Glossary — Python 3.14.4 documentation

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3.14.4 Documentation
Glossary
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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,
and
are both keyword
arguments in the following calls to
complex()
complex
real
imag
complex
**
'real'
'imag'
})
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,
and
are both positional arguments in the
following calls:
complex
complex
))
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,
+=
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
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
has an attribute
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
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
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
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
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
and
are
free variables
, but only
is a
closure variable
def
outer
():
def
inner
():
nonlocal
+=
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
in mathematics or
in
engineering. Python has built-in support for complex numbers, which are
written with this latter notation; the imaginary part is written with a
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
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
arg
):
...
staticmethod
@staticmethod
def
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
in
the class dictionary for
, but if
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:
**
for
in
range(10)}
generates a dictionary containing key
mapped to
value
**
. 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
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
f-strings
String literals prefixed with
or
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
//
evaluates to
in contrast to the
2.75
returned by float true
division. Note that
(-11)
//
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
int
int
->
int
return
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__
future statement
from
__future__
import

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
for
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
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
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
(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
in
range(256)
if
==
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
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
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
# 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
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
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
finder
returned by a callable on
sys.path_hooks
(i.e. a
path entry hook
) which knows how to locate modules given
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
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
in
range
len
food
)):
food
])
As opposed to the cleaner, Pythonic method:
for
piece
in
food
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
...
class
...
def
meth
self
):
...
pass
...
>>>
__qualname__
'C'
>>>
__qualname__
'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
in
'abracadabra'
if
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
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
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
or
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
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
field
'annotation'
Variable annotations are usually used for
type hints
: for example this variable is expected to take
int
values:
count
int
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.
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