extra features such as a self-documenting representation like
 ``Employee(name='jones', title='programmer')``.

 namespace
 The place where a variable is stored. Namespaces are implemented as
 dictionaries. There are the local, global and built-in namespaces as well
 as nested namespaces in objects (in methods). Namespaces support
 modularity by preventing naming conflicts. For instance, the functions
 :func:`builtins.open <.open>` and :func:`os.open` are distinguished by
 their namespaces. Namespaces also aid readability and maintainability by
 making it clear which module implements a function. For instance, writing
 :func:`random.seed` or :func:`itertools.islice` makes it clear that those
 functions are implemented by the :mod:`random` and :mod:`itertools`
 modules, respectively.

 namespace package
 A :pep:`420` :term:`package` which serves only as a container for
 subpackages. Namespace packages may have no physical representation,
 and specifically are not like a :term:`regular package` because they
 have no ``__init__.py`` file.

 See also :term:`module`.

 nested scope
 The ability to refer to a variable in an enclosing definition. For
 instance, a function defined inside another function can refer to
 variables in the outer function. Note that nested scopes by default work
 only for reference and not for assignment. Local variables both read and
 write in the innermost scope. Likewise, global variables read and write
 to the global namespace. The :keyword:`nonlocal` allows writing to outer
 scopes.

 new-style class
 Old name for the flavor of classes now used for all class objects. In
 earlier Python versions, only new-style classes could use Python's newer,
 versatile features like :attr:`~object.__slots__`, descriptors,
 properties, :meth:`__getattribute__`, class methods, and static methods.

 object
 Any data with state (attributes or value) and defined behavior
 (methods). Also the ultimate base class of any :term:`new-style
 class`.

 package
 A Python :term:`module` which can contain submodules or recursively,
 subpackages. Technically, a package is a Python module with an
 ``__path__`` attribute.

 See also :term:`regular package` and :term:`namespace package`.

 parameter
 A named entity in a :term:`function` (or method) definition that
 specifies an :term:`argument` (or in some cases, arguments) that the
 function can accept. There are five kinds of parameter:

 * :dfn:`positional-or-keyword`: specifies an argument that can be passed
 either :term:`positionally <argument>` or as a :term:`keyword argument
 <argument>`. This is the default kind of parameter, for example *foo*
 and *bar* in the following::

 def func(foo, bar=None): ...

 .. _positional-only_parameter:

 * :dfn:`positional-only`: specifies an argument that can be supplied only
 by position. Python has no syntax for defining positional-only
 parameters. However, some built-in functions have positional-only
 parameters (e.g. :func:`abs`).

 .. _keyword-only_parameter:

 * :dfn:`keyword-only`: specifies an argument that can be supplied only
 by keyword. Keyword-only parameters can be defined by including a
 single var-positional parameter or bare ``*`` in the parameter list
 of the function definition before them, for example *kw_only1* and
 *kw_only2* in the following::

 def func(arg, *, kw_only1, kw_only2): ...

 * :dfn:`var-positional`: specifies that an arbitrary sequence of
 positional arguments can be provided (in addition to any positional
 arguments already accepted by other parameters). Such a parameter can
 be defined by prepending the parameter name with ``*``, for example
 *args* in the following::

 def func(*args, **kwargs): ...

 * :dfn:`var-keyword`: specifies that arbitrarily many keyword arguments
 can be provided (in addition to any keyword arguments already accepted
 by other parameters). Such a parameter can be defined by prepending
 the parameter name with ``**``, for example *kwargs* in the example
 above.

 Parameters can specify both optional and required arguments, as well as
 default values for some optional arguments.

 See also the :term:`argument` glossary entry, the FAQ question on
 :ref:`the difference between arguments and parameters
 <faq-argument-vs-parameter>`, the :class:`inspect.Parameter` class, the
 :ref:`function` section, and :pep:`362`.

 path entry
 A single location on the :term:`import path` which the :term:`path
 based finder` consults to find modules for importing.

 path entry finder
 A :term:`finder` returned by a callable on :data:`sys.path_hooks`
 (i.e. a :term:`path entry hook`) which knows how to locate modules given
 a :term:`path entry`.

 See :class:`importlib.abc.PathEntryFinder` for the methods that path entry
 finders implement.

 path entry hook
 A callable on the :data:`sys.path_hook` list which returns a :term:`path
 entry finder` if it knows how to find modules on a specific :term:`path
 entry`.

 path based finder
 One of the default :term:`meta path finders <meta path finder>` which
 searches an :term:`import path` for modules.

 path-like object
 An object representing a file system path. A path-like object is either
 a :class:`str` or :class:`bytes` object representing a path, or an object
 implementing the :class:`os.PathLike` protocol. An object that supports
 the :class:`os.PathLike` protocol can be converted to a :class:`str` or
 :class:`bytes` file system path by calling the :func:`os.fspath` function;
 :func:`os.fsdecode` and :func:`os.fsencode` can be used to guarantee a
 :class:`str` or :class:`bytes` result instead, respectively. Introduced
 by :pep:`519`.

 PEP
 Python Enhancement Proposal. A PEP is a design document
 providing information to the Python community, or describing a new
 feature for Python or its processes or environment. PEPs should
 provide a concise technical specification and a rationale for proposed
 features.

 PEPs are intended to be the primary mechanisms for proposing major new
 features, for collecting community input on an issue, and for documenting
 the design decisions that have gone into Python. The PEP author is
 responsible for building consensus within the community and documenting
 dissenting opinions.

 See :pep:`1`.

 portion
 A set of files in a single directory (possibly stored in a zip file)
 that contribute to a namespace package, as defined in :pep:`420`.

 positional argument
 See :term:`argument`.

 provisional API
 A provisional API is one which has been deliberately excluded from
 the standard library's backwards compatibility guarantees. While major
 changes to such interfaces are not expected, as long as they are marked
 provisional, backwards incompatible changes (up to and including removal
 of the interface) may occur if deemed necessary by core developers. Such
 changes will not be made gratuitously -- they will occur only if serious
 fundamental flaws are uncovered that were missed prior to the inclusion
 of the API.

 Even for provisional APIs, backwards incompatible changes are seen as
 a "solution of last resort" - every attempt will still be made to find
 a backwards compatible resolution to any identified problems.

 This process allows the standard library to continue to evolve over
 time, without locking in problematic design errors for extended periods
 of time. See :pep:`411` for more details.

 provisional package
 See :term:`provisional API`.

 Python 3000
 Nickname for the Python 3.x release line (coined long ago when the
 release of version 3 was something in the distant future.) This is also
 abbreviated "Py3k".

 Pythonic
 An idea or piece of code which closely follows the most common idioms
 of the Python language, rather than implementing code using concepts
 common to other languages. For example, a common idiom in Python is
 to loop over all elements of an iterable using a :keyword:`for`
 statement. Many other languages don't have this type of construct, so
 people unfamiliar with Python sometimes use a numerical counter instead::

 for i in range(len(food)):
 print(food[i])

 As opposed to the cleaner, Pythonic method::

 for piece in food:
 print(piece)

 qualified name
 A dotted name showing the "path" from a module's global scope to a
 class, function or method defined in that module, as defined in
 :pep:`3155`. For top-level functions and classes, the qualified name
 is the same as the object's name::

 >>> class C:
 ... class D:
 ... def meth(self):
 ... pass
 ...
 >>> C.__qualname__
 'C'
 >>> C.D.__qualname__
 'C.D'
 >>> C.D.meth.__qualname__
 'C.D.meth'

 When used to refer to modules, the *fully qualified name* means the
 entire dotted path to the module, including any parent packages,
 e.g. ``email.mime.text``::

 >>> import email.mime.text
 >>> email.mime.text.__name__
 'email.mime.text'

 reference count
 The number of references to an object. When the reference count of an
 object drops to zero, it is deallocated. Reference counting is
 generally not visible to Python code, but it is a key element of the
 :term:`CPython` implementation. The :mod:`sys` module defines a
 :func:`~sys.getrefcount` function that programmers can call to return the
 reference count for a particular object.

 regular package
 A traditional :term:`package`, such as a directory containing an
 ``__init__.py`` file.

 See also :term:`namespace package`.

 __slots__
 A declaration inside a class that saves memory by pre-declaring space for
 instance attributes and eliminating instance dictionaries. Though
 popular, the technique is somewhat tricky to get right and is best
 reserved for rare cases where there are large numbers of instances in a
 memory-critical application.

 sequence
 An :term:`iterable` which supports efficient element access using integer
 indices via the :meth:`__getitem__` special method and defines a
 :meth:`__len__` method that returns the length of the sequence.
 Some built-in sequence types are :class:`list`, :class:`str`,
 :class:`tuple`, and :class:`bytes`. Note that :class:`dict` also
 supports :meth:`__getitem__` and :meth:`__len__`, but is considered a
 mapping rather than a sequence because the lookups use arbitrary
 :term:`immutable` keys rather than integers.

 The :class:`collections.abc.Sequence` abstract base class
 defines a much richer interface that goes beyond just
 :meth:`__getitem__` and :meth:`__len__`, adding :meth:`count`,
 :meth:`index`, :meth:`__contains__`, and
 :meth:`__reversed__`. Types that implement this expanded
 interface can be registered explicitly using
 :func:`~abc.register`.

 single dispatch
 A form of :term:`generic function` dispatch where the implementation is
 chosen based on the type of a single argument.

 slice
 An object usually containing a portion of a :term:`sequence`. A slice is
 created using the subscript notation, ``[]`` with colons between numbers
 when several are given, such as in ``variable_name[1:3:5]``. The bracket
 (subscript) notation uses :class:`slice` objects internally.

 special method
 .. index:: pair: special; method

 A method that is called implicitly by Python to execute a certain
 operation on a type, such as addition. Such methods have names starting
 and ending with double underscores. Special methods are documented in
 :ref:`specialnames`.

 statement
 A statement is part of a suite (a "block" of code). A statement is either
 an :term:`expression` or one of several constructs with a keyword, such
 as :keyword:`if`, :keyword:`while` or :keyword:`for`.

 struct sequence
 A tuple with named elements. Struct sequences expose an interface similar
 to :term:`named tuple` in that elements can be accessed either by
 index or as an attribute. However, they do not have any of the named tuple
 methods like :meth:`~collections.somenamedtuple._make` or
 :meth:`~collections.somenamedtuple._asdict`. Examples of struct sequences
 include :data:`sys.float_info` and the return value of :func:`os.stat`.

 text encoding
 A codec which encodes Unicode strings to bytes.

 text file
 A :term:`file object` able to read and write :class:`str` objects.
 Often, a text file actually accesses a byte-oriented datastream
 and handles the :term:`text encoding` automatically.
 Examples of text files are files opened in text mode (``'r'`` or ``'w'``),
 :data:`sys.stdin`, :data:`sys.stdout`, and instances of
 :class:`io.StringIO`.

 See also :term:`binary file` for a file object able to read and write
 :term:`bytes-like objects <bytes-like object>`.

 triple-quoted string
 A string which is bound by three instances of either a quotation mark
 (") or an apostrophe ('). While they don't provide any functionality
 not available with single-quoted strings, they are useful for a number
 of reasons. They allow you to include unescaped single and double
 quotes within a string and they can span multiple lines without the
 use of the continuation character, making them especially useful when
 writing docstrings.

 type
 The type of a Python object determines what kind of object it is; every
 object has a type. An object's type is accessible as its
 :attr:`~instance.__class__` attribute or can be retrieved with
 ``type(obj)``.

 type alias
 A synonym for a type, created by assigning the type to an identifier.

 Type aliases are useful for simplifying :term:`type hints <type hint>`.
 For example::

 from typing import List, Tuple

 def remove_gray_shades(
 colors: List[Tuple[int, int, int]]) -> List[Tuple[int, int, int]]:
 pass

 could be made more readable like this::

 from typing import List, Tuple

 Color = Tuple[int, int, int]

 def remove_gray_shades(colors: List[Color]) -> List[Color]:
 pass

 See :mod:`typing` and :pep:`484`, which describe this functionality.

 type hint
 An :term:`annotation` that specifies the expected type for a variable, a class
 attribute, or a function parameter or return value.

 Type hints are optional and are not enforced by Python but
 they are useful to static type analysis tools, and aid IDEs with code
 completion and refactoring.

 Type hints of global variables, class attributes, and functions,
 but not local variables, can be accessed using
 :func:`typing.get_type_hints`.

 See :mod:`typing` and :pep:`484`, which describe this functionality.

 universal newlines
 A manner of interpreting text streams in which all of the following are
 recognized as ending a line: the Unix end-of-line convention ``'\n'``,
 the Windows convention ``'\r\n'``, and the old Macintosh convention
 ``'\r'``. See :pep:`278` and :pep:`3116`, as well as
 :func:`bytes.splitlines` for an additional use.

 variable annotation
 An :term:`annotation` of a variable or a class attribute.

 When annotating a variable or a class attribute, assignment is optional::

 class C:
 field: 'annotation'

 Variable annotations are usually used for
 :term:`type hints <type hint>`: for example this variable is expected to take
 :class:`int` values::

 count: int = 0

 Variable annotation syntax is explained in section :ref:`annassign`.

 See :term:`function annotation`, :pep:`484`
 and :pep:`526`, which describe this functionality.

 virtual environment
 A cooperatively isolated runtime environment that allows Python users
 and applications to install and upgrade Python distribution packages
 without interfering with the behaviour of other Python applications
 running on the same system.

 See also :mod:`venv`.

 virtual machine
 A computer defined entirely in software. Python's virtual machine
 executes the :term:`bytecode` emitted by the bytecode compiler.

 Zen of Python
 Listing of Python design principles and philosophies that are helpful in
 understanding and using the language. The listing can be found by typing
 "``import this``" at the interactive prompt.

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