# 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.

annotate function

  A function that can be called to retrieve the annotations of an object. This function is accessible as the `__annotate__` attribute of functions, classes, and modules. Annotate functions are a subset of evaluate functions.

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

  See variable annotation, function annotation, PEP 484, PEP 526, and PEP 649, 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

  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 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.

attached thread state

  A thread state that is active for the current OS thread.

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

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

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

attribute

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

  It is possible to give an object an attribute whose name is not an identifier as defined by Names (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.

cyclic isolate

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

data race

  A situation where multiple threads access the same memory location concurrently, at least one of the accesses is a write, and the threads do not use any synchronization to control their access. Data races lead to 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.

evaluate function

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

expression

  A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statements 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
f-strings

  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-threaded build

  A build of CPython that supports free threading, configured using the `--disable-gil` option before compilation.

  See Python support for free threading.

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

  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 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.

  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.

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

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.

  Immortal objects can be identified via `sys._is_immortal()`, or via `PyUnstable_IsImmortal()` in the C API.

immutable

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

index

  A numeric value that represents the position of an element in a sequence.

  In Python, indexing starts at zero. For example, `things[0]` names the first element of `things`; `things[1]` names the second one.

  In some contexts, Python allows negative indexes for counting from the end of a sequence, and indexing using slices.

  See also subscript.

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

  A value that identifies an entry in a mapping. See also subscript.

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.

optional module

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

  See Requirements for optional modules for a list of optional modules that require third-party libraries.

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 of type `slice`, used to describe a portion of a sequence. A slice object is created when using the slicing form of subscript notation, with colons inside square brackets, such as in `variable_name[1:3:5]`.

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.

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.

subscript

  The expression in square brackets of a subscription expression, for example, the `3` in `items[3]`. Usually used to select an element of a container. Also called a key when subscripting a mapping, or an index when subscripting a sequence.

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.

t-string
t-strings

  String literals prefixed with `t` or `T` are commonly called “t-strings” which is short for template string literals.

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 state

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

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

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

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

  See Thread State and the Global Interpreter Lock for more information.

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.