>>>The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter.
...Can refer to:
The default Python prompt of the interactive shell when entering the code for an indented code block, when within a pair of matching left and right delimiters (parentheses, square brackets, curly braces or triple quotes), or after specifying a decorator.
The Ellipsis built-in constant.
A tool that tries to convert Python 2.x code to Python 3.x code by handling most of the incompatibilities which can be detected by parsing the source and traversing the parse tree.
2to3 is available in the standard library as lib2to3; a standalone
entry point is provided as Tools/scripts/2to3. See
2to3 - Automated Python 2 to 3 code translation.
Abstract base 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.
A label associated with a variable, a class attribute or a function parameter or return value, used by convention as a type hint.
Annotations of local variables cannot be accessed at runtime, but
annotations of global variables, class attributes, and functions
are stored in the __annotations__
special attribute of modules, classes, and functions,
respectively.
See variable annotation, function annotation, PEP 484 and PEP 526, which describe this functionality.
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.
An object which controls the environment seen in an
async with statement by defining __aenter__() and
__aexit__() methods. Introduced by PEP 492.
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.
An object created by a asynchronous generator function.
This is an asynchronous iterator which when called using the
__anext__() method returns an awaitable object which will execute
the body of the asynchronous generator function until the next
yield expression.
Each yield temporarily suspends processing, remembering the
location execution state (including local variables and pending
try-statements). When the asynchronous generator iterator effectively
resumes with another awaitable returned by __anext__(), it
picks up where it left off. See PEP 492 and PEP 525.
An object, that can be used in an async for statement.
Must return an asynchronous iterator from its
__aiter__() method. Introduced by PEP 492.
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.
A value associated with an object which is referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a.
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.
Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s creator.
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.
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.
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.
A subroutine function which is passed as an argument to be executed at some point in the future.
A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class.
A variable defined in a class and intended to be modified only at class level (i.e., not in an instance of the class).
The implicit conversion of an instance of one type to another during an
operation which involves two arguments of the same type. For example,
int(3.15) converts the floating point number to the integer 3, but
in 3+4.5, each argument is of a different type (one int, one float),
and both must be converted to the same type before they can be added or it
will raise a TypeError. Without coercion, all arguments of even
compatible types would have to be normalized to the same value by the
programmer, e.g., float(3)+4.5 rather than just 3+4.5.
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.
An object which controls the environment seen in a with
statement by defining __enter__() and __exit__() methods.
See PEP 343.
A variable which can have different values depending on its context.
This is similar to Thread-Local Storage in which each execution
thread may have a different value for a variable. However, with context
variables, there may be several contexts in one execution thread and the
main usage for context variables is to keep track of variables in
concurrent asynchronous tasks.
See contextvars.
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.
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.
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.
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.
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(...):
...
f = staticmethod(f)
@staticmethod
def f(...):
...
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.
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.
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.
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.
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.
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.
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.
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.
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.
A module written in C or C++, using Python’s C API to interact with the core and with user code.
String literals prefixed with 'f' or 'F' are commonly called
“f-strings” which is short for
formatted string literals. See also PEP 498.
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.
A synonym for file object.
An object that tries to find the loader for a module that is being imported.
Since Python 3.3, there are two types of finder: meta path finders for use with sys.meta_path, and path
entry finders for use with sys.path_hooks.
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.
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.
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.
A pseudo-module which programmers can use to enable new language features which are not compatible with the current interpreter.
By importing the __future__ module and evaluating its variables,
you can see when a new feature was first added to the language and when it
becomes the default:
>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
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.
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.
An object created by a generator function.
Each yield temporarily suspends processing, remembering the
location execution state (including local variables and pending
try-statements). When the generator iterator resumes, it picks up where
it left off (in contrast to functions which start fresh on every
invocation).
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
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.
A type that can be parameterized; typically a container like
list. Used for type hints and
annotations.
See PEP 483 for more details, and typing or
generic alias type for its uses.
The mechanism used by the CPython interpreter to assure that
only one thread executes Python bytecode at a time.
This simplifies the CPython implementation by making the object model
(including critical built-in types such as dict) implicitly
safe against concurrent access. Locking the entire interpreter
makes it easier for the interpreter to be multi-threaded, at the
expense of much of the parallelism afforded by multi-processor
machines.
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.
Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.
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.
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().
An Integrated Development Environment for Python. IDLE is a basic editor and interpreter environment which ships with the standard distribution of Python.
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.
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.
The process by which Python code in one module is made available to Python code in another module.
An object that both finds and loads a module; both a finder and loader object.
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)).
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.
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.
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.
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.
A key function or collation function is a callable that returns a value
used for sorting or ordering. For example, locale.strxfrm() is
used to produce a sort key that is aware of locale specific sort
conventions.
A number of tools in Python accept key functions to control how elements
are ordered or grouped. They include min(), max(),
sorted(), list.sort(), heapq.merge(),
heapq.nsmallest(), heapq.nlargest(), and
itertools.groupby().
There are several ways to create a key function. For example. the
str.lower() method can serve as a key function for case insensitive
sorts. Alternatively, a key function can be built from a
lambda expression such as lambda r: (r[0], r[2]). Also,
the operator module provides three key function constructors:
attrgetter(), itemgetter(), and
methodcaller(). See the Sorting HOW TO for examples of how to create and use key functions.
See argument.
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
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.
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).
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.
An object that loads a module. It must define a method named
load_module(). A loader is typically returned by a
finder. See PEP 302 for details and
importlib.abc.Loader for an abstract base class.
An informal synonym for special method.
A container object that supports arbitrary key lookups and implements the
methods specified in the Mapping or
MutableMapping
abstract base classes. Examples
include dict, collections.defaultdict,
collections.OrderedDict and collections.Counter.
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.
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.
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 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.
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.
A namespace containing the import-related information used to load a
module. An instance of importlib.machinery.ModuleSpec.
Mutable objects can change their value but keep their id(). See
also immutable.
The term “named tuple” applies to any type or class that inherits from tuple and whose indexable elements are also accessible using named attributes. The type or class may have other features as well.
Several built-in types are named tuples, including the values returned
by time.localtime() and os.stat(). Another example is
sys.float_info:
>>> sys.float_info[1] # indexed access
1024
>>> sys.float_info.max_exp # named field access
1024
>>> isinstance(sys.float_info, tuple) # kind of tuple
True
Some named tuples are built-in types (such as the above examples).
Alternatively, a named tuple can be created from a regular class
definition that inherits from tuple and that defines named
fields. Such a class can be written by hand or it can be created with
the factory function collections.namedtuple(). The latter
technique also adds some extra methods that may not be found in
hand-written or built-in named tuples.
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.
A PEP 420 package which serves only as a container for
subpackages. Namespace packages may have no physical representation,
and specifically are not like a regular package because they
have no __init__.py file.
See also module.
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.
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.
Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any new-style class.
A Python module which can contain submodules or recursively,
subpackages. Technically, a package is a Python module with an
__path__ attribute.
See also regular package and namespace package.
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.
A single location on the import path which the path based finder consults to find modules for importing.
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.
A callable on the sys.path_hook list which returns a path
entry finder if it knows how to find modules on a specific path
entry.
One of the default meta path finders which searches an import path for modules.
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.
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.
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.
See argument.
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.
See provisional API.
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”.
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)
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'
The number of references to an object. When the reference count of an
object drops to zero, it is deallocated. Reference counting is
generally not visible to Python code, but it is a key element of the
CPython implementation. The sys module defines a
getrefcount() function that programmers can call to return the
reference count for a particular object.
A traditional package, such as a directory containing an
__init__.py file.
See also namespace package.
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.
An iterable which supports efficient element access using integer
indices via the __getitem__() special method and defines a
__len__() method that returns the length of the sequence.
Some built-in sequence types are list, str,
tuple, and bytes. Note that dict also
supports __getitem__() and __len__(), but is considered a
mapping rather than a sequence because the lookups use arbitrary
immutable keys rather than integers.
The collections.abc.Sequence abstract base class
defines a much richer interface that goes beyond just
__getitem__() and __len__(), adding count(),
index(), __contains__(), and
__reversed__(). Types that implement this expanded
interface can be registered explicitly using
register().
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.
A form of generic function dispatch where the implementation is chosen based on the type of a single argument.
An object usually containing a portion of a sequence. A slice is
created using the subscript notation, [] with colons between numbers
when several are given, such as in variable_name[1:3:5]. The bracket
(subscript) notation uses slice objects internally.
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.
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.
A codec which encodes Unicode strings to bytes.
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.
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.
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).
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
An annotation that specifies the expected type for a variable, a class attribute, or a function parameter or return value.
Type hints are optional and are not enforced by Python but they are useful to static type analysis tools, and aid IDEs with code completion and refactoring.
Type hints of global variables, class attributes, and functions,
but not local variables, can be accessed using
typing.get_type_hints().
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.
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.
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.
A computer defined entirely in software. Python’s virtual machine executes the bytecode emitted by the bytecode compiler.
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.