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.. _glossary:
********
Glossary
********
.. if you add new entries, keep the alphabetical sorting!
.. glossary::
``>>>``
The default Python prompt of the interactive shell. Often seen for code
examples which can be executed interactively in the interpreter.
``...``
Can refer to:
* 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.
* The :const:`Ellipsis` built-in constant.
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.
Also see :ref:`annotations-howto`
for best practices on working with annotations.
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:`~object.__aenter__` and
:meth:`~object.__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:`~object.__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:`~object.__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:`~object.__aiter__` method. Introduced by :pep:`492`.
asynchronous iterator
An object that implements the :meth:`~object.__aiter__` and :meth:`~object.__anext__`
methods. :meth:`~object.__anext__` must return an :term:`awaitable` object.
:keyword:`async for` resolves the awaitables returned by an asynchronous
iterator's :meth:`~object.__anext__` method until it raises a
:exc:`StopAsyncIteration` exception. Introduced by :pep:`492`.
attribute
A value associated with an object which is usually referenced by name
using dotted expressions.
For example, if an object *o* has an attribute
*a* it would be referenced as *o.a*.
It is possible to give an object an attribute whose name is not an
identifier as defined by :ref:`identifiers`, for example using
:func:`setattr`, if the object allows it.
Such an attribute will not be accessible using a dotted expression,
and would instead need to be retrieved with :func:`getattr`.
awaitable
An object that can be used in an :keyword:`await` expression. Can be
a :term:`coroutine` or an object with an :meth:`~object.__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.
borrowed reference
In Python's C API, a borrowed reference is a reference to an object.
It does not modify the object reference count. It becomes a dangling
pointer if the object is destroyed. For example, a garbage collection can
remove the last :term:`strong reference` to the object and so destroy it.
Calling :c:func:`Py_INCREF` on the :term:`borrowed reference` is
recommended to convert it to a :term:`strong reference` in-place, except
when the object cannot be destroyed before the last usage of the borrowed
reference. The :c:func:`Py_NewRef` function can be used to create a new
:term:`strong reference`.
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>`.
callable
A callable is an object that can be called, possibly with a set
of arguments (see :term:`argument`), with the following syntax::
callable(argument1, argument2, argumentN)
A :term:`function`, and by extension a :term:`method`, is a callable.
An instance of a class that implements the :meth:`~object.__call__`
method is also a callable.
callback
A subroutine function which is passed as an argument to be executed at
some point in the future.
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).
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 are 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(arg):
...
f = staticmethod(f)
@staticmethod
def f(arg):
...
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`
or the :ref:`Descriptor How To Guide <descriptorhowto>`.
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 comprehension
A compact way to process all or part of the elements in an iterable and
return a dictionary with the results. ``results = {n: n ** 2 for n in
range(10)}`` generates a dictionary containing key ``n`` mapped to
value ``n ** 2``. See :ref:`comprehensions`.
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`.
filesystem encoding and error handler
Encoding and error handler used by Python to decode bytes from the
operating system and encode Unicode to the operating system.
The filesystem encoding must guarantee to successfully decode all bytes
below 128. If the file system encoding fails to provide this guarantee,
API functions can raise :exc:`UnicodeError`.
The :func:`sys.getfilesystemencoding` and
:func:`sys.getfilesystemencodeerrors` functions can be used to get the
filesystem encoding and error handler.
The :term:`filesystem encoding and error handler` are configured at
Python startup by the :c:func:`PyConfig_Read` function: see
:c:member:`~PyConfig.filesystem_encoding` and
:c:member:`~PyConfig.filesystem_errors` members of :c:type:`PyConfig`.
See also the :term:`locale encoding`.
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.
Also see :ref:`annotations-howto`
for best practices on working with annotations.
__future__
A :ref:`future statement <future>`, ``from __future__ import <feature>``,
directs the compiler to compile the current module using syntax or
semantics that will become standard in a future release of Python.
The :mod:`__future__` module documents the possible values of
*feature*. By importing this module and evaluating its variables,
you can see when a new feature was first added to the language and
when it will (or did) become 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`.
generic type
A :term:`type` that can be parameterized; typically a
:ref:`container class<sequence-types>` such as :class:`list` or
:class:`dict`. Used for :term:`type hints <type hint>` and
:term:`annotations <annotation>`.
For more details, see :ref:`generic alias types<types-genericalias>`,
:pep:`483`, :pep:`484`, :pep:`585`, and the :mod:`typing` module.
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 and Learning Environment for Python.
:ref:`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`.
.. impl-detail::
CPython does not consistently apply the requirement that an iterator
define :meth:`__iter__`.
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,
:func:`operator.attrgetter`, :func:`operator.itemgetter`, and
:func:`operator.methodcaller` are three key function constructors. 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.
locale encoding
On Unix, it is the encoding of the LC_CTYPE locale. It can be set with
:func:`locale.setlocale(locale.LC_CTYPE, new_locale) <locale.setlocale>`.
On Windows, it is the ANSI code page (ex: ``"cp1252"``).
On Android and VxWorks, Python uses ``"utf-8"`` as the locale encoding.
``locale.getencoding()`` can be used to get the locale encoding.
See also the :term:`filesystem encoding and error handler`.
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
The term "named tuple" applies to any type or class that inherits from
tuple and whose indexable elements are also accessible using named
attributes. The type or class may have other features as well.
Several built-in types are named tuples, including the values returned
by :func:`time.localtime` and :func:`os.stat`. Another example is
:data:`sys.float_info`::
>>> sys.float_info[1] # indexed access
1024
>>> sys.float_info.max_exp # named field access
1024
>>> isinstance(sys.float_info, tuple) # kind of tuple
True
Some named tuples are built-in types (such as the above examples).
Alternatively, a named tuple can be created from a regular class
definition that inherits from :class:`tuple` and that defines named
fields. Such a class can be written by hand or it can be created with
the factory function :func:`collections.namedtuple`. The latter
technique also adds some extra methods that may not be found in
hand-written or built-in named tuples.
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 a
``__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. Positional-only parameters can be defined by including a
``/`` character in the parameter list of the function definition after
them, for example *posonly1* and *posonly2* in the following::
def func(posonly1, posonly2, /, positional_or_keyword): ...
.. _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. Programmers can call the
:func:`sys.getrefcount` function 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.ABCMeta.register`.
set comprehension
A compact way to process all or part of the elements in an iterable and
return a set with the results. ``results = {c for c in 'abracadabra' if
c not in 'abc'}`` generates the set of strings ``{'r', 'd'}``. See
:ref:`comprehensions`.
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`.
strong reference
In Python's C API, a strong reference is a reference to an object
which increments the object's reference count when it is created and
decrements the object's reference count when it is deleted.
The :c:func:`Py_NewRef` function can be used to create a strong reference
to an object. Usually, the :c:func:`Py_DECREF` function must be called on
the strong reference before exiting the scope of the strong reference, to
avoid leaking one reference.
See also :term:`borrowed reference`.
text encoding
A string in Python is a sequence of Unicode code points (in range
``U+0000``--``U+10FFFF``). To store or transfer a string, it needs to be
serialized as a sequence of bytes.
Serializing a string into a sequence of bytes is known as "encoding", and
recreating the string from the sequence of bytes is known as "decoding".
There are a variety of different text serialization
:ref:`codecs <standard-encodings>`, which are collectively referred to as
"text encodings".
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::
def remove_gray_shades(
colors: list[tuple[int, int, int]]) -> list[tuple[int, int, int]]:
pass
could be made more readable like this::
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.
Also see :ref:`annotations-howto`
for best practices on working with annotations.
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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