>>>...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.
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 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.
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 a 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
that 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.
async for statement.
Must return an asynchronous iterator from its
__aiter__() method. Introduced by PEP 492.__aiter__() and __anext__()
methods. __anext__ must return an awaitable object.
async for resolves awaitable returned from asynchronous
iterator’s __anext__() method until it raises
StopAsyncIteration exception. Introduced by PEP 492.await expression. Can be
a coroutine or an object with an __await__() method.
See also PEP 492.A file object able to read and write bytes-like objects.
An object that supports the bufferobjects 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 and .pyo 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.
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.-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.with
statement by defining __enter__() and __exit__() methods.
See PEP 343.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.
async def statement. See also
PEP 492.async def statement,
and may contain await, async for, and
async with keywords. These were introduced
by PEP 492.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.
__hash__() and __eq__() methods.
Called a hash in Perl.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.__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.type() or
isinstance(). (Note, however, that duck-typing can be complemented
with abstract base classes.) Instead, it
typically employs hasattr() tests or EAFP programming.try and except
statements. The technique contrasts with the LBYL style
common to many other languages such as C.if. Assignments are also statements,
not expressions.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.
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.
//. 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.An arbitrary metadata value associated with a function parameter or return
value. Its syntax is explained in section Function definitions. Annotations
may be accessed via the __annotations__ special attribute of a
function object.
Python itself does not assign any particular meaning to function annotations. They are intended to be interpreted by third-party libraries or tools. See PEP 3107, which describes some of their potential uses.
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)
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 expression defining a loop variable, range,
and an optional if expression. 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.
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.
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.
All of Python’s immutable built-in objects are hashable, while no mutable
containers (such as lists or dictionaries) are. 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().
sys.path, but
for subpackages it may also come from the parent package’s __path__
attribute.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)).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.
list, str,
and tuple) and some non-sequence types like dict,
file objects, and objects of any classes you define
with an __iter__() or __getitem__() method. 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.
lambda [arguments]: expressionLook 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.
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.load_module(). A loader is typically returned by a
finder. See PEP 302 for details and
importlib.abc.Loader for an abstract base class.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.
self).
See function and nested scope.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.
importlib.machinery.ModuleSpec.id(). See
also immutable.Any tuple-like class whose indexable elements are also accessible using
named attributes (for example, time.localtime() returns a
tuple-like object where the year is accessible either with an
index such as t[0] or with a named attribute like t.tm_year).
A named tuple can be a built-in type such as time.struct_time,
or it can be created with a regular class definition. A full featured
named tuple can also be created with the factory function
collections.namedtuple(). The latter approach automatically
provides extra features such as a self-documenting representation like
Employee(name='jones', title='programmer').
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.
nonlocal allows writing to outer
scopes.__slots__, descriptors,
properties, __getattribute__(), class methods, and static methods.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): ...
abs()).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 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.
sys.path_hook list which returns a path
entry finder if it knows how to find modules on a specific path
entry.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.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.
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'
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.
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().
[] with colons between numbers
when several are given, such as in variable_name[1:3:5]. The bracket
(subscript) notation uses slice objects internally.if, while or for._make() or
_asdict(). Examples of struct sequences
include sys.float_info and the return value of os.stat().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.
See also
A binary file reads and write bytes objects.
__class__ attribute or can be retrieved with
type(obj).'\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.A type metadata value associated with a module global variable or
a class attribute. Its syntax is explained in section Annotated assignment statements.
Annotations are stored in the __annotations__ special
attribute of a class or module object and can be accessed using
typing.get_type_hints().
Python itself does not assign any particular meaning to variable annotations. They are intended to be interpreted by third-party libraries or type checking tools. See PEP 526, PEP 484 which describe some of their potential uses.
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.
import this” at the interactive prompt.