Glossary
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">>>"
   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 "Ellipsis" built-in constant.

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

   2to3 is available in the standard library as "lib2to3"; a
   standalone entry point is provided as "Tools/scripts/2to3".  See
   2to3 - Automated Python 2 to 3 code translation.

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.

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
   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.

   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 "await" expressions
   as well as "async for", and "async with" statements.

asynchronous generator iterator
   An object created by a *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
   location 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**.

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

awaitable
   An object that can be used in an "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.

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.

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).

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

complex number
   An extension of the familiar real number system in which all
   numbers are expressed as a sum of a real part and an imaginary
   part.  Imaginary numbers are real multiples of the imaginary unit
   (the square root of "-1"), often written "i" in mathematics or "j"
   in engineering.  Python has built-in support for complex numbers,
   which are written with this latter notation; the imaginary part is
   written with a "j" suffix, e.g., "3+1j".  To get access to complex
   equivalents of the "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.

context manager
   An object which controls the environment seen in a "with" statement
   by defining "__enter__()" and "__exit__()" methods. See **PEP
   343**.

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

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.

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.

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*.

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

   Since Python 3.3, 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 **PEP 302**, **PEP 420** and **PEP 451** for much more detail.

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

function
   A series of statements which returns some value to a caller. It can
   also be passed zero or more *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.

__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
   A function which returns a *generator iterator*.  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.

   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 *generator* function.

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

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.

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

hash-based pyc
   A bytecode cache file that uses the hash rather than the last-
   modified time of the corresponding source file to determine its
   validity. See 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 Environment for Python.  IDLE is a basic
   editor and interpreter environment which ships with the standard
   distribution of Python.

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

import path
   A list of locations (or *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)").

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.

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.lower()" 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, the
   "operator" module provides three key function constructors:
   "attrgetter()", "itemgetter()", and "methodcaller()".  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.

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.

loader
   An object that loads a module. It must define a method named
   "load_module()". A loader is typically returned by a *finder*. See
   **PEP 302** for details and "importlib.abc.Loader" for an *abstract
   base class*.

magic method
   An informal synonym for *special method*.

mapping
   A container object that supports arbitrary key lookups and
   implements the methods specified in the "Mapping" or
   "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".

MRO
   See *method resolution order*.

mutable
   Mutable objects can change their value but keep their "id()".  See
   also *immutable*.

named tuple
   The term “named tuple” applies to any type or class that inherits
   from tuple and whose indexable elements are also accessible using
   named attributes.  The type or class may have other features as
   well.

   Several built-in types are named tuples, including the values
   returned by "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
   with the factory function "collections.namedtuple()".  The latter
   technique also adds some extra methods that may not be found in
   hand-written or built-in named tuples.

namespace
   The place where a variable is stored.  Namespaces are implemented
   as dictionaries.  There are the local, global and built-in
   namespaces as well as nested namespaces in objects (in methods).
   Namespaces support modularity by preventing naming conflicts.  For
   instance, the functions "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 **PEP 420** *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.

   See also *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.

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

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

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

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**.

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_hook" 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'

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

regular package
   A traditional *package*, such as a directory containing an
   "__init__.py" file.

   See also *namespace package*.

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

sequence
   An *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 *immutable* 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()".

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.

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.

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".

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*.

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 analysis tools, and aid IDEs with code
   completion and refactoring.

   Type hints of global variables, class attributes, and functions,
   but not local variables, can be accessed using
   "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.

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.

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.
