Expressions

6. Expressions¶
This chapter explains the meaning of the elements of expressions in Python.
Syntax Notes: In this and the following chapters, grammar notation will be used to describe syntax, not lexical analysis.
When (one alternative of) a syntax rule has the form:
name: othername
and no semantics are given, the semantics of this form of name
are the same
as for othername
.
6.1. Arithmetic conversions¶
When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common real type”, this means that the operator implementation for built-in numeric types works as described in the Numeric Types section of the standard library documentation.
Some additional rules apply for certain operators and non-numeric operands
(for example, a string as a left argument to the %
operator).
Extensions must define their own conversion behavior.
6.2. Atoms¶
Atoms are the most basic elements of expressions. The simplest atoms are names or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms.
Formally, the syntax for atoms is:
atom: | 'True' | 'False' | 'None' | '...' |identifier
|literal
|enclosure
enclosure: |parenth_form
|list_display
|dict_display
|set_display
|generator_expression
|yield_atom
6.2.1. Built-in constants¶
The keywords True
, False
, and None
name
built-in constants.
The token ...
names the Ellipsis
constant.
Evaluation of these atoms yields the corresponding value.
Note
Several more built-in constants are available as global variables, but only the ones mentioned here are keywords. In particular, these names cannot be reassigned or used as attributes:
>>> False = 123
File "<input>", line 1
False = 123
^^^^^
SyntaxError: cannot assign to False
6.2.2. Identifiers (Names)¶
An identifier occurring as an atom is a name. See section Names (identifiers and keywords) for lexical definition and section Naming and binding for documentation of naming and binding.
When the name is bound to an object, evaluation of the atom yields that object.
When a name is not bound, an attempt to evaluate it raises a NameError
exception.
6.2.2.1. Private name mangling¶
When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class.
See also
The class specifications.
More precisely, private names are transformed to a longer form before code is generated for them. If the transformed name is longer than 255 characters, implementation-defined truncation may happen.
The transformation is independent of the syntactical context in which the identifier is used but only the following private identifiers are mangled:
Any name used as the name of a variable that is assigned or read or any name of an attribute being accessed.
The
__name__
attribute of nested functions, classes, and type aliases is however not mangled.The name of imported modules, e.g.,
__spam
inimport __spam
. If the module is part of a package (i.e., its name contains a dot), the name is not mangled, e.g., the__foo
inimport __foo.bar
is not mangled.The name of an imported member, e.g.,
__f
infrom spam import __f
.
The transformation rule is defined as follows:
The class name, with leading underscores removed and a single leading underscore inserted, is inserted in front of the identifier, e.g., the identifier
__spam
occurring in a class namedFoo
,_Foo
or__Foo
is transformed to_Foo__spam
.If the class name consists only of underscores, the transformation is the identity, e.g., the identifier
__spam
occurring in a class named_
or__
is left as is.
6.2.3. Literals¶
A literal is a textual representation of a value. Python supports numeric, string and bytes literals. Format strings and template strings are treated as string literals.
Numeric literals consist of a single NUMBER
token, which names an integer, floating-point number, or an imaginary number.
See the Numeric literals section in Lexical analysis documentation for details.
String and bytes literals may consist of several tokens. See section String literal concatenation for details.
Note that negative and complex numbers, like -3
or 3+4.2j
,
are syntactically not literals, but unary or
binary arithmetic operations involving the -
or +
operator.
Evaluation of a literal yields an object of the given type
(int
, float
, complex
, str
,
bytes
, or Template
) with the given value.
The value may be approximated in the case of floating-point
and imaginary literals.
The formal grammar for literals is:
literal:strings
|NUMBER
6.2.3.1. Literals and object identity¶
All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.
CPython implementation detail
For example, in CPython, small integers with the same value evaluate to the same object:
>>> x = 7
>>> y = 7
>>> x is y
True
However, large integers evaluate to different objects:
>>> x = 123456789
>>> y = 123456789
>>> x is y
False
This behavior may change in future versions of CPython. In particular, the boundary between “small” and “large” integers has already changed in the past.
CPython will emit a SyntaxWarning
when you compare literals
using is
:
>>> x = 7
>>> x is 7
<input>:1: SyntaxWarning: "is" with 'int' literal. Did you mean "=="?
True
See When can I rely on identity tests with the is operator? for more information.
Template strings are immutable but may reference mutable
objects as Interpolation
values.
For the purposes of this section, two t-strings have the “same value” if
both their structure and the identity of the values match.
CPython implementation detail: Currently, each evaluation of a template string results in a different object.
6.2.3.2. String literal concatenation¶
Multiple adjacent string or bytes literals, possibly using different quoting conventions, are allowed, and their meaning is the same as their concatenation:
>>> "hello" 'world'
"helloworld"
This feature is defined at the syntactical level, so it only works with literals. To concatenate string expressions at run time, the ‘+’ operator may be used:
>>> greeting = "Hello"
>>> space = " "
>>> name = "Blaise"
>>> print(greeting + space + name) # not: print(greeting space name)
Hello Blaise
Literal concatenation can freely mix raw strings, triple-quoted strings, and formatted string literals. For example:
>>> "Hello" r', ' f"{name}!"
"Hello, Blaise!"
This feature can be used to reduce the number of backslashes needed, to split long strings conveniently across long lines, or even to add comments to parts of strings. For example:
re.compile("[A-Za-z_]" # letter or underscore
"[A-Za-z0-9_]*" # letter, digit or underscore
)
However, bytes literals may only be combined with other byte literals; not with string literals of any kind. Also, template string literals may only be combined with other template string literals:
>>> t"Hello" t"{name}!"
Template(strings=('Hello', '!'), interpolations=(...))
Formally:
strings: (STRING
|fstring
)+ |tstring
+
6.2.4. Parenthesized forms¶
A parenthesized form is an optional expression list enclosed in parentheses:
parenth_form: "(" [starred_expression
] ")"
A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.
An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).
Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.
6.2.5. Displays for lists, sets and dictionaries¶
For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:
either the container contents are listed explicitly, or
they are computed via a set of looping and filtering instructions, called a comprehension.
Common syntax elements for comprehensions are:
comprehension:assignment_expression
comp_for
comp_for: ["async"] "for"target_list
"in"or_test
[comp_iter
] comp_iter:comp_for
|comp_if
comp_if: "if"or_test
[comp_iter
]
The comprehension consists of a single expression followed by at least one
for
clause and zero or more for
or if
clauses.
In this case, the elements of the new container are those that would be produced
by considering each of the for
or if
clauses a block,
nesting from left to right, and evaluating the expression to produce an element
each time the innermost block is reached.
However, aside from the iterable expression in the leftmost for
clause,
the comprehension is executed in a separate implicitly nested scope. This ensures
that names assigned to in the target list don’t “leak” into the enclosing scope.
The iterable expression in the leftmost for
clause is evaluated
directly in the enclosing scope and then passed as an argument to the implicitly
nested scope. Subsequent for
clauses and any filter condition in the
leftmost for
clause cannot be evaluated in the enclosing scope as
they may depend on the values obtained from the leftmost iterable. For example:
[x*y for x in range(10) for y in range(x, x+10)]
.
To ensure the comprehension always results in a container of the appropriate
type, yield
and yield from
expressions are prohibited in the implicitly
nested scope.
Since Python 3.6, in an async def
function, an async for
clause may be used to iterate over a asynchronous iterator.
A comprehension in an async def
function may consist of either a
for
or async for
clause following the leading
expression, may contain additional for
or async for
clauses, and may also use await
expressions.
If a comprehension contains async for
clauses, or if it contains
await
expressions or other asynchronous comprehensions anywhere except
the iterable expression in the leftmost for
clause, it is called an
asynchronous comprehension. An asynchronous comprehension may suspend the
execution of the coroutine function in which it appears.
See also PEP 530.
Added in version 3.6: Asynchronous comprehensions were introduced.
Changed in version 3.8: yield
and yield from
prohibited in the implicitly nested scope.
Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.
6.2.6. List displays¶
A list display is a possibly empty series of expressions enclosed in square brackets:
list_display: "[" [flexible_expression_list
|comprehension
] "]"
A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.
6.2.7. Set displays¶
A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:
set_display: "{" (flexible_expression_list
|comprehension
) "}"
A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.
An empty set cannot be constructed with {}
; this literal constructs an empty
dictionary.
6.2.8. Dictionary displays¶
A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:
dict_display: "{" [dict_item_list
|dict_comprehension
] "}" dict_item_list:dict_item
(","dict_item
)* [","] dict_item:expression
":"expression
| "**"or_expr
dict_comprehension:expression
":"expression
comp_for
A dictionary display yields a new dictionary object.
If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.
A double asterisk **
denotes dictionary unpacking.
Its operand must be a mapping. Each mapping item is added
to the new dictionary. Later values replace values already set by
earlier dict items and earlier dictionary unpackings.
Added in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448.
A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.
Restrictions on the types of the key values are listed earlier in section The standard type hierarchy. (To summarize, the key type should be hashable, which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.
Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572.
6.2.9. Generator expressions¶
A generator expression is a compact generator notation in parentheses:
generator_expression: "("expression
comp_for
")"
A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.
Variables used in the generator expression are evaluated lazily when the
__next__()
method is called for the generator object (in the same
fashion as normal generators). However, the iterable expression in the
leftmost for
clause is immediately evaluated, and the
iterator is immediately created for that iterable, so that an error
produced while creating the iterator will be emitted at the point where the generator expression
is defined, rather than at the point where the first value is retrieved.
Subsequent for
clauses and any filter condition in the leftmost
for
clause cannot be evaluated in the enclosing scope as they may
depend on the values obtained from the leftmost iterable. For example:
(x*y for x in range(10) for y in range(x, x+10))
.
The parentheses can be omitted on calls with only one argument. See section Calls for details.
To avoid interfering with the expected operation of the generator expression
itself, yield
and yield from
expressions are prohibited in the
implicitly defined generator.
If a generator expression contains either async for
clauses or await
expressions it is called an
asynchronous generator expression. An asynchronous generator
expression returns a new asynchronous generator object,
which is an asynchronous iterator (see Asynchronous Iterators).
Added in version 3.6: Asynchronous generator expressions were introduced.
Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could
only appear in async def
coroutines. Starting
with 3.7, any function can use asynchronous generator expressions.
Changed in version 3.8: yield
and yield from
prohibited in the implicitly nested scope.
6.2.10. Yield expressions¶
yield_atom: "("yield_expression
")" yield_from: "yield" "from"expression
yield_expression: "yield"yield_list
|yield_from
The yield expression is used when defining a generator function
or an asynchronous generator function and
thus can only be used in the body of a function definition. Using a yield
expression in a function’s body causes that function to be a generator function,
and using it in an async def
function’s body causes that
coroutine function to be an asynchronous generator function. For example:
def gen(): # defines a generator function
yield 123
async def agen(): # defines an asynchronous generator function
yield 123
Due to their side effects on the containing scope, yield
expressions
are not permitted as part of the implicitly defined scopes used to
implement comprehensions and generator expressions.
Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.
Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions.
When a generator function is called, it returns an iterator known as a
generator. That generator then controls the execution of the generator
function. The execution starts when one of the generator’s methods is called.
At that time, the execution proceeds to the first yield expression, where it is
suspended again, returning the value of yield_list
to the generator’s caller,
or None
if yield_list
is omitted.
By suspended, we mean that all local state is
retained, including the current bindings of local variables, the instruction
pointer, the internal evaluation stack, and the state of any exception handling.
When the execution is resumed by calling one of the generator’s methods, the
function can proceed exactly as if the yield expression were just another
external call. The value of the yield expression after resuming depends on the
method which resumed the execution. If __next__()
is used
(typically via either a for
or the next()
builtin) then the
result is None
. Otherwise, if send()
is used, then
the result will be the value passed in to that method.
All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.
Yield expressions are allowed anywhere in a try
construct. If the
generator is not resumed before it is
finalized (by reaching a zero reference count or by being garbage collected),
the generator-iterator’s close()
method will be called,
allowing any pending finally
clauses to execute.
When yield from <expr>
is used, the supplied expression must be an
iterable. The values produced by iterating that iterable are passed directly
to the caller of the current generator’s methods. Any values passed in with
send()
and any exceptions passed in with
throw()
are passed to the underlying iterator if it has the
appropriate methods. If this is not the case, then send()
will raise AttributeError
or TypeError
, while
throw()
will just raise the passed in exception immediately.
When the underlying iterator is complete, the value
attribute of the raised StopIteration
instance becomes the value of
the yield expression. It can be either set explicitly when raising
StopIteration
, or automatically when the subiterator is a generator
(by returning a value from the subgenerator).
Changed in version 3.3: Added yield from <expr>
to delegate control flow to a subiterator.
The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.
See also
- PEP 255 - Simple Generators
The proposal for adding generators and the
yield
statement to Python.- PEP 342 - Coroutines via Enhanced Generators
The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.
- PEP 380 - Syntax for Delegating to a Subgenerator
The proposal to introduce the
yield_from
syntax, making delegation to subgenerators easy.- PEP 525 - Asynchronous Generators
The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.
6.2.10.1. Generator-iterator methods¶
This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.
Note that calling any of the generator methods below when the generator
is already executing raises a ValueError
exception.
- generator.__next__()¶
Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a
__next__()
method, the current yield expression always evaluates toNone
. The execution then continues to the next yield expression, where the generator is suspended again, and the value of theyield_list
is returned to__next__()
’s caller. If the generator exits without yielding another value, aStopIteration
exception is raised.This method is normally called implicitly, e.g. by a
for
loop, or by the built-innext()
function.
- generator.send(value)¶
Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The
send()
method returns the next value yielded by the generator, or raisesStopIteration
if the generator exits without yielding another value. Whensend()
is called to start the generator, it must be called withNone
as the argument, because there is no yield expression that could receive the value.
- generator.throw(value)¶
- generator.throw(type[, value[, traceback]])
Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a
StopIteration
exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.In typical use, this is called with a single exception instance similar to the way the
raise
keyword is used.For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing
__traceback__
attribute stored in value may be cleared.Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.
- generator.close()¶
Raises a
GeneratorExit
exception at the point where the generator function was paused (equivalent to callingthrow(GeneratorExit)
). The exception is raised by the yield expression where the generator was paused. If the generator function catches the exception and returns a value, this value is returned fromclose()
. If the generator function is already closed, or raisesGeneratorExit
(by not catching the exception),close()
returnsNone
. If the generator yields a value, aRuntimeError
is raised. If the generator raises any other exception, it is propagated to the caller. If the generator has already exited due to an exception or normal exit,close()
returnsNone
and has no other effect.Changed in version 3.13: If a generator returns a value upon being closed, the value is returned by
close()
.
6.2.10.2. Examples¶
Here is a simple example that demonstrates the behavior of generators and generator functions:
>>> def echo(value=None):
... print("Execution starts when 'next()' is called for the first time.")
... try:
... while True:
... try:
... value = (yield value)
... except Exception as e:
... value = e
... finally:
... print("Don't forget to clean up when 'close()' is called.")
...
>>> generator = echo(1)
>>> print(next(generator))
Execution starts when 'next()' is called for the first time.
1
>>> print(next(generator))
None
>>> print(generator.send(2))
2
>>> generator.throw(TypeError, "spam")
TypeError('spam',)
>>> generator.close()
Don't forget to clean up when 'close()' is called.
For examples using yield from
, see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in
Python.”
6.2.10.3. Asynchronous generator functions¶
The presence of a yield expression in a function or method defined using
async def
further defines the function as an
asynchronous generator function.
When an asynchronous generator function is called, it returns an
asynchronous iterator known as an asynchronous generator object.
That object then controls the execution of the generator function.
An asynchronous generator object is typically used in an
async for
statement in a coroutine function analogously to
how a generator object would be used in a for
statement.
Calling one of the asynchronous generator’s methods returns an awaitable
object, and the execution starts when this object is awaited on. At that time,
the execution proceeds to the first yield expression, where it is suspended
again, returning the value of yield_list
to the
awaiting coroutine. As with a generator, suspension means that all local state
is retained, including the current bindings of local variables, the instruction
pointer, the internal evaluation stack, and the state of any exception handling.
When the execution is resumed by awaiting on the next object returned by the
asynchronous generator’s methods, the function can proceed exactly as if the
yield expression were just another external call. The value of the yield
expression after resuming depends on the method which resumed the execution. If
__anext__()
is used then the result is None
. Otherwise, if
asend()
is used, then the result will be the value passed in to that
method.
If an asynchronous generator happens to exit early by break
, the caller
task being cancelled, or other exceptions, the generator’s async cleanup code
will run and possibly raise exceptions or access context variables in an
unexpected context–perhaps after the lifetime of tasks it depends, or
during the event loop shutdown when the async-generator garbage collection hook
is called.
To prevent this, the caller must explicitly close the async generator by calling
aclose()
method to finalize the generator and ultimately detach it
from the event loop.
In an asynchronous generator function, yield expressions are allowed anywhere
in a try
construct. However, if an asynchronous generator is not
resumed before it is finalized (by reaching a zero reference count or by
being garbage collected), then a yield expression within a try
construct could result in a failure to execute pending finally
clauses. In this case, it is the responsibility of the event loop or
scheduler running the asynchronous generator to call the asynchronous
generator-iterator’s aclose()
method and run the resulting
coroutine object, thus allowing any pending finally
clauses
to execute.
To take care of finalization upon event loop termination, an event loop should
define a finalizer function which takes an asynchronous generator-iterator and
presumably calls aclose()
and executes the coroutine.
This finalizer may be registered by calling sys.set_asyncgen_hooks()
.
When first iterated over, an asynchronous generator-iterator will store the
registered finalizer to be called upon finalization. For a reference example
of a finalizer method see the implementation of
asyncio.Loop.shutdown_asyncgens
in Lib/asyncio/base_events.py.
The expression yield from <expr>
is a syntax error when used in an
asynchronous generator function.
6.2.10.4. Asynchronous generator-iterator methods¶
This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.
- async agen.__anext__()¶
Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an
__anext__()
method, the current yield expression always evaluates toNone
in the returned awaitable, which when run will continue to the next yield expression. The value of theyield_list
of the yield expression is the value of theStopIteration
exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises aStopAsyncIteration
exception, signalling that the asynchronous iteration has completed.This method is normally called implicitly by a
async for
loop.
- async agen.asend(value)¶
Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the
send()
method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by theasend()
method will return the next value yielded by the generator as the value of the raisedStopIteration
, or raisesStopAsyncIteration
if the asynchronous generator exits without yielding another value. Whenasend()
is called to start the asynchronous generator, it must be called withNone
as the argument, because there is no yield expression that could receive the value.
- async agen.athrow(value)¶
- async agen.athrow(type[, value[, traceback]])
Returns an awaitable that raises an exception of type
type
at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raisedStopIteration
exception. If the asynchronous generator exits without yielding another value, aStopAsyncIteration
exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.
- async agen.aclose()¶
Returns an awaitable that when run will throw a
GeneratorExit
into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raisesGeneratorExit
(by not catching the exception), then the returned awaitable will raise aStopIteration
exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise aStopAsyncIteration
exception. If the asynchronous generator yields a value, aRuntimeError
is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls toaclose()
will return an awaitable that does nothing.
6.3. Primaries¶
Primaries represent the most tightly bound operations of the language. Their syntax is:
primary:atom
|attributeref
|subscription
|call
6.3.1. Attribute references¶
An attribute reference is a primary followed by a period and a name:
attributeref:primary
"."identifier
The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.
This production can be customized by overriding the
__getattribute__()
method or the __getattr__()
method. The __getattribute__()
method is called first and either
returns a value or raises AttributeError
if the attribute is not
available.
If an AttributeError
is raised and the object has a __getattr__()
method, that method is called as a fallback.
6.3.2. Subscriptions and slicings¶
The subscription syntax is usually used for selecting an element from a
container – for example, to get a value from
a dict
:
>>> digits_by_name = {'one': 1, 'two': 2}
>>> digits_by_name['two'] # Subscripting a dictionary using the key 'two'
2
In the subscription syntax, the object being subscribed – a primary – is followed by a subscript in square brackets. In the simplest case, the subscript is a single expression.
Depending on the type of the object being subscribed, the subscript is sometimes called a key (for mappings), index (for sequences), or type argument (for generic types). Syntactically, these are all equivalent:
>>> colors = ['red', 'blue', 'green', 'black']
>>> colors[3] # Subscripting a list using the index 3
'black'
>>> list[str] # Parameterizing the list type using the type argument str
list[str]
At runtime, the interpreter will evaluate the primary and
the subscript, and call the primary’s __getitem__()
or
__class_getitem__()
special method with the subscript
as argument.
For more details on which of these methods is called, see
__class_getitem__ versus __getitem__.
To show how subscription works, we can define a custom object that
implements __getitem__()
and prints out the value of
the subscript:
>>> class SubscriptionDemo:
... def __getitem__(self, key):
... print(f'subscripted with: {key!r}')
...
>>> demo = SubscriptionDemo()
>>> demo[1]
subscripted with: 1
>>> demo['a' * 3]
subscripted with: 'aaa'
See __getitem__()
documentation for how built-in types handle
subscription.
Subscriptions may also be used as targets in assignment or
deletion statements.
In these cases, the interpreter will call the subscripted object’s
__setitem__()
or __delitem__()
special method, respectively, instead of __getitem__()
.
>>> colors = ['red', 'blue', 'green', 'black']
>>> colors[3] = 'white' # Setting item at index
>>> colors
['red', 'blue', 'green', 'white']
>>> del colors[3] # Deleting item at index 3
>>> colors
['red', 'blue', 'green']
All advanced forms of subscript documented in the following sections are also usable for assignment and deletion.
6.3.2.1. Slicings¶
A more advanced form of subscription, slicing, is commonly used to extract a portion of a sequence. In this form, the subscript is a slice: up to three expressions separated by colons. Any of the expressions may be omitted, but a slice must contain at least one colon:
>>> number_names = ['zero', 'one', 'two', 'three', 'four', 'five']
>>> number_names[1:3]
['one', 'two']
>>> number_names[1:]
['one', 'two', 'three', 'four', 'five']
>>> number_names[:3]
['zero', 'one', 'two']
>>> number_names[:]
['zero', 'one', 'two', 'three', 'four', 'five']
>>> number_names[::2]
['zero', 'two', 'four']
>>> number_names[:-3]
['zero', 'one', 'two']
>>> del number_names[4:]
>>> number_names
['zero', 'one', 'two', 'three']
When a slice is evaluated, the interpreter constructs a slice
object
whose start
, stop
and
step
attributes, respectively, are the results of the
expressions between the colons.
Any missing expression evaluates to None
.
This slice
object is then passed to the __getitem__()
or __class_getitem__()
special method, as above.
# continuing with the SubscriptionDemo instance defined above:
>>> demo[2:3]
subscripted with: slice(2, 3, None)
>>> demo[::'spam']
subscripted with: slice(None, None, 'spam')
6.3.2.2. Comma-separated subscripts¶
The subscript can also be given as two or more comma-separated expressions or slices:
# continuing with the SubscriptionDemo instance defined above:
>>> demo[1, 2, 3]
subscripted with: (1, 2, 3)
>>> demo[1:2, 3]
subscripted with: (slice(1, 2, None), 3)
This form is commonly used with numerical libraries for slicing
multi-dimensional data.
In this case, the interpreter constructs a tuple
of the results of the
expressions or slices, and passes this tuple to the __getitem__()
or __class_getitem__()
special method, as above.
The subscript may also be given as a single expression or slice followed by a comma, to specify a one-element tuple:
>>> demo['spam',]
subscripted with: ('spam',)
6.3.2.3. “Starred” subscriptions¶
Added in version 3.11: Expressions in tuple_slices may be starred. See PEP 646.
The subscript can also contain a starred expression.
In this case, the interpreter unpacks the result into a tuple, and passes
this tuple to __getitem__()
or __class_getitem__()
:
# continuing with the SubscriptionDemo instance defined above:
>>> demo[*range(10)]
subscripted with: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
Starred expressions may be combined with comma-separated expressions and slices:
>>> demo['a', 'b', *range(3), 'c']
subscripted with: ('a', 'b', 0, 1, 2, 'c')
6.3.2.4. Formal subscription grammar¶
subscription:primary
'['subscript
']' subscript:single_subscript
|tuple_subscript
single_subscript:proper_slice
|assignment_expression
proper_slice: [expression
] ":" [expression
] [ ":" [expression
] ] tuple_subscript: ','.(single_subscript
|starred_expression
)+ [',']
Recall that the |
operator denotes ordered choice.
Specifically, in subscript
, if both alternatives would match, the
first (single_subscript
) has priority.
6.3.3. Calls¶
A call calls a callable object (e.g., a function) with a possibly empty series of arguments:
call:primary
"(" [argument_list
[","] |comprehension
] ")" argument_list:positional_arguments
[","starred_and_keywords
] [","keywords_arguments
] |starred_and_keywords
[","keywords_arguments
] |keywords_arguments
positional_arguments:positional_item
(","positional_item
)* positional_item:assignment_expression
| "*"expression
starred_and_keywords: ("*"expression
|keyword_item
) ("," "*"expression
| ","keyword_item
)* keywords_arguments: (keyword_item
| "**"expression
) (","keyword_item
| "," "**"expression
)* keyword_item:identifier
"="expression
An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.
The primary must evaluate to a callable object (user-defined functions, built-in
functions, methods of built-in objects, class objects, methods of class
instances, and all objects having a __call__()
method are callable). All
argument expressions are evaluated before the call is attempted. Please refer
to section Function definitions for the syntax of formal parameter lists.
If keyword arguments are present, they are first converted to positional
arguments, as follows. First, a list of unfilled slots is created for the
formal parameters. If there are N positional arguments, they are placed in the
first N slots. Next, for each keyword argument, the identifier is used to
determine the corresponding slot (if the identifier is the same as the first
formal parameter name, the first slot is used, and so on). If the slot is
already filled, a TypeError
exception is raised. Otherwise, the
argument is placed in the slot, filling it (even if the expression is
None
, it fills the slot). When all arguments have been processed, the slots
that are still unfilled are filled with the corresponding default value from the
function definition. (Default values are calculated, once, when the function is
defined; thus, a mutable object such as a list or dictionary used as default
value will be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.) If there are any unfilled
slots for which no default value is specified, a TypeError
exception is
raised. Otherwise, the list of filled slots is used as the argument list for
the call.
CPython implementation detail: An implementation may provide built-in functions whose positional parameters
do not have names, even if they are ‘named’ for the purpose of documentation,
and which therefore cannot be supplied by keyword. In CPython, this is the
case for functions implemented in C that use PyArg_ParseTuple()
to
parse their arguments.
If there are more positional arguments than there are formal parameter slots, a
TypeError
exception is raised, unless a formal parameter using the syntax
*identifier
is present; in this case, that formal parameter receives a tuple
containing the excess positional arguments (or an empty tuple if there were no
excess positional arguments).
If any keyword argument does not correspond to a formal parameter name, a
TypeError
exception is raised, unless a formal parameter using the syntax
**identifier
is present; in this case, that formal parameter receives a
dictionary containing the excess keyword arguments (using the keywords as keys
and the argument values as corresponding values), or a (new) empty dictionary if
there were no excess keyword arguments.
If the syntax *expression
appears in the function call, expression
must
evaluate to an iterable. Elements from these iterables are
treated as if they were additional positional arguments. For the call
f(x1, x2, *y, x3, x4)
, if y evaluates to a sequence y1, …, yM,
this is equivalent to a call with M+4 positional arguments x1, x2,
y1, …, yM, x3, x4.
A consequence of this is that although the *expression
syntax may appear
after explicit keyword arguments, it is processed before the
keyword arguments (and any **expression
arguments – see below). So:
>>> def f(a, b):
... print(a, b)
...
>>> f(b=1, *(2,))
2 1
>>> f(a=1, *(2,))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() got multiple values for keyword argument 'a'
>>> f(1, *(2,))
1 2
It is unusual for both keyword arguments and the *expression
syntax to be
used in the same call, so in practice this confusion does not often arise.
If the syntax **expression
appears in the function call, expression
must
evaluate to a mapping, the contents of which are treated as
additional keyword arguments. If a parameter matching a key has already been
given a value (by an explicit keyword argument, or from another unpacking),
a TypeError
exception is raised.
When **expression
is used, each key in this mapping must be
a string.
Each value from the mapping is assigned to the first formal parameter
eligible for keyword assignment whose name is equal to the key.
A key need not be a Python identifier (e.g. "max-temp °F"
is acceptable,
although it will not match any formal parameter that could be declared).
If there is no match to a formal parameter
the key-value pair is collected by the **
parameter, if there is one,
or if there is not, a TypeError
exception is raised.
Formal parameters using the syntax *identifier
or **identifier
cannot be
used as positional argument slots or as keyword argument names.
Changed in version 3.5: Function calls accept any number of *
and **
unpackings,
positional arguments may follow iterable unpackings (*
),
and keyword arguments may follow dictionary unpackings (**
).
Originally proposed by PEP 448.
A call always returns some value, possibly None
, unless it raises an
exception. How this value is computed depends on the type of the callable
object.
If it is—
- a user-defined function:
The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions. When the code block executes a
return
statement, this specifies the return value of the function call. If execution reaches the end of the code block without executing areturn
statement, the return value isNone
.- a built-in function or method:
The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.
- a class object:
A new instance of that class is returned.
- a class instance method:
The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.
- a class instance:
The class must define a
__call__()
method; the effect is then the same as if that method was called.
6.4. Await expression¶
Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function.
await_expr: "await" primary
Added in version 3.5.
6.5. The power operator¶
The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:
power: (await_expr
|primary
) ["**"u_expr
]
Thus, in an unparenthesized sequence of power and unary operators, the operators
are evaluated from right to left (this does not constrain the evaluation order
for the operands): -1**2
results in -1
.
The power operator has the same semantics as the built-in pow()
function,
when called with two arguments: it yields its left argument raised to the power
of its right argument.
Numeric arguments are first converted to a common type,
and the result is of that type.
For int operands, the result has the same type as the operands unless the second
argument is negative; in that case, all arguments are converted to float and a
float result is delivered. For example, 10**2
returns 100
, but
10**-2
returns 0.01
.
Raising 0.0
to a negative power results in a ZeroDivisionError
.
Raising a negative number to a fractional power results in a complex
number. (In earlier versions it raised a ValueError
.)
This operation can be customized using the special __pow__()
and
__rpow__()
methods.
6.6. Unary arithmetic and bitwise operations¶
All unary arithmetic and bitwise operations have the same priority:
u_expr:power
| "-"u_expr
| "+"u_expr
| "~"u_expr
The unary -
(minus) operator yields the negation of its numeric argument; the
operation can be overridden with the __neg__()
special method.
The unary +
(plus) operator yields its numeric argument unchanged; the
operation can be overridden with the __pos__()
special method.
The unary ~
(invert) operator yields the bitwise inversion of its integer
argument. The bitwise inversion of x
is defined as -(x+1)
. It only
applies to integral numbers or to custom objects that override the
__invert__()
special method.
In all three cases, if the argument does not have the proper type, a
TypeError
exception is raised.
6.7. Binary arithmetic operations¶
The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:
m_expr:u_expr
|m_expr
"*"u_expr
|m_expr
"@"m_expr
|m_expr
"//"u_expr
|m_expr
"/"u_expr
|m_expr
"%"u_expr
a_expr:m_expr
|a_expr
"+"m_expr
|a_expr
"-"m_expr
The *
(multiplication) operator yields the product of its arguments. The
arguments must either both be numbers, or one argument must be an integer and
the other must be a sequence. In the former case, the numbers are
converted to a common real type and then
multiplied together. In the latter case, sequence repetition is performed;
a negative repetition factor yields an empty sequence.
This operation can be customized using the special __mul__()
and
__rmul__()
methods.
Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.
The @
(at) operator is intended to be used for matrix multiplication. No
builtin Python types implement this operator.
This operation can be customized using the special __matmul__()
and
__rmatmul__()
methods.
Added in version 3.5.
The /
(division) and //
(floor division) operators yield the quotient of
their arguments. The numeric arguments are first
converted to a common type.
Division of integers yields a float, while floor division of integers results in an
integer; the result is that of mathematical division with the ‘floor’ function
applied to the result. Division by zero raises the ZeroDivisionError
exception.
The division operation can be customized using the special __truediv__()
and __rtruediv__()
methods.
The floor division operation can be customized using the special
__floordiv__()
and __rfloordiv__()
methods.
The %
(modulo) operator yields the remainder from the division of the first
argument by the second. The numeric arguments are first
converted to a common type.
A zero right argument raises the ZeroDivisionError
exception. The
arguments may be floating-point numbers, e.g., 3.14%0.7
equals 0.34
(since 3.14
equals 4*0.7 + 0.34
.) The modulo operator always yields a
result with the same sign as its second operand (or zero); the absolute value of
the result is strictly smaller than the absolute value of the second operand
[1].
The floor division and modulo operators are connected by the following
identity: x == (x//y)*y + (x%y)
. Floor division and modulo are also
connected with the built-in function divmod()
: divmod(x, y) == (x//y,
x%y)
. [2].
In addition to performing the modulo operation on numbers, the %
operator is
also overloaded by string objects to perform old-style string formatting (also
known as interpolation). The syntax for string formatting is described in the
Python Library Reference, section printf-style String Formatting.
The modulo operation can be customized using the special __mod__()
and __rmod__()
methods.
The floor division operator, the modulo operator, and the divmod()
function are not defined for complex numbers. Instead, convert to a
floating-point number using the abs()
function if appropriate.
The +
(addition) operator yields the sum of its arguments. The arguments
must either both be numbers or both be sequences of the same type. In the
former case, the numbers are
converted to a common real type and then
added together.
In the latter case, the sequences are concatenated.
This operation can be customized using the special __add__()
and
__radd__()
methods.
Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.
The -
(subtraction) operator yields the difference of its arguments.
The numeric arguments are first
converted to a common real type.
This operation can be customized using the special __sub__()
and
__rsub__()
methods.
Changed in version 3.14: If only one operand is a complex number, the other operand is converted to a floating-point number.
6.8. Shifting operations¶
The shifting operations have lower priority than the arithmetic operations:
shift_expr:a_expr
|shift_expr
("<<" | ">>")a_expr
These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.
The left shift operation can be customized using the special __lshift__()
and __rlshift__()
methods.
The right shift operation can be customized using the special __rshift__()
and __rrshift__()
methods.
A right shift by n bits is defined as floor division by pow(2,n)
. A left
shift by n bits is defined as multiplication with pow(2,n)
.
6.9. Binary bitwise operations¶
Each of the three bitwise operations has a different priority level:
and_expr:shift_expr
|and_expr
"&"shift_expr
xor_expr:and_expr
|xor_expr
"^"and_expr
or_expr:xor_expr
|or_expr
"|"xor_expr
The &
operator yields the bitwise AND of its arguments, which must be
integers or one of them must be a custom object overriding __and__()
or
__rand__()
special methods.
The ^
operator yields the bitwise XOR (exclusive OR) of its arguments, which
must be integers or one of them must be a custom object overriding __xor__()
or
__rxor__()
special methods.
The |
operator yields the bitwise (inclusive) OR of its arguments, which
must be integers or one of them must be a custom object overriding __or__()
or
__ror__()
special methods.
6.10. Comparisons¶
Unlike C, all comparison operations in Python have the same priority, which is
lower than that of any arithmetic, shifting or bitwise operation. Also unlike
C, expressions like a < b < c
have the interpretation that is conventional
in mathematics:
comparison:or_expr
(comp_operator
or_expr
)* comp_operator: "<" | ">" | "==" | ">=" | "<=" | "!=" | "is" ["not"] | ["not"] "in"
Comparisons yield boolean values: True
or False
. Custom
rich comparison methods may return non-boolean values. In this case
Python will call bool()
on such value in boolean contexts.
Comparisons can be chained arbitrarily, e.g., x < y <= z
is equivalent to
x < y and y <= z
, except that y
is evaluated only once (but in both
cases z
is not evaluated at all when x < y
is found to be false).
Formally, if a, b, c, …, y, z are expressions and op1, op2, …,
opN are comparison operators, then a op1 b op2 c ... y opN z
is equivalent
to a op1 b and b op2 c and ... y opN z
, except that each expression is
evaluated at most once.
Note that a op1 b op2 c
doesn’t imply any kind of comparison between a and
c, so that, e.g., x < y > z
is perfectly legal (though perhaps not
pretty).
6.10.1. Value comparisons¶
The operators <
, >
, ==
, >=
, <=
, and !=
compare the
values of two objects. The objects do not need to have the same type.
Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.
Because all types are (direct or indirect) subtypes of object
, they
inherit the default comparison behavior from object
. Types can
customize their comparison behavior by implementing
rich comparison methods like __lt__()
, described in
Basic customization.
The default behavior for equality comparison (==
and !=
) is based on
the identity of the objects. Hence, equality comparison of instances with the
same identity results in equality, and equality comparison of instances with
different identities results in inequality. A motivation for this default
behavior is the desire that all objects should be reflexive (i.e. x is y
implies x == y
).
A default order comparison (<
, >
, <=
, and >=
) is not provided;
an attempt raises TypeError
. A motivation for this default behavior is
the lack of a similar invariant as for equality.
The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.
The following list describes the comparison behavior of the most important built-in types.
Numbers of built-in numeric types (Numeric Types — int, float, complex) and of the standard library types
fractions.Fraction
anddecimal.Decimal
can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.The not-a-number values
float('NaN')
anddecimal.Decimal('NaN')
are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, ifx = float('NaN')
,3 < x
,x < 3
andx == x
are all false, whilex != x
is true. This behavior is compliant with IEEE 754.None
andNotImplemented
are singletons. PEP 8 advises that comparisons for singletons should always be done withis
oris not
, never the equality operators.Binary sequences (instances of
bytes
orbytearray
) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.Strings (instances of
str
) compare lexicographically using the numerical Unicode code points (the result of the built-in functionord()
) of their characters. [3]Strings and binary sequences cannot be directly compared.
Sequences (instances of
tuple
,list
, orrange
) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raisesTypeError
.Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.
Lexicographical comparison between built-in collections works as follows:
For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example,
[1,2] == (1,2)
is false because the type is not the same).Collections that support order comparison are ordered the same as their first unequal elements (for example,
[1,2,x] <= [1,2,y]
has the same value asx <= y
). If a corresponding element does not exist, the shorter collection is ordered first (for example,[1,2] < [1,2,3]
is true).
Mappings (instances of
dict
) compare equal if and only if they have equal(key, value)
pairs. Equality comparison of the keys and values enforces reflexivity.Order comparisons (
<
,>
,<=
, and>=
) raiseTypeError
.Sets (instances of
set
orfrozenset
) can be compared within and across their types.They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets
{1,2}
and{2,3}
are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example,min()
,max()
, andsorted()
produce undefined results given a list of sets as inputs).Comparison of sets enforces reflexivity of its elements.
Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.
User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:
Equality comparison should be reflexive. In other words, identical objects should compare equal:
x is y
impliesx == y
Comparison should be symmetric. In other words, the following expressions should have the same result:
x == y
andy == x
x != y
andy != x
x < y
andy > x
x <= y
andy >= x
Comparison should be transitive. The following (non-exhaustive) examples illustrate that:
x > y and y > z
impliesx > z
x < y and y <= z
impliesx < z
Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:
x == y
andnot x != y
x < y
andnot x >= y
(for total ordering)x > y
andnot x <= y
(for total ordering)The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the
total_ordering()
decorator.The
hash()
result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.
Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.
6.10.2. Membership test operations¶
The operators in
and not in
test for membership. x in
s
evaluates to True
if x is a member of s, and False
otherwise.
x not in s
returns the negation of x in s
. All built-in sequences and
set types support this as well as dictionary, for which in
tests
whether the dictionary has a given key. For container types such as list, tuple,
set, frozenset, dict, or collections.deque, the expression x in y
is equivalent
to any(x is e or x == e for e in y)
.
For the string and bytes types, x in y
is True
if and only if x is a
substring of y. An equivalent test is y.find(x) != -1
. Empty strings are
always considered to be a substring of any other string, so "" in "abc"
will
return True
.
For user-defined classes which define the __contains__()
method, x in
y
returns True
if y.__contains__(x)
returns a true value, and
False
otherwise.
For user-defined classes which do not define __contains__()
but do define
__iter__()
, x in y
is True
if some value z
, for which the
expression x is z or x == z
is true, is produced while iterating over y
.
If an exception is raised during the iteration, it is as if in
raised
that exception.
Lastly, the old-style iteration protocol is tried: if a class defines
__getitem__()
, x in y
is True
if and only if there is a non-negative
integer index i such that x is y[i] or x == y[i]
, and no lower integer index
raises the IndexError
exception. (If any other exception is raised, it is as
if in
raised that exception).
The operator not in
is defined to have the inverse truth value of
in
.
6.10.3. Identity comparisons¶
The operators is
and is not
test for an object’s identity: x
is y
is true if and only if x and y are the same object. An Object’s identity
is determined using the id()
function. x is not y
yields the inverse
truth value. [4]
6.11. Boolean operations¶
or_test:and_test
|or_test
"or"and_test
and_test:not_test
|and_test
"and"not_test
not_test:comparison
| "not"not_test
In the context of Boolean operations, and also when expressions are used by
control flow statements, the following values are interpreted as false:
False
, None
, numeric zero of all types, and empty strings and containers
(including strings, tuples, lists, dictionaries, sets and frozensets). All
other values are interpreted as true. User-defined objects can customize their
truth value by providing a __bool__()
method.
The operator not
yields True
if its argument is false, False
otherwise.
The expression x and y
first evaluates x; if x is false, its value is
returned; otherwise, y is evaluated and the resulting value is returned.
The expression x or y
first evaluates x; if x is true, its value is
returned; otherwise, y is evaluated and the resulting value is returned.
Note that neither and
nor or
restrict the value and type
they return to False
and True
, but rather return the last evaluated
argument. This is sometimes useful, e.g., if s
is a string that should be
replaced by a default value if it is empty, the expression s or 'foo'
yields
the desired value. Because not
has to create a new value, it
returns a boolean value regardless of the type of its argument
(for example, not 'foo'
produces False
rather than ''
.)
6.12. Assignment expressions¶
assignment_expression: [identifier
":="]expression
An assignment expression (sometimes also called a “named expression” or
“walrus”) assigns an expression
to an
identifier
, while also returning the value of the
expression
.
One common use case is when handling matched regular expressions:
if matching := pattern.search(data):
do_something(matching)
Or, when processing a file stream in chunks:
while chunk := file.read(9000):
process(chunk)
Assignment expressions must be surrounded by parentheses when
used as expression statements and when used as sub-expressions in
slicing, conditional, lambda,
keyword-argument, and comprehension-if expressions and
in assert
, with
, and assignment
statements.
In all other places where they can be used, parentheses are not required,
including in if
and while
statements.
Added in version 3.8: See PEP 572 for more details about assignment expressions.
6.13. Conditional expressions¶
conditional_expression:or_test
["if"or_test
"else"expression
] expression:conditional_expression
|lambda_expr
A conditional expression (sometimes called a “ternary operator”) is an alternative to the if-else statement. As it is an expression, it returns a value and can appear as a sub-expression.
The expression x if C else y
first evaluates the condition, C rather than x.
If C is true, x is evaluated and its value is returned; otherwise, y is
evaluated and its value is returned.
See PEP 308 for more details about conditional expressions.
6.14. Lambdas¶
lambda_expr: "lambda" [parameter_list
] ":"expression
Lambda expressions (sometimes called lambda forms) are used to create anonymous
functions. The expression lambda parameters: expression
yields a function
object. The unnamed object behaves like a function object defined with:
def <lambda>(parameters):
return expression
See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.
6.15. Expression lists¶
starred_expression: "*"or_expr
|expression
flexible_expression:assignment_expression
|starred_expression
flexible_expression_list:flexible_expression
(","flexible_expression
)* [","] starred_expression_list:starred_expression
(","starred_expression
)* [","] expression_list:expression
(","expression
)* [","] yield_list:expression_list
|starred_expression
"," [starred_expression_list
]
Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.
An asterisk *
denotes iterable unpacking. Its operand must be
an iterable. The iterable is expanded into a sequence of items,
which are included in the new tuple, list, or set, at the site of
the unpacking.
Added in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448.
Added in version 3.11: Any item in an expression list may be starred. See PEP 646.
A trailing comma is required only to create a one-item tuple,
such as 1,
; it is optional in all other cases.
A single expression without a
trailing comma doesn’t create a tuple, but rather yields the value of that
expression. (To create an empty tuple, use an empty pair of parentheses:
()
.)
6.16. Evaluation order¶
Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.
In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:
expr1, expr2, expr3, expr4
(expr1, expr2, expr3, expr4)
{expr1: expr2, expr3: expr4}
expr1 + expr2 * (expr3 - expr4)
expr1(expr2, expr3, *expr4, **expr5)
expr3, expr4 = expr1, expr2
6.17. Operator precedence¶
The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).
Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.
Operator |
Description |
|---|---|
|
Binding or parenthesized expression, list display, dictionary display, set display |
|
Subscription (including slicing), call, attribute reference |
Await expression |
|
|
Exponentiation [5] |
|
Positive, negative, bitwise NOT |
|
Multiplication, matrix multiplication, division, floor division, remainder [6] |
|
Addition and subtraction |
|
Shifts |
|
Bitwise AND |
|
Bitwise XOR |
|
Bitwise OR |
Comparisons, including membership tests and identity tests |
|
Boolean NOT |
|
Boolean AND |
|
Boolean OR |
|
|
Conditional expression |
Lambda expression |
|
|
Assignment expression |
Footnotes


— Windows registry access

winreg
— Windows registry access¶
These functions expose the Windows registry API to Python. Instead of using an integer as the registry handle, a handle object is used to ensure that the handles are closed correctly, even if the programmer neglects to explicitly close them.
Availability: Windows.
Changed in version 3.3: Several functions in this module used to raise a
WindowsError
, which is now an alias of OSError
.
Functions¶
This module offers the following functions:
- winreg.CloseKey(hkey)¶
Closes a previously opened registry key. The hkey argument specifies a previously opened key.
Note
If hkey is not closed using this method (or via
hkey.Close()
), it is closed when the hkey object is destroyed by Python.
- winreg.ConnectRegistry(computer_name, key)¶
Establishes a connection to a predefined registry handle on another computer, and returns a handle object.
computer_name is the name of the remote computer, of the form
r"\\computername"
. IfNone
, the local computer is used.key is the predefined handle to connect to.
The return value is the handle of the opened key. If the function fails, an
OSError
exception is raised.Raises an auditing event
winreg.ConnectRegistry
with argumentscomputer_name
,key
.Changed in version 3.3: See above.
- winreg.CreateKey(key, sub_key)¶
Creates or opens the specified key, returning a handle object.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that names the key this method opens or creates.
If key is one of the predefined keys, sub_key may be
None
. In that case, the handle returned is the same key handle passed in to the function.If the key already exists, this function opens the existing key.
The return value is the handle of the opened key. If the function fails, an
OSError
exception is raised.Raises an auditing event
winreg.CreateKey
with argumentskey
,sub_key
,access
.Raises an auditing event
winreg.OpenKey/result
with argumentkey
.Changed in version 3.3: See above.
- winreg.CreateKeyEx(key, sub_key, reserved=0, access=KEY_WRITE)¶
Creates or opens the specified key, returning a handle object.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that names the key this method opens or creates.
reserved is a reserved integer, and must be zero. The default is zero.
access is an integer that specifies an access mask that describes the desired security access for the key. Default is
KEY_WRITE
. See Access Rights for other allowed values.If key is one of the predefined keys, sub_key may be
None
. In that case, the handle returned is the same key handle passed in to the function.If the key already exists, this function opens the existing key.
The return value is the handle of the opened key. If the function fails, an
OSError
exception is raised.Raises an auditing event
winreg.CreateKey
with argumentskey
,sub_key
,access
.Raises an auditing event
winreg.OpenKey/result
with argumentkey
.Added in version 3.2.
Changed in version 3.3: See above.
- winreg.DeleteKey(key, sub_key)¶
Deletes the specified key.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that must be a subkey of the key identified by the key parameter. This value must not be
None
, and the key may not have subkeys.This method can not delete keys with subkeys.
If the method succeeds, the entire key, including all of its values, is removed. If the method fails, an
OSError
exception is raised.Raises an auditing event
winreg.DeleteKey
with argumentskey
,sub_key
,access
.Changed in version 3.3: See above.
- winreg.DeleteKeyEx(key, sub_key, access=KEY_WOW64_64KEY, reserved=0)¶
Deletes the specified key.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that must be a subkey of the key identified by the key parameter. This value must not be
None
, and the key may not have subkeys.reserved is a reserved integer, and must be zero. The default is zero.
access is an integer that specifies an access mask that describes the desired security access for the key. Default is
KEY_WOW64_64KEY
. On 32-bit Windows, the WOW64 constants are ignored. See Access Rights for other allowed values.This method can not delete keys with subkeys.
If the method succeeds, the entire key, including all of its values, is removed. If the method fails, an
OSError
exception is raised.On unsupported Windows versions,
NotImplementedError
is raised.Raises an auditing event
winreg.DeleteKey
with argumentskey
,sub_key
,access
.Added in version 3.2.
Changed in version 3.3: See above.
- winreg.DeleteValue(key, value)¶
Removes a named value from a registry key.
key is an already open key, or one of the predefined HKEY_* constants.
value is a string that identifies the value to remove.
Raises an auditing event
winreg.DeleteValue
with argumentskey
,value
.
- winreg.EnumKey(key, index)¶
Enumerates subkeys of an open registry key, returning a string.
key is an already open key, or one of the predefined HKEY_* constants.
index is an integer that identifies the index of the key to retrieve.
The function retrieves the name of one subkey each time it is called. It is typically called repeatedly until an
OSError
exception is raised, indicating, no more values are available.Raises an auditing event
winreg.EnumKey
with argumentskey
,index
.Changed in version 3.3: See above.
- winreg.EnumValue(key, index)¶
Enumerates values of an open registry key, returning a tuple.
key is an already open key, or one of the predefined HKEY_* constants.
index is an integer that identifies the index of the value to retrieve.
The function retrieves the name of one subkey each time it is called. It is typically called repeatedly, until an
OSError
exception is raised, indicating no more values.The result is a tuple of 3 items:
Index
Meaning
0
A string that identifies the value name
1
An object that holds the value data, and whose type depends on the underlying registry type
2
An integer that identifies the type of the value data (see table in docs for
SetValueEx()
)Raises an auditing event
winreg.EnumValue
with argumentskey
,index
.Changed in version 3.3: See above.
- winreg.ExpandEnvironmentStrings(str)¶
Expands environment variable placeholders
%NAME%
in strings likeREG_EXPAND_SZ
:>>> ExpandEnvironmentStrings('%windir%') 'C:\\Windows'
Raises an auditing event
winreg.ExpandEnvironmentStrings
with argumentstr
.
- winreg.FlushKey(key)¶
Writes all the attributes of a key to the registry.
key is an already open key, or one of the predefined HKEY_* constants.
It is not necessary to call
FlushKey()
to change a key. Registry changes are flushed to disk by the registry using its lazy flusher. Registry changes are also flushed to disk at system shutdown. UnlikeCloseKey()
, theFlushKey()
method returns only when all the data has been written to the registry. An application should only callFlushKey()
if it requires absolute certainty that registry changes are on disk.Note
If you don’t know whether a
FlushKey()
call is required, it probably isn’t.
- winreg.LoadKey(key, sub_key, file_name)¶
Creates a subkey under the specified key and stores registration information from a specified file into that subkey.
key is a handle returned by
ConnectRegistry()
or one of the constantsHKEY_USERS
orHKEY_LOCAL_MACHINE
.sub_key is a string that identifies the subkey to load.
file_name is the name of the file to load registry data from. This file must have been created with the
SaveKey()
function. Under the file allocation table (FAT) file system, the filename may not have an extension.A call to
LoadKey()
fails if the calling process does not have theSE_RESTORE_PRIVILEGE
privilege. Note that privileges are different from permissions – see the RegLoadKey documentation for more details.If key is a handle returned by
ConnectRegistry()
, then the path specified in file_name is relative to the remote computer.Raises an auditing event
winreg.LoadKey
with argumentskey
,sub_key
,file_name
.
- winreg.OpenKey(key, sub_key, reserved=0, access=KEY_READ)¶
- winreg.OpenKeyEx(key, sub_key, reserved=0, access=KEY_READ)¶
Opens the specified key, returning a handle object.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that identifies the sub_key to open.
reserved is a reserved integer, and must be zero. The default is zero.
access is an integer that specifies an access mask that describes the desired security access for the key. Default is
KEY_READ
. See Access Rights for other allowed values.The result is a new handle to the specified key.
If the function fails,
OSError
is raised.Raises an auditing event
winreg.OpenKey
with argumentskey
,sub_key
,access
.Raises an auditing event
winreg.OpenKey/result
with argumentkey
.Changed in version 3.2: Allow the use of named arguments.
Changed in version 3.3: See above.
- winreg.QueryInfoKey(key)¶
Returns information about a key, as a tuple.
key is an already open key, or one of the predefined HKEY_* constants.
The result is a tuple of 3 items:
Index
Meaning
0
An integer giving the number of sub keys this key has.
1
An integer giving the number of values this key has.
2
An integer giving when the key was last modified (if available) as 100’s of nanoseconds since Jan 1, 1601.
Raises an auditing event
winreg.QueryInfoKey
with argumentkey
.
- winreg.QueryValue(key, sub_key)¶
Retrieves the unnamed value for a key, as a string.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that holds the name of the subkey with which the value is associated. If this parameter is
None
or empty, the function retrieves the value set by theSetValue()
method for the key identified by key.Values in the registry have name, type, and data components. This method retrieves the data for a key’s first value that has a
NULL
name. But the underlying API call doesn’t return the type, so always useQueryValueEx()
if possible.Raises an auditing event
winreg.QueryValue
with argumentskey
,sub_key
,value_name
.
- winreg.QueryValueEx(key, value_name)¶
Retrieves the type and data for a specified value name associated with an open registry key.
key is an already open key, or one of the predefined HKEY_* constants.
value_name is a string indicating the value to query.
The result is a tuple of 2 items:
Index
Meaning
0
The value of the registry item.
1
An integer giving the registry type for this value (see table in docs for
SetValueEx()
)Raises an auditing event
winreg.QueryValue
with argumentskey
,sub_key
,value_name
.
- winreg.SaveKey(key, file_name)¶
Saves the specified key, and all its subkeys to the specified file.
key is an already open key, or one of the predefined HKEY_* constants.
file_name is the name of the file to save registry data to. This file cannot already exist. If this filename includes an extension, it cannot be used on file allocation table (FAT) file systems by the
LoadKey()
method.If key represents a key on a remote computer, the path described by file_name is relative to the remote computer. The caller of this method must possess the SeBackupPrivilege security privilege. Note that privileges are different than permissions – see the Conflicts Between User Rights and Permissions documentation for more details.
This function passes
NULL
for security_attributes to the API.Raises an auditing event
winreg.SaveKey
with argumentskey
,file_name
.
- winreg.SetValue(key, sub_key, type, value)¶
Associates a value with a specified key.
key is an already open key, or one of the predefined HKEY_* constants.
sub_key is a string that names the subkey with which the value is associated.
type is an integer that specifies the type of the data. Currently this must be
REG_SZ
, meaning only strings are supported. Use theSetValueEx()
function for support for other data types.value is a string that specifies the new value.
If the key specified by the sub_key parameter does not exist, the SetValue function creates it.
Value lengths are limited by available memory. Long values (more than 2048 bytes) should be stored as files with the filenames stored in the configuration registry. This helps the registry perform efficiently.
The key identified by the key parameter must have been opened with
KEY_SET_VALUE
access.Raises an auditing event
winreg.SetValue
with argumentskey
,sub_key
,type
,value
.
- winreg.SetValueEx(key, value_name, reserved, type, value)¶
Stores data in the value field of an open registry key.
key is an already open key, or one of the predefined HKEY_* constants.
value_name is a string that names the subkey with which the value is associated.
reserved can be anything – zero is always passed to the API.
type is an integer that specifies the type of the data. See Value Types for the available types.
value is a string that specifies the new value.
This method can also set additional value and type information for the specified key. The key identified by the key parameter must have been opened with
KEY_SET_VALUE
access.To open the key, use the
CreateKey()
orOpenKey()
methods.Value lengths are limited by available memory. Long values (more than 2048 bytes) should be stored as files with the filenames stored in the configuration registry. This helps the registry perform efficiently.
Raises an auditing event
winreg.SetValue
with argumentskey
,sub_key
,type
,value
.
- winreg.DisableReflectionKey(key)¶
Disables registry reflection for 32-bit processes running on a 64-bit operating system.
key is an already open key, or one of the predefined HKEY_* constants.
Will generally raise
NotImplementedError
if executed on a 32-bit operating system.If the key is not on the reflection list, the function succeeds but has no effect. Disabling reflection for a key does not affect reflection of any subkeys.
Raises an auditing event
winreg.DisableReflectionKey
with argumentkey
.
- winreg.EnableReflectionKey(key)¶
Restores registry reflection for the specified disabled key.
key is an already open key, or one of the predefined HKEY_* constants.
Will generally raise
NotImplementedError
if executed on a 32-bit operating system.Restoring reflection for a key does not affect reflection of any subkeys.
Raises an auditing event
winreg.EnableReflectionKey
with argumentkey
.
- winreg.QueryReflectionKey(key)¶
Determines the reflection state for the specified key.
key is an already open key, or one of the predefined HKEY_* constants.
Returns
True
if reflection is disabled.Will generally raise
NotImplementedError
if executed on a 32-bit operating system.Raises an auditing event
winreg.QueryReflectionKey
with argumentkey
.
Constants¶
The following constants are defined for use in many winreg
functions.
HKEY_* Constants¶
- winreg.HKEY_CLASSES_ROOT¶
Registry entries subordinate to this key define types (or classes) of documents and the properties associated with those types. Shell and COM applications use the information stored under this key.
- winreg.HKEY_CURRENT_USER¶
Registry entries subordinate to this key define the preferences of the current user. These preferences include the settings of environment variables, data about program groups, colors, printers, network connections, and application preferences.
- winreg.HKEY_LOCAL_MACHINE¶
Registry entries subordinate to this key define the physical state of the computer, including data about the bus type, system memory, and installed hardware and software.
- winreg.HKEY_USERS¶
Registry entries subordinate to this key define the default user configuration for new users on the local computer and the user configuration for the current user.
- winreg.HKEY_PERFORMANCE_DATA¶
Registry entries subordinate to this key allow you to access performance data. The data is not actually stored in the registry; the registry functions cause the system to collect the data from its source.
- winreg.HKEY_CURRENT_CONFIG¶
Contains information about the current hardware profile of the local computer system.
- winreg.HKEY_DYN_DATA¶
This key is not used in versions of Windows after 98.
Access Rights¶
For more information, see Registry Key Security and Access.
- winreg.KEY_ALL_ACCESS¶
Combines the STANDARD_RIGHTS_REQUIRED,
KEY_QUERY_VALUE
,KEY_SET_VALUE
,KEY_CREATE_SUB_KEY
,KEY_ENUMERATE_SUB_KEYS
,KEY_NOTIFY
, andKEY_CREATE_LINK
access rights.
- winreg.KEY_WRITE¶
Combines the STANDARD_RIGHTS_WRITE,
KEY_SET_VALUE
, andKEY_CREATE_SUB_KEY
access rights.
- winreg.KEY_READ¶
Combines the STANDARD_RIGHTS_READ,
KEY_QUERY_VALUE
,KEY_ENUMERATE_SUB_KEYS
, andKEY_NOTIFY
values.
- winreg.KEY_QUERY_VALUE¶
Required to query the values of a registry key.
- winreg.KEY_SET_VALUE¶
Required to create, delete, or set a registry value.
- winreg.KEY_CREATE_SUB_KEY¶
Required to create a subkey of a registry key.
- winreg.KEY_ENUMERATE_SUB_KEYS¶
Required to enumerate the subkeys of a registry key.
- winreg.KEY_NOTIFY¶
Required to request change notifications for a registry key or for subkeys of a registry key.
- winreg.KEY_CREATE_LINK¶
Reserved for system use.
64-bit Specific¶
For more information, see Accessing an Alternate Registry View.
- winreg.KEY_WOW64_64KEY¶
Indicates that an application on 64-bit Windows should operate on the 64-bit registry view. On 32-bit Windows, this constant is ignored.
- winreg.KEY_WOW64_32KEY¶
Indicates that an application on 64-bit Windows should operate on the 32-bit registry view. On 32-bit Windows, this constant is ignored.
Value Types¶
For more information, see Registry Value Types.
- winreg.REG_BINARY¶
Binary data in any form.
- winreg.REG_DWORD¶
32-bit number.
- winreg.REG_DWORD_BIG_ENDIAN¶
A 32-bit number in big-endian format.
- winreg.REG_EXPAND_SZ¶
Null-terminated string containing references to environment variables (
%PATH%
).
- winreg.REG_LINK¶
A Unicode symbolic link.
- winreg.REG_MULTI_SZ¶
A sequence of null-terminated strings, terminated by two null characters. (Python handles this termination automatically.)
- winreg.REG_NONE¶
No defined value type.
- winreg.REG_QWORD¶
A 64-bit number.
Added in version 3.6.
- winreg.REG_QWORD_LITTLE_ENDIAN¶
A 64-bit number in little-endian format. Equivalent to
REG_QWORD
.Added in version 3.6.
- winreg.REG_RESOURCE_LIST¶
A device-driver resource list.
- winreg.REG_FULL_RESOURCE_DESCRIPTOR¶
A hardware setting.
- winreg.REG_RESOURCE_REQUIREMENTS_LIST¶
A hardware resource list.
- winreg.REG_SZ¶
A null-terminated string.
Registry Handle Objects¶
This object wraps a Windows HKEY object, automatically closing it when the
object is destroyed. To guarantee cleanup, you can call either the
Close()
method on the object, or the CloseKey()
function.
All registry functions in this module return one of these objects.
All registry functions in this module which accept a handle object also accept an integer, however, use of the handle object is encouraged.
Handle objects provide semantics for __bool__()
– thus
if handle:
print("Yes")
will print Yes
if the handle is currently valid (has not been closed or
detached).
Handle objects can be converted to an integer (e.g., using the built-in
int()
function), in which case the underlying Windows handle value is
returned. You can also use the Detach()
method to return the
integer handle, and also disconnect the Windows handle from the handle object.
- PyHKEY.Close()¶
Closes the underlying Windows handle.
If the handle is already closed, no error is raised.
- PyHKEY.Detach()¶
Detaches the Windows handle from the handle object.
The result is an integer that holds the value of the handle before it is detached. If the handle is already detached or closed, this will return zero.
After calling this function, the handle is effectively invalidated, but the handle is not closed. You would call this function when you need the underlying Win32 handle to exist beyond the lifetime of the handle object.
Raises an auditing event
winreg.PyHKEY.Detach
with argumentkey
.
- PyHKEY.__enter__()¶
- PyHKEY.__exit__(*exc_info)¶
The HKEY object implements
__enter__()
and__exit__()
and thus supports the context protocol for thewith
statement:with OpenKey(HKEY_LOCAL_MACHINE, "foo") as key: ... # work with key
will automatically close key when control leaves the
with
block.


— Telnet client

telnetlib
— Telnet client¶
Deprecated since version 3.11, removed in version 3.13.
This module is no longer part of the Python standard library. It was removed in Python 3.13 after being deprecated in Python 3.11. The removal was decided in PEP 594.
Possible replacements are third-party libraries from PyPI: telnetlib3 or Exscript. These are not supported or maintained by the Python core team.
The last version of Python that provided the telnetlib
module was
Python 3.12.


Bytes Objects

Bytes Objects¶
These functions raise TypeError
when expecting a bytes parameter and
called with a non-bytes parameter.
-
PyTypeObject PyBytes_Type¶
- Part of the Stable ABI.
This instance of
PyTypeObject
represents the Python bytes type; it is the same object asbytes
in the Python layer.
-
int PyBytes_Check(PyObject *o)¶
Return true if the object o is a bytes object or an instance of a subtype of the bytes type. This function always succeeds.
-
int PyBytes_CheckExact(PyObject *o)¶
Return true if the object o is a bytes object, but not an instance of a subtype of the bytes type. This function always succeeds.
-
PyObject *PyBytes_FromString(const char *v)¶
- Return value: New reference. Part of the Stable ABI.
Return a new bytes object with a copy of the string v as value on success, and
NULL
on failure. The parameter v must not beNULL
; it will not be checked.
-
PyObject *PyBytes_FromStringAndSize(const char *v, Py_ssize_t len)¶
- Return value: New reference. Part of the Stable ABI.
Return a new bytes object with a copy of the string v as value and length len on success, and
NULL
on failure. If v isNULL
, the contents of the bytes object are uninitialized.
-
PyObject *PyBytes_FromFormat(const char *format, ...)¶
- Return value: New reference. Part of the Stable ABI.
Take a C
printf()
-style format string and a variable number of arguments, calculate the size of the resulting Python bytes object and return a bytes object with the values formatted into it. The variable arguments must be C types and must correspond exactly to the format characters in the format string. The following format characters are allowed:Format Characters
Type
Comment
%%
n/a
The literal % character.
%c
int
A single byte, represented as a C int.
%d
int
Equivalent to
printf("%d")
. [1]%u
unsigned int
Equivalent to
printf("%u")
. [1]%ld
long
Equivalent to
printf("%ld")
. [1]%lu
unsigned long
Equivalent to
printf("%lu")
. [1]%zd
Equivalent to
printf("%zd")
. [1]%zu
size_t
Equivalent to
printf("%zu")
. [1]%i
int
Equivalent to
printf("%i")
. [1]%x
int
Equivalent to
printf("%x")
. [1]%s
const char*
A null-terminated C character array.
%p
const void*
The hex representation of a C pointer. Mostly equivalent to
printf("%p")
except that it is guaranteed to start with the literal0x
regardless of what the platform’sprintf
yields.An unrecognized format character causes all the rest of the format string to be copied as-is to the result object, and any extra arguments discarded.
-
PyObject *PyBytes_FromFormatV(const char *format, va_list vargs)¶
- Return value: New reference. Part of the Stable ABI.
Identical to
PyBytes_FromFormat()
except that it takes exactly two arguments.
-
PyObject *PyBytes_FromObject(PyObject *o)¶
- Return value: New reference. Part of the Stable ABI.
Return the bytes representation of object o that implements the buffer protocol.
-
Py_ssize_t PyBytes_Size(PyObject *o)¶
- Part of the Stable ABI.
Return the length of the bytes in bytes object o.
-
Py_ssize_t PyBytes_GET_SIZE(PyObject *o)¶
Similar to
PyBytes_Size()
, but without error checking.
-
char *PyBytes_AsString(PyObject *o)¶
- Part of the Stable ABI.
Return a pointer to the contents of o. The pointer refers to the internal buffer of o, which consists of
len(o) + 1
bytes. The last byte in the buffer is always null, regardless of whether there are any other null bytes. The data must not be modified in any way, unless the object was just created usingPyBytes_FromStringAndSize(NULL, size)
. It must not be deallocated. If o is not a bytes object at all,PyBytes_AsString()
returnsNULL
and raisesTypeError
.
-
char *PyBytes_AS_STRING(PyObject *string)¶
Similar to
PyBytes_AsString()
, but without error checking.
-
int PyBytes_AsStringAndSize(PyObject *obj, char **buffer, Py_ssize_t *length)¶
- Part of the Stable ABI.
Return the null-terminated contents of the object obj through the output variables buffer and length. Returns
0
on success.If length is
NULL
, the bytes object may not contain embedded null bytes; if it does, the function returns-1
and aValueError
is raised.The buffer refers to an internal buffer of obj, which includes an additional null byte at the end (not counted in length). The data must not be modified in any way, unless the object was just created using
PyBytes_FromStringAndSize(NULL, size)
. It must not be deallocated. If obj is not a bytes object at all,PyBytes_AsStringAndSize()
returns-1
and raisesTypeError
.Changed in version 3.5: Previously,
TypeError
was raised when embedded null bytes were encountered in the bytes object.
-
void PyBytes_Concat(PyObject **bytes, PyObject *newpart)¶
- Part of the Stable ABI.
Create a new bytes object in *bytes containing the contents of newpart appended to bytes; the caller will own the new reference. The reference to the old value of bytes will be stolen. If the new object cannot be created, the old reference to bytes will still be discarded and the value of *bytes will be set to
NULL
; the appropriate exception will be set.
-
void PyBytes_ConcatAndDel(PyObject **bytes, PyObject *newpart)¶
- Part of the Stable ABI.
Create a new bytes object in *bytes containing the contents of newpart appended to bytes. This version releases the strong reference to newpart (i.e. decrements its reference count).
-
PyObject *PyBytes_Join(PyObject *sep, PyObject *iterable)¶
Similar to
sep.join(iterable)
in Python.sep must be Python
bytes
object. (Note thatPyUnicode_Join()
acceptsNULL
separator and treats it as a space, whereasPyBytes_Join()
doesn’t acceptNULL
separator.)iterable must be an iterable object yielding objects that implement the buffer protocol.
On success, return a new
bytes
object. On error, set an exception and returnNULL
.Added in version 3.14.
-
int _PyBytes_Resize(PyObject **bytes, Py_ssize_t newsize)¶
Resize a bytes object. newsize will be the new length of the bytes object. You can think of it as creating a new bytes object and destroying the old one, only more efficiently. Pass the address of an existing bytes object as an lvalue (it may be written into), and the new size desired. On success, *bytes holds the resized bytes object and
0
is returned; the address in *bytes may differ from its input value. If the reallocation fails, the original bytes object at *bytes is deallocated, *bytes is set toNULL
,MemoryError
is set, and-1
is returned.
-
PyObject *PyBytes_Repr(PyObject *bytes, int smartquotes)¶
- Part of the Stable ABI.
Get the string representation of bytes. This function is currently used to implement
bytes.__repr__()
in Python.This function does not do type checking; it is undefined behavior to pass bytes as a non-bytes object or
NULL
.If smartquotes is true, the representation will use a double-quoted string instead of single-quoted string when single-quotes are present in bytes. For example, the byte string
'Python'
would be represented asb"'Python'"
when smartquotes is true, orb'\'Python\''
when it is false.On success, this function returns a strong reference to a
str
object containing the representation. On failure, this returnsNULL
with an exception set.
-
PyObject *PyBytes_DecodeEscape(const char *s, Py_ssize_t len, const char *errors, Py_ssize_t unicode, const char *recode_encoding)¶
- Part of the Stable ABI.
Unescape a backslash-escaped string s. s must not be
NULL
. len must be the size of s.errors must be one of
"strict"
,"replace"
, or"ignore"
. If errors isNULL
, then"strict"
is used by default.On success, this function returns a strong reference to a Python
bytes
object containing the unescaped string. On failure, this function returnsNULL
with an exception set.Changed in version 3.9: unicode and recode_encoding are now unused.