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 three dots form of the Ellipsis object.

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

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

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

   See *variable annotation*, *function annotation*, **PEP 484** and
   **PEP 526**, which describe this functionality. Also see
   Annotations Best Practices for best practices on working with
   annotations.

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

   * *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 "complex()":

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

   * *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 *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 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 *parameter* glossary entry, the FAQ question on the
   difference between arguments and parameters, and **PEP 362**.

asynchronous context manager
   An object which controls the environment seen in an "async with"
   statement by defining "__aenter__()" and "__aexit__()" methods.
   Introduced by **PEP 492**.

asynchronous generator
   Informally used to mean either an *asynchronous generator function*
   or an *asynchronous generator iterator*, depending on context.  The
   formal terms *asynchronous generator function* and *asynchronous
   generator iterator* are uncommon in practice; "asynchronous
   generator" alone is almost always sufficient.

asynchronous generator function
   A function which returns an *asynchronous generator iterator*. It
   looks like a coroutine function defined with "async def" except
   that it contains "yield" expressions for producing a series of
   values usable in an "async for" loop.  See **PEP 525**.

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

asynchronous generator iterator
   An object created by an *asynchronous generator function*.

   This is an *asynchronous iterator* which when called using the
   "__anext__()" method returns an awaitable object which will execute
   the body of the asynchronous generator function until the next
   "yield" expression.

   Each "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
   "__anext__()", it picks up where it left off.  See **PEP 492** and
   **PEP 525**.

asynchronous iterable
   An object, that can be used in an "async for" statement. Must
   return an *asynchronous iterator* from its "__aiter__()" method.
   Introduced by **PEP 492**.

asynchronous iterator
   An object that implements the "__aiter__()" and "__anext__()"
   methods.  "__anext__()" must return an *awaitable* object. "async
   for" resolves the awaitables returned by an asynchronous iterator's
   "__anext__()" method until it raises a "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 *race condition*
   and *data race*.

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 Identifiers and keywords, for example
   using "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 "getattr()".

awaitable
   An object that can be used in an "await" expression.  Can be a
   *coroutine* or an object with an "__await__()" method. See also
   **PEP 492**.

BDFL
   Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python's
   creator.

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

   See also *text file* for a file object able to read and write "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 *strong
   reference* to the object and so destroy it.

   Calling "Py_INCREF()" on the *borrowed reference* is recommended to
   convert it to a *strong reference* in-place, except when the object
   cannot be destroyed before the last usage of the borrowed
   reference. The "Py_NewRef()" function can be used to create a new
   *strong reference*.

bytes-like object
   An object that supports the Buffer Protocol and can export a
   C-*contiguous* buffer. This includes all "bytes", "bytearray", and
   "array.array" objects, as well as many common "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 "bytearray" and a
   "memoryview" of a "bytearray". Other operations require the binary
   data to be stored in immutable objects ("read-only bytes-like
   objects"); examples of these include "bytes" and a "memoryview" of
   a "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 *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 the dis module.

callable
   A callable is an object that can be called, possibly with a set of
   arguments (see *argument*), with the following syntax:

      callable(argument1, argument2, argumentN)

   A *function*, and by extension a *method*, is a callable. An
   instance of a class that implements the "__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 *free variable* referenced from a *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
   "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 *free variables*, but only "x" is a *closure
   variable*:

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

   Due to the "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 *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 "math" module, use "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.  "asyncio" is a library
   for dealing with asynchronous tasks and coroutines.  "threading"
   provides access to operating system threads and "multiprocessing"
   to operating system processes. Multi-core processors can execute
   threads and processes on different CPU cores at the same time (see
   *parallelism*).

concurrent modification
   When multiple threads modify shared data at the same time.
   Concurrent modification without proper synchronization can cause
   *race conditions*, and might also trigger a *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 *context
     manager* via a "with" statement.

   * The collection of key­value bindings associated with a particular
     "contextvars.Context" object and accessed via "ContextVar"
     objects.  Also see *context variable*.

   * A "contextvars.Context" object.  Also see *current context*.

context management protocol
   The "__enter__()" and "__exit__()" methods called by the "with"
   statement.  See **PEP 343**.

context manager
   An object which implements the *context management protocol* and
   controls the environment seen in a "with" statement.  See **PEP
   343**.

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

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 "async def" statement.  See also **PEP
   492**.

coroutine function
   A function which returns a *coroutine* object.  A coroutine
   function may be defined with the "async def" statement, and may
   contain "await", "async for", and "async with" keywords.  These
   were introduced by **PEP 492**.

CPython
   The canonical implementation of the Python programming language, as
   distributed on python.org.  The term "CPython" is used when
   necessary to distinguish this implementation from others such as
   Jython or IronPython.

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

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 *non-deterministic* behavior and can cause data
   corruption. Proper use of *locks* and other *synchronization
   primitives* prevents data races.  Note that data races can only
   happen in native code, but that *native code* might be exposed in a
   Python API.  See also *race condition* and *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 *locks* in a consistent order.  See also *lock* and
   *reentrant*.

decorator
   A function returning another function, usually applied as a
   function transformation using the "@wrapper" syntax.  Common
   examples for decorators are "@classmethod" and "@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 function definitions and class
   definitions for more about decorators.

descriptor
   Any object which defines the methods "__get__()", "__set__()", or
   "__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 Implementing
   Descriptors or the Descriptor How To Guide.

dictionary
   An associative array, where arbitrary keys are mapped to values.
   The keys can be any object with "__hash__()" and "__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 Displays for lists, sets and dictionaries.

dictionary view
   The objects returned from "dict.keys()", "dict.values()", and
   "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
   Dictionary view objects.

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 "__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 "type()" or "isinstance()".  (Note, however,
   that duck-typing can be complemented with *abstract base classes*.)
   Instead, it typically employs "hasattr()" tests or *EAFP*
   programming.

dunder
   An informal short-hand for "double underscore", used when talking
   about a *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 "try" and
   "except" statements.  The technique contrasts with the *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
   *statement*s which cannot be used as expressions, such as "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 formatted string literals.  See also
   **PEP 498**.

file object
   An object exposing a file-oriented API (with methods such as
   "read()" or "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 *file-like objects* or
   *streams*.

   There are actually three categories of file objects: raw *binary
   files*, buffered *binary files* and *text files*. Their interfaces
   are defined in the "io" module.  The canonical way to create a file
   object is by using the "open()" function.

file-like object
   A synonym for *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 "UnicodeError".

   The "sys.getfilesystemencoding()" and
   "sys.getfilesystemencodeerrors()" functions can be used to get the
   filesystem encoding and error handler.

   The *filesystem encoding and error handler* are configured at
   Python startup by the "PyConfig_Read()" function: see
   "filesystem_encoding" and "filesystem_errors" members of
   "PyConfig".

   See also the *locale encoding*.

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

   There are two types of finder: *meta path finders* for use with
   "sys.meta_path", and *path entry finders* for use with
   "sys.path_hooks".

   See Finders and loaders and "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 *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 language execution model, a free
   variable is any variable used in a namespace which is not a local
   variable in that namespace. See *closure variable* for an example.
   Pragmatically, due to the name of the "codeobject.co_freevars"
   attribute, the term is also sometimes used as a synonym for
   *closure variable*.

function
   A series of statements which returns some value to a caller. It can
   also be passed zero or more *arguments* which may be used in the
   execution of the body. See also *parameter*, *method*, and the
   Function definitions section.

function annotation
   An *annotation* of a function parameter or return value.

   Function annotations are usually used for *type hints*: for
   example, this function is expected to take two "int" arguments and
   is also expected to have an "int" return value:

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

   Function annotation syntax is explained in section Function
   definitions.

   See *variable annotation* and **PEP 484**, which describe this
   functionality. Also see Annotations Best Practices for best
   practices on working with annotations.

__future__
   A future statement, "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
   "__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 "gc"
   module.

generator
   Informally used to mean either a *generator function* or a
   *generator iterator*, depending on context.  The formal terms
   *generator function* and *generator iterator* are uncommon in
   practice; "generator" alone is almost always sufficient.

generator function
   A function which returns a *generator* object.  It looks like a
   normal function except that it contains "yield" expressions for
   producing a series of values usable in a "for"-loop or that can be
   retrieved one at a time with the "next()" function. See Yield
   expressions.

generator iterator
   An object created by a *generator function* or a *generator
   expression*.

   Each "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).

   Generator iterators also implement the "send()" method to send a
   value into the suspended generator, and the "throw()" method to
   raise an exception at the point where the generator was paused.
   See Generator-iterator methods.

generator expression
   An *expression* that returns an *iterator*.  It looks like a normal
   expression followed by a "for" clause defining a loop variable,
   range, and an optional "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 *single dispatch* glossary entry, the
   "@functools.singledispatch" decorator, and **PEP 443**.

generic type
   A *type* that can be parameterized; typically a container class
   such as "list" or "dict". Used for *type hints* and *annotations*.

   For more details, see generic alias types, **PEP 483**, **PEP
   484**, **PEP 585**, and the "typing" module.

GIL
   See *global interpreter lock*.

global interpreter lock
   The mechanism used by the *CPython* interpreter to assure that only
   one thread executes Python *bytecode* at a time. This simplifies
   the CPython implementation by making the object model (including
   critical built-in types such as "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 "--disable-
   gil" build configuration. After building Python with this option,
   code must be run with "-X gil=0" or after setting the
   "PYTHON_GIL=0" 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**.

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

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 Cached bytecode invalidation.

hashable
   An object is *hashable* if it has a hash value which never changes
   during its lifetime (it needs a "__hash__()" method), and can be
   compared to other objects (it needs an "__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 "id()".

IDLE
   An Integrated Development and Learning Environment for Python. IDLE
   --- Python editor and shell 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 *reference count* is never modified,
   and therefore it is never deallocated while the interpreter is
   running. For example, "True" and "None" are immortal in CPython.

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 *thread-safe* because their state cannot be modified
   after creation, eliminating concerns about improperly synchronized
   *concurrent modification*.

import path
   A list of locations (or *path entries*) that are searched by the
   *path based finder* for modules to import. During import, this list
   of locations usually comes from "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 *finder* and
   *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 Interactive Mode.

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 *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 *garbage collector*. 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 "list", "str", and
   "tuple") and some non-sequence types like "dict", *file objects*,
   and objects of any classes you define with an "__iter__()" method
   or with a "__getitem__()" method that implements *sequence*
   semantics.

   Iterables can be used in a "for" loop and in many other places
   where a sequence is needed ("zip()", "map()", ...).  When an
   iterable object is passed as an argument to the built-in function
   "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 "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 *iterator*, *sequence*, and
   *generator*.

iterator
   An object representing a stream of data.  Repeated calls to the
   iterator's "__next__()" method (or passing it to the built-in
   function "next()") return successive items in the stream.  When no
   more data are available a "StopIteration" exception is raised
   instead.  At this point, the iterator object is exhausted and any
   further calls to its "__next__()" method just raise "StopIteration"
   again.  Iterators are required to have an "__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 "list") produces a
   fresh new iterator each time you pass it to the "iter()" function
   or use it in a "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 Iterator Types.

   **CPython implementation detail:** CPython does not consistently
   apply the requirement that an iterator define "__iter__()". And
   also please note that *free-threaded* CPython does not guarantee
   *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,
   "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 "min()", "max()",
   "sorted()", "list.sort()", "heapq.merge()", "heapq.nsmallest()",
   "heapq.nlargest()", and "itertools.groupby()".

   There are several ways to create a key function.  For example. the
   "str.casefold()" method can serve as a key function for case
   insensitive sorts.  Alternatively, a key function can be built from
   a "lambda" expression such as "lambda r: (r[0], r[2])".  Also,
   "operator.attrgetter()", "operator.itemgetter()", and
   "operator.methodcaller()" are three key function constructors.  See
   the Sorting HOW TO for examples of how to create and use key
   functions.

keyword argument
   See *argument*.

lambda
   An anonymous inline function consisting of a single *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 *EAFP* approach and is characterized by the presence of
   many "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.  See also
   *thread-safe*.

lexical analyzer
   Formal name for the *tokenizer*; see *token*.

list
   A built-in Python *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
   "if" clause is optional.  If omitted, all elements in "range(256)"
   are processed.

lock
   A *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
   "threading" module provides "Lock" (a basic lock) and "RLock" (a
   *reentrant* lock).  Locks are used to prevent *race conditions* and
   ensure *thread-safe* access to shared data.  Alternative design
   patterns to locks exist such as queues, producer/consumer patterns,
   and thread-local state. See also *deadlock*, and *reentrant*.

lock-free
   An operation that does not acquire any *lock* and uses atomic CPU
   instructions to ensure correctness. Lock-free operations can
   execute concurrently without blocking each other and cannot be
   blocked by operations that hold locks. In *free-threaded* Python,
   built-in types like "dict" and "list" provide lock-free read
   operations, which means other threads may observe intermediate
   states during multi-step modifications even when those
   modifications hold the *per-object lock*.

loader
   An object that loads a module. It must define the "exec_module()"
   and "create_module()" methods to implement the "Loader" interface.
   A loader is typically returned by a *finder*. See also:

   * Finders and loaders

   * "importlib.abc.Loader"

   * **PEP 302**

locale encoding
   On Unix, it is the encoding of the LC_CTYPE locale. It can be set
   with "locale.setlocale(locale.LC_CTYPE, new_locale)".

   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 *filesystem encoding and error handler*.

magic method
   An informal synonym for *special method*.

mapping
   A container object that supports arbitrary key lookups and
   implements the methods specified in the "collections.abc.Mapping"
   or "collections.abc.MutableMapping" abstract base classes.
   Examples include "dict", "collections.defaultdict",
   "collections.OrderedDict" and "collections.Counter".

meta path finder
   A *finder* returned by a search of "sys.meta_path".  Meta path
   finders are related to, but different from *path entry finders*.

   See "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 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 *argument* (which is usually called
   "self"). See *function* and *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 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 *importing*.

   See also *package*.

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

   See also Module specs.

MRO
   See *method resolution order*.

mutable
   An *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
   *race conditions*.  See also *immutable*, *thread-safe*, and
   *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 "time.localtime()" and "os.stat()".  Another example is
   "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 "tuple" and that defines named
   fields.  Such a class can be written by hand, or it can be created
   by inheriting "typing.NamedTuple", or with the factory function
   "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 "builtins.open" and "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 "random.seed()" or
   "itertools.islice()" makes it clear that those functions are
   implemented by the "random" and "itertools" modules, respectively.

namespace package
   A *package* which serves only as a container for subpackages.
   Namespace packages may have no physical representation, and
   specifically are not like a *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 *regular package*.

   For more information, see **PEP 420** and Namespace packages.

   See also *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 *extension modules* that
   can be called from Python.  See also *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
   "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 "__slots__", descriptors,
   properties, "__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 *race conditions* where
   the relative timing or interleaving of threads affects the result.
   Proper synchronization using *locks* and other *synchronization
   primitives* helps ensure deterministic behavior.

object
   Any data with state (attributes or value) and defined behavior
   (methods).  Also the ultimate base class of any *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.

package
   A Python *module* which can contain submodules or recursively,
   subpackages.  Technically, a package is a Python module with a
   "__path__" attribute.

   See also *regular package* and *namespace package*.

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

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

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

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

   * *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*: 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): ...

   * *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): ...

   * *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 *argument* glossary entry, the FAQ question on the
   difference between arguments and parameters, the
   "inspect.Parameter" class, the Function definitions section, and
   **PEP 362**.

per-object lock
   A *lock* associated with an individual object instance rather than
   a global lock shared across all objects. In *free-threaded* Python,
   built-in types like "dict" and "list" use per-object locks to allow
   concurrent operations on different objects while serializing
   operations on the same object. Operations that hold the per-object
   lock prevent other locking operations on the same object from
   proceeding, but do not block *lock-free* operations.

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

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

   See "importlib.abc.PathEntryFinder" for the methods that path entry
   finders implement.

path entry hook
   A callable on the "sys.path_hooks" list which returns a *path entry
   finder* if it knows how to find modules on a specific *path entry*.

path based finder
   One of the default *meta path finders* which searches an *import
   path* for modules.

path-like object
   An object representing a file system path. A path-like object is
   either a "str" or "bytes" object representing a path, or an object
   implementing the "os.PathLike" protocol. An object that supports
   the "os.PathLike" protocol can be converted to a "str" or "bytes"
   file system path by calling the "os.fspath()" function;
   "os.fsdecode()" and "os.fsencode()" can be used to guarantee a
   "str" or "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 *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 *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 "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 behavior depends on the relative
   timing or ordering of events, particularly in multi-threaded
   programs.  Race conditions can lead to *non-deterministic* behavior
   and bugs that are difficult to reproduce.  A *data race* is a
   specific type of race condition involving unsynchronized access to
   shared memory.  The *LBYL* coding style is particularly susceptible
   to race conditions in multi-threaded code.  Using *locks* and other
   *synchronization primitives* 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
   *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 *CPython* implementation.  Programmers can call the
   "sys.getrefcount()" function to return the reference count for a
   particular object.

   In *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 *package*, such as a directory containing an
   "__init__.py" file.

   See also *namespace package*.

reentrant
   A property of a function or *lock* that allows it to be called or
   acquired multiple times by the same thread without causing errors
   or a *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 *non-deterministic* if
   they're called reentrantly in a multithreaded program.

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

   See also *lock* and *deadlock*.

REPL
   An acronym for the "read–eval–print loop", another name for the
   *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 *iterable* which supports efficient element access using integer
   indices via the "__getitem__()" special method and defines a
   "__len__()" method that returns the length of the sequence. Some
   built-in sequence types are "list", "str", "tuple", and "bytes".
   Note that "dict" also supports "__getitem__()" and "__len__()", but
   is considered a mapping rather than a sequence because the lookups
   use arbitrary *hashable* keys rather than integers.

   The "collections.abc.Sequence" abstract base class defines a much
   richer interface that goes beyond just "__getitem__()" and
   "__len__()", adding "count()", "index()", "__contains__()", and
   "__reversed__()". Types that implement this expanded interface can
   be registered explicitly using "register()". For more documentation
   on sequence methods generally, see Common Sequence Operations.

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 Displays for lists, sets and dictionaries.

single dispatch
   A form of *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 *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 "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.

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 Special method names.

standard library
   The collection of *packages*, *modules* and *extension modules*
   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 The Python Standard Library.

   See also "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 *expression* or one of several constructs with a keyword,
   such as "if", "while" or "for".

static type checker
   An external tool that reads Python code and analyzes it, looking
   for issues such as incorrect types. See also *type hints* and the
   "typing" module.

stdlib
   An abbreviation of *standard library*.

steal
   In Python's C API, "*stealing*" an argument means that ownership of
   the argument is transferred to the called function. The caller must
   not use that reference after the call. Generally, functions that
   "steal" an argument do so even if they fail.

   See Reference Count Details for a full explanation.

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 "Py_INCREF()" when the reference is
   created and released with "Py_DECREF()" when the reference is
   deleted.

   The "Py_NewRef()" function can be used to create a strong reference
   to an object. Usually, the "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 *borrowed reference*.

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

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 codecs, which
   are collectively referred to as "text encodings".

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

   See also *binary file* for a file object able to read and write
   *bytes-like objects*.

thread-safe
   A module, function, or class that behaves correctly when used by
   multiple threads concurrently.  Thread-safe code uses appropriate
   *synchronization primitives* like *locks* to protect shared mutable
   state, or is designed to avoid shared mutable state entirely.  In
   the *free-threaded* build, built-in types like "dict", "list", and
   "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 *race conditions* and *data races*
   when used in multi-threaded programs.

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

   The "tokenize" module exposes Python's lexical analyzer. The
   "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
   "__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 *type hints*. 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 "typing" and **PEP 484**, which describe this functionality.

type hint
   An *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 checkers*. 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
   "typing.get_type_hints()".

   See "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
   "bytes.splitlines()" for an additional use.

variable annotation
   An *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 *type hints*: for example
   this variable is expected to take "int" values:

      count: int = 0

   Variable annotation syntax is explained in section Annotated
   assignment statements.

   See *function annotation*, **PEP 484** and **PEP 526**, which
   describe this functionality. Also see Annotations Best Practices
   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 "venv".

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

walrus operator
   A light-hearted way to refer to the assignment expression 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.
