.. _glossary:

********
Glossary
********

.. if you add new entries, keep the alphabetical sorting!

.. glossary::

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

   ``...``
      Can refer to:

      * The default Python prompt of the :term:`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.

      .. index:: single: ...; ellipsis literal

      * The three dots form of the :ref:`Ellipsis <bltin-ellipsis-object>` object.

   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.

   annotate function
      A function that can be called to retrieve the :term:`annotations <annotation>`
      of an object. This function is accessible as the :attr:`~object.__annotate__`
      attribute of functions, classes, and modules. Annotate functions are a
      subset of :term:`evaluate functions <evaluate function>`.

   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
      can be retrieved by calling :func:`annotationlib.get_annotations`
      on modules, classes, and functions, respectively.

      See :term:`variable annotation`, :term:`function annotation`, :pep:`484`,
      :pep:`526`, and :pep:`649`, 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 an :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
      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`.

   atomic operation
      An operation that appears to execute as a single, indivisible step: no
      other thread can observe it half-done, and its effects become visible all
      at once.  Python does not guarantee that high-level statements are atomic
      (for example, ``x += 1`` performs multiple bytecode operations and is not
      atomic).  Atomicity is only guaranteed where explicitly documented.  See
      also :term:`race condition` and :term:`data race`.

   attached thread state

      A :term:`thread state` that is active for the current OS thread.

      When a :term:`thread state` is attached, the OS thread has
      access to the full Python C API and can safely invoke the
      bytecode interpreter.

      Unless a function explicitly notes otherwise, attempting to call
      the C API without an attached thread state will result in a fatal
      error or undefined behavior.  A thread state can be attached and detached
      explicitly by the user through the C API, or implicitly by the runtime,
      including during blocking C calls and by the bytecode interpreter in between
      calls.

      On most builds of Python, having an attached thread state implies that the
      caller holds the :term:`GIL` for the current interpreter, so only
      one OS thread can have an attached thread state at a given moment. In
      :term:`free-threaded <free threading>` builds of Python, threads can concurrently
      hold an attached thread state, allowing for true parallelism of the bytecode
      interpreter.

   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 <sys.stdin>`,
      :data:`sys.stdout.buffer <sys.stdout>`, 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,
      where the code using the object does not own the reference.
      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).

   closure variable
      A :term:`free variable` referenced from a :term:`nested scope` that is defined in an outer
      scope rather than being resolved at runtime from the globals or builtin namespaces.
      May be explicitly defined with the :keyword:`nonlocal` keyword to allow write access,
      or implicitly defined if the variable is only being read.

      For example, in the ``inner`` function in the following code, both ``x`` and ``print`` are
      :term:`free variables <free variable>`, but only ``x`` is a *closure variable*::

          def outer():
              x = 0
              def inner():
                  nonlocal x
                  x += 1
                  print(x)
              return inner

      Due to the :attr:`codeobject.co_freevars` attribute (which, despite its name, only
      includes the names of closure variables rather than listing all referenced free
      variables), the more general :term:`free variable` term is sometimes used even
      when the intended meaning is to refer specifically to closure variables.

   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.

   concurrency
      The ability of a computer program to perform multiple tasks at the same
      time.  Python provides libraries for writing programs that make use of
      different forms of concurrency.  :mod:`asyncio` is a library for dealing
      with asynchronous tasks and coroutines.  :mod:`threading` provides
      access to operating system threads and :mod:`multiprocessing` to
      operating system processes. Multi-core processors can execute threads and
      processes on different CPU cores at the same time (see
      :term:`parallelism`).

   concurrent modification
      When multiple threads modify shared data at the same time.  Concurrent
      modification without proper synchronization can cause
      :term:`race conditions <race condition>`, and might also trigger a
      :term:`data race <data race>`, data corruption, or both.

   context
      This term has different meanings depending on where and how it is used.
      Some common meanings:

      * The temporary state or environment established by a :term:`context
        manager` via a :keyword:`with` statement.
      * The collection of key­value bindings associated with a particular
        :class:`contextvars.Context` object and accessed via
        :class:`~contextvars.ContextVar` objects.  Also see :term:`context
        variable`.
      * A :class:`contextvars.Context` object.  Also see :term:`current
        context`.

   context management protocol
      The :meth:`~object.__enter__` and :meth:`~object.__exit__` methods called
      by the :keyword:`with` statement.  See :pep:`343`.

   context manager
      An object which implements the :term:`context management protocol` and
      controls the environment seen in a :keyword:`with` statement.  See
      :pep:`343`.

   context variable
      A variable whose value depends on which context is the :term:`current
      context`.  Values are accessed via :class:`contextvars.ContextVar`
      objects.  Context variables are primarily used to isolate state between
      concurrent asynchronous tasks.

   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.

   current context
      The :term:`context` (:class:`contextvars.Context` object) that is
      currently used by :class:`~contextvars.ContextVar` objects to access (get
      or set) the values of :term:`context variables <context variable>`.  Each
      thread has its own current context.  Frameworks for executing asynchronous
      tasks (see :mod:`asyncio`) associate each task with a context which
      becomes the current context whenever the task starts or resumes execution.

   cyclic isolate
      A subgroup of one or more objects that reference each other in a reference
      cycle, but are not referenced by objects outside the group.  The goal of
      the :term:`cyclic garbage collector <garbage collection>` is to identify these groups and break the reference
      cycles so that the memory can be reclaimed.

   data race
      A situation where multiple threads access the same memory location
      concurrently, at least one of the accesses is a write, and the threads
      do not use any synchronization to control their access.  Data races
      lead to :term:`non-deterministic` behavior and can cause data corruption.
      Proper use of :term:`locks <lock>` and other :term:`synchronization primitives
      <synchronization primitive>` prevents data races.  Note that data races
      can only happen in native code, but that :term:`native code` might be
      exposed in a Python API.  See also :term:`race condition` and
      :term:`thread-safe`.

   deadlock
      A situation in which two or more tasks (threads, processes, or coroutines)
      wait indefinitely for each other to release resources or complete actions,
      preventing any from making progress.  For example, if thread A holds lock
      1 and waits for lock 2, while thread B holds lock 2 and waits for lock 1,
      both threads will wait indefinitely.  In Python this often arises from
      acquiring multiple locks in conflicting orders or from circular
      join/await dependencies.  Deadlocks can be avoided by always acquiring
      multiple :term:`locks <lock>` in a consistent order.  See also
      :term:`lock` and :term:`reentrant`.

   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:`~object.__get__`,
      :meth:`~object.__set__`, or :meth:`~object.__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:`~object.__hash__` and
      :meth:`~object.__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:`~definition.__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.

   dunder
      An informal short-hand for "double underscore", used when talking about a
      :term:`special method`. For example, ``__init__`` is often pronounced
      "dunder init".

   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.

   evaluate function
      A function that can be called to evaluate a lazily evaluated attribute
      of an object, such as the value of type aliases created with the :keyword:`type`
      statement.

   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
   f-strings
      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.

      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 :ref:`finders-and-loaders` and :mod:`importlib` 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`.

   free threading
      A threading model where multiple threads can run Python bytecode
      simultaneously within the same interpreter.  This is in contrast to
      the :term:`global interpreter lock` which allows only one thread to
      execute Python bytecode at a time.  See :pep:`703`.

   free variable
      Formally, as defined in the :ref:`language execution model <bind_names>`, a free
      variable is any variable used in a namespace which is not a local variable in that
      namespace. See :term:`closure variable` for an example.
      Pragmatically, due to the name of the :attr:`codeobject.co_freevars` attribute,
      the term is also sometimes used as a synonym for :term:`closure variable`.

   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
      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 :term:`expression` that returns an :term:`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.

      As of Python 3.13, the GIL can be disabled using the :option:`--disable-gil`
      build configuration. After building Python with this option, code must be
      run with :option:`-X gil=0 <-X>` or after setting the :envvar:`PYTHON_GIL=0 <PYTHON_GIL>`
      environment variable. This feature enables improved performance for
      multi-threaded applications and makes it easier to use multi-core CPUs
      efficiently. For more details, see :pep:`703`.

      In prior versions of Python's C API, a function might declare that it
      requires the GIL to be held in order to use it. This refers to having an
      :term:`attached thread state`.

   global state
      Data that is accessible throughout a program, such as module-level
      variables, class variables, or C static variables in :term:`extension modules
      <extension module>`.  In multi-threaded programs, global state shared
      between threads typically requires synchronization to avoid
      :term:`race conditions <race condition>` and
      :term:`data races <data race>`.

   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:`~object.__hash__` method), and can be
      compared to other objects (it needs an :meth:`~object.__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.

   immortal
      *Immortal objects* are a CPython implementation detail introduced
      in :pep:`683`.

      If an object is immortal, its :term:`reference count` is never modified,
      and therefore it is never deallocated while the interpreter is running.
      For example, :const:`True` and :const:`None` are immortal in CPython.

      Immortal objects can be identified via :func:`sys._is_immortal`, or
      via :c:func:`PyUnstable_IsImmortal` in the C API.

   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.  Immutable objects are inherently :term:`thread-safe`
      because their state cannot be modified after creation, eliminating concerns
      about improperly synchronized :term:`concurrent modification`.

   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)``). For more on interactive
      mode, see :ref:`tut-interac`.

   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:`~object.__iter__` method or with a
      :meth:`~object.__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 :keyword:`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:`~iterator.__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:`~iterator.__iter__`.
         And also please note that :term:`free-threaded <free threading>`
         CPython does not guarantee :term:`thread-safe` behavior of iterator
         operations.


   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.casefold` 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
      :term:`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 :term:`locks <lock>` or by using the
      :term:`EAFP` approach.  See also :term:`thread-safe`.

   lexical analyzer

      Formal name for the *tokenizer*; see :term:`token`.

   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.

   lock
      A :term:`synchronization primitive` that allows only one thread at a
      time to access a shared resource.  A thread must acquire a lock before
      accessing the protected resource and release it afterward.  If a thread
      attempts to acquire a lock that is already held by another thread, it
      will block until the lock becomes available.  Python's :mod:`threading`
      module provides :class:`~threading.Lock` (a basic lock) and
      :class:`~threading.RLock` (a :term:`reentrant` lock).  Locks are used
      to prevent :term:`race conditions <race condition>` and ensure
      :term:`thread-safe` access to shared data.  Alternative design patterns
      to locks exist such as queues, producer/consumer patterns, and
      thread-local state. See also :term:`deadlock`, and :term:`reentrant`.

   loader
      An object that loads a module.
      It must define the :meth:`!exec_module` and :meth:`!create_module` methods
      to implement the :class:`~importlib.abc.Loader` interface.
      A loader is typically returned by a :term:`finder`.
      See also:

      * :ref:`finders-and-loaders`
      * :class:`importlib.abc.Loader`
      * :pep:`302`

   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.

      :func:`locale.getencoding` can be used to get the locale encoding.

      See also the :term:`filesystem encoding and error handler`.

   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 :ref:`python_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`.

      See also :ref:`module-specs`.

   MRO
      See :term:`method resolution order`.

   mutable
      An :term:`object` with state that is allowed to change during the course
      of the program.  In multi-threaded programs, mutable objects that are
      shared between threads require careful synchronization to avoid
      :term:`race conditions <race condition>`.  See also :term:`immutable`,
      :term:`thread-safe`, and :term:`concurrent modification`.

   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 by
      inheriting :class:`typing.NamedTuple`, or with the factory function
      :func:`collections.namedtuple`.  The latter techniques also add 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 :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.

      Namespace packages allow several individually installable packages to have a common parent package.
      Otherwise, it is recommended to use a :term:`regular package`.

      For more information, see :pep:`420` and :ref:`reference-namespace-package`.

      See also :term:`module`.

   native code
      Code that is compiled to machine instructions and runs directly on the
      processor, as opposed to code that is interpreted or runs in a virtual
      machine.  In the context of Python, native code typically refers to
      C, C++, Rust or Fortran code in :term:`extension modules <extension module>`
      that can be called from Python.  See also :term:`extension 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:`~object.__getattribute__`, class methods, and static
      methods.

   non-deterministic
      Behavior where the outcome of a program can vary between executions with
      the same inputs.  In multi-threaded programs, non-deterministic behavior
      often results from :term:`race conditions <race condition>` where the
      relative timing or interleaving of threads affects the result.
      Proper synchronization using :term:`locks <lock>` and other
      :term:`synchronization primitives <synchronization primitive>` helps
      ensure deterministic behavior.

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

   optimized scope
      A scope where target local variable names are reliably known to the
      compiler when the code is compiled, allowing optimization of read and
      write access to these names. The local namespaces for functions,
      generators, coroutines, comprehensions, and generator expressions are
      optimized in this fashion. Note: most interpreter optimizations are
      applied to all scopes, only those relying on a known set of local
      and nonlocal variable names are restricted to optimized scopes.

   optional module
      An :term:`extension module` that is part of the :term:`standard library`,
      but may be absent in some builds of :term:`CPython`,
      usually due to missing third-party libraries or because the module
      is not available for a given platform.

      See :ref:`optional-module-requirements` for a list of optional modules
      that require third-party libraries.

   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`.

   parallelism
      Executing multiple operations at the same time (e.g. on multiple CPU
      cores).  In Python builds with the
      :term:`global interpreter lock (GIL) <global interpreter lock>`, only one
      thread runs Python bytecode at a time, so taking advantage of multiple
      CPU cores typically involves multiple processes
      (e.g. :mod:`multiprocessing`) or native extensions that release the GIL.
      In :term:`free-threaded <free threading>` Python, multiple Python threads
      can run Python code simultaneously on different cores.

   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_hooks` 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'

   race condition
      A condition of a program where the its behavior
      depends on the relative timing or ordering of events, particularly in
      multi-threaded programs.  Race conditions can lead to
      :term:`non-deterministic` behavior and bugs that are difficult to
      reproduce.  A :term:`data race` is a specific type of race condition
      involving unsynchronized access to shared memory.  The :term:`LBYL`
      coding style is particularly susceptible to race conditions in
      multi-threaded code.  Using :term:`locks <lock>` and other
      :term:`synchronization primitives <synchronization primitive>`
      helps prevent race conditions.

   reference count
      The number of references to an object.  When the reference count of an
      object drops to zero, it is deallocated.  Some objects are
      :term:`immortal` and have reference counts that are never modified, and
      therefore the objects are never 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.

      In :term:`CPython`, reference counts are not considered to be stable
      or well-defined values; the number of references to an object, and how
      that number is affected by Python code, may be different between
      versions.

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

      See also :term:`namespace package`.

   reentrant
      A property of a function or :term:`lock` that allows it to be called or
      acquired multiple times by the same thread without causing errors or a
      :term:`deadlock`.

      For functions, reentrancy means the function can be safely called again
      before a previous invocation has completed, which is important when
      functions may be called recursively or from signal handlers. Thread-unsafe
      functions may be :term:`non-deterministic` if they're called reentrantly in a
      multithreaded program.

      For locks, Python's :class:`threading.RLock` (reentrant lock) is
      reentrant, meaning a thread that already holds the lock can acquire it
      again without blocking.  In contrast, :class:`threading.Lock` is not
      reentrant - attempting to acquire it twice from the same thread will cause
      a deadlock.

      See also :term:`lock` and :term:`deadlock`.

   REPL
      An acronym for the "read–eval–print loop", another name for the
      :term:`interactive` interpreter shell.

   __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:`~object.__getitem__` special method and defines a
      :meth:`~object.__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:`~object.__getitem__` and :meth:`!__len__`, but is considered a
      mapping rather than a sequence because the lookups use arbitrary
      :term:`hashable` keys rather than integers.

      The :class:`collections.abc.Sequence` abstract base class
      defines a much richer interface that goes beyond just
      :meth:`~object.__getitem__` and :meth:`~object.__len__`, adding
      :meth:`~sequence.count`, :meth:`~sequence.index`,
      :meth:`~object.__contains__`, and :meth:`~object.__reversed__`.
      Types that implement this expanded
      interface can be registered explicitly using
      :func:`~abc.ABCMeta.register`. For more documentation on sequence
      methods generally, see
      :ref:`Common Sequence Operations <typesseq-common>`.

   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.

   soft deprecated
      A soft deprecated API should not be used in new code,
      but it is safe for already existing code to use it.
      The API remains documented and tested, but will not be enhanced further.

      Soft deprecation, unlike normal deprecation, does not plan on removing the API
      and will not emit warnings.

      See `PEP 387: Soft Deprecation
      <https://peps.python.org/pep-0387/#soft-deprecation>`_.

   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`.

   standard library
      The collection of :term:`packages <package>`, :term:`modules <module>`
      and :term:`extension modules <extension module>` distributed as a part
      of the official Python interpreter package.  The exact membership of the
      collection may vary based on platform, available system libraries, or
      other criteria.  Documentation can be found at :ref:`library-index`.

      See also :data:`sys.stdlib_module_names` for a list of all possible
      standard library module names.

   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`.

   static type checker
      An external tool that reads Python code and analyzes it, looking for
      issues such as incorrect types. See also :term:`type hints <type hint>`
      and the :mod:`typing` module.

   stdlib
      An abbreviation of :term:`standard library`.

   strong reference
      In Python's C API, a strong reference is a reference to an object
      which is owned by the code holding the reference.  The strong
      reference is taken by calling :c:func:`Py_INCREF` when the
      reference is created and released with :c:func:`Py_DECREF`
      when the reference 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`.

   synchronization primitive
      A basic building block for coordinating (synchronizing) the execution of
      multiple threads to ensure :term:`thread-safe` access to shared resources.
      Python's :mod:`threading` module provides several synchronization primitives
      including :class:`~threading.Lock`, :class:`~threading.RLock`,
      :class:`~threading.Semaphore`, :class:`~threading.Condition`,
      :class:`~threading.Event`, and :class:`~threading.Barrier`.  Additionally,
      the :mod:`queue` module provides multi-producer, multi-consumer queues
      that are especially useful in multithreaded programs. These
      primitives help prevent :term:`race conditions <race condition>` and
      coordinate thread execution.  See also :term:`lock`.

   t-string
   t-strings
      String literals prefixed with ``t`` or ``T`` are commonly called
      "t-strings" which is short for
      :ref:`template string literals <t-strings>`.

   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>`.

   thread state

      The information used by the :term:`CPython` runtime to run in an OS thread.
      For example, this includes the current exception, if any, and the
      state of the bytecode interpreter.

      Each thread state is bound to a single OS thread, but threads may have
      many thread states available.  At most, one of them may be
      :term:`attached <attached thread state>` at once.

      An :term:`attached thread state` is required to call most
      of Python's C API, unless a function explicitly documents otherwise.
      The bytecode interpreter only runs under an attached thread state.

      Each thread state belongs to a single interpreter, but each interpreter
      may have many thread states, including multiple for the same OS thread.
      Thread states from multiple interpreters may be bound to the same
      thread, but only one can be :term:`attached <attached thread state>` in
      that thread at any given moment.

      See :ref:`Thread State and the Global Interpreter Lock <threads>` for more
      information.

   thread-safe
      A module, function, or class that behaves correctly when used by multiple
      threads concurrently.  Thread-safe code uses appropriate
      :term:`synchronization primitives <synchronization primitive>` like
      :term:`locks <lock>` to protect shared mutable state, or is designed
      to avoid shared mutable state entirely.  In the
      :term:`free-threaded <free threading>` build, built-in types like
      :class:`dict`, :class:`list`, and :class:`set` use internal locking
      to make many operations thread-safe, although thread safety is not
      necessarily guaranteed.  Code that is not thread-safe may experience
      :term:`race conditions <race condition>` and :term:`data races <data race>`
      when used in multi-threaded programs.

   token

      A small unit of source code, generated by the
      :ref:`lexical analyzer <lexical>` (also called the *tokenizer*).
      Names, numbers, strings, operators,
      newlines and similar are represented by tokens.

      The :mod:`tokenize` module exposes Python's lexical analyzer.
      The :mod:`token` module contains information on the various types
      of tokens.

   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:`~object.__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 :term:`static type checkers <static type checker>`.
      They can also 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.

   walrus operator
      A light-hearted way to refer to the :ref:`assignment expression
      <assignment-expressions>` operator ``:=`` because it looks a bit like a
      walrus if you turn your head.

   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.