3.1. Objects, values and types
Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. Even code is represented by objects.
Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The
operator compares the identity of two objects; the
function returns an integer representing its identity.
CPython implementation detail: For CPython, id(x) is the memory address where x is stored.
An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The
function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable.
The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.
Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable.
CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the
module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly).
Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a
…
statement may keep objects alive.
Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The
…
statement and the
statement provide convenient ways to do this.
Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.
Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. For example, after a=1;b=1, a and b may or may not refer to the same object with the value one, depending on the implementation. This is because
is an immutable type, so the reference to 1 can be reused. This behaviour depends on the implementation used, so should not be relied upon, but is something to be aware of when making use of object identity tests. However, after c=[];d=[], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that e=f=[] assigns the same object to both e and f.)
3.2. The standard type hierarchy
Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.
Some of the type descriptions below contain a paragraph listing ‘special attributes.’ These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.
3.2.1. None
This type has a single value. There is a single object with this value. This object is accessed through the built-in name None. It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false.
3.2.2. NotImplemented
This type has a single value. There is a single object with this value. This object is accessed through the built-in name
. Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) It should not be evaluated in a boolean context.
See
Implementing the arithmetic operations
for more details.
Changed in version 3.9: Evaluating
in a boolean context was deprecated.
3.2.3. Ellipsis
This type has a single value. There is a single object with this value. This object is accessed through the literal ... or the built-in name Ellipsis. Its truth value is true.
3.2.4.
These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.
The string representations of the numeric classes, computed by
and
, have the following properties:
They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.
The representation is in base 10, when possible.
Leading zeros, possibly excepting a single zero before a decimal point, are not shown.
Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.
A sign is shown only when the number is negative.
Python distinguishes between integers, floating-point numbers, and complex numbers:
3.2.4.1.
These represent elements from the mathematical set of integers (positive and negative).
Note
The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers.
There are two types of integers:
Integers (
)These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2’s complement which gives the illusion of an infinite string of sign bits extending to the left.
Booleans (
)These represent the truth values False and True. The two objects representing the values False and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.
3.2.4.2.
(
)
These represent machine-level double precision floating-point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating-point numbers; the savings in processor and memory usage that are usually the reason for using these are dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating-point numbers.
3.2.4.3.
(
)
These represent complex numbers as a pair of machine-level double precision floating-point numbers. The same caveats apply as for floating-point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag.
3.2.5. Sequences
These represent finite ordered sets indexed by non-negative numbers. The built-in function
returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, …, n-1. Item i of sequence a is selected by a[i]. Some sequences, including built-in sequences, interpret negative subscripts by adding the sequence length. For example, a[-2] equals a[n-2], the second to last item of sequence a with length n.
The resulting value must be a nonnegative integer less than the number of items in the sequence. If it is not, an
is raised.
Sequences also support slicing: a[start:stop] selects all items with index k such that start<=k<stop. When used as an expression, a slice is a sequence of the same type. The comment above about negative subscripts also applies to negative slice positions. Note that no error is raised if a slice position is less than zero or larger than the length of the sequence.
If start is missing or
, slicing behaves as if start was zero. If stop is missing or None, slicing behaves as if stop was equal to the length of the sequence.
Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k] selects all items of a with index x where x=i+n*k, n>=0 and i<=x<j.
Sequences are distinguished according to their mutability:
3.2.5.1. Immutable sequences
An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)
The following types are immutable sequences:
StringsA string (
) is a sequence of values that represent characters, or more formally, Unicode code points. All the code points in the range 0 to 0x10FFFF can be represented in a string.
Python doesn’t have a dedicated character type. Instead, every code point in the string is represented as a string object with length 1.
The built-in function
converts a code point from its string form to an integer in the range 0 to 0x10FFFF;
converts an integer in the range 0 to 0x10FFFF to the corresponding length 1 string object.
can be used to convert a
to
using the given text encoding, and
can be used to achieve the opposite.
TuplesThe items of a
are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a ‘singleton’) can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.
BytesA
object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like b'abc') and the built-in
constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the
method.
3.2.5.2. Mutable sequences
Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and
(delete) statements.
Note
The
and
module provide additional examples of mutable sequence types.
There are currently two intrinsic mutable sequence types:
ListsThe items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)
Byte ArraysA bytearray object is a mutable array. They are created by the built-in
constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable
objects.
3.2.6. Set types
These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, and the built-in function
returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.
For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0), only one of them can be contained in a set.
There are currently two intrinsic set types:
SetsThese represent a mutable set. They are created by the built-in
constructor and can be modified afterwards by several methods, such as
.
Frozen setsThese represent an immutable set. They are created by the built-in
constructor. As a frozenset is immutable and
, it can be used again as an element of another set, or as a dictionary key.
3.2.7. Mappings
These represent finite sets of objects indexed by arbitrary index sets. The subscript notation a[k] selects the item indexed by k from the mapping a; this can be used in expressions and as the target of assignments or
statements. The built-in function
returns the number of items in a mapping.
There is currently a single intrinsic mapping type:
3.2.7.1. Dictionaries
These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key’s hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0) then they can be used interchangeably to index the same dictionary entry.
Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.
Dictionaries are mutable; they can be created by the {} notation (see section
).
The extension modules
and
provide additional examples of mapping types, as does the
module.
Changed in version 3.7: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.
3.2.8. Callable types
These are the types to which the function call operation (see section
) can be applied:
3.2.8.1. User-defined functions
A user-defined function object is created by a function definition (see section
). It should be called with an argument list containing the same number of items as the function’s formal parameter list.
3.2.8.1.1. Special read-only attributes
Attribute
Meaning
function.__builtins__
A reference to the
that holds the function’s builtins namespace.
Added in version 3.10.
function.__globals__
A reference to the
that holds the function’s
– the global namespace of the module in which the function was defined.
function.__closure__
None or a
of cells that contain bindings for the names specified in the
attribute of the function’s
.
A cell object has the attribute cell_contents. This can be used to get the value of the cell, as well as set the value.
3.2.8.1.2. Special writable attributes
Most of these attributes check the type of the assigned value:
Attribute
Meaning
function.__doc__
The function’s documentation string, or None if unavailable.
function.__name__
The function’s name. See also:
.
function.__qualname__
The function’s
. See also:
.
Added in version 3.3.
function.__module__
The name of the module the function was defined in, or None if unavailable.
function.__defaults__
A
containing default
values for those parameters that have defaults, or None if no parameters have a default value.
function.__code__
The
representing the compiled function body.
function.__dict__
The namespace supporting arbitrary function attributes. See also:
.
function.__annotations__
A
containing annotations of
. The keys of the dictionary are the parameter names, and 'return' for the return annotation, if provided. See also:
.
Changed in version 3.14: Annotations are now
. See
.
function.__annotate__
The
for this function, or None if the function has no annotations. See
.
Added in version 3.14.
function.__kwdefaults__
A
containing defaults for keyword-only
.
function.__type_params__
A
containing the
of a
.
Added in version 3.12.
Function objects also support getting and setting arbitrary attributes, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes.
CPython implementation detail: CPython’s current implementation only supports function attributes on user-defined functions. Function attributes on
may be supported in the future.
Additional information about a function’s definition can be retrieved from its
(accessible via the
attribute).
3.2.8.2. Instance methods
An instance method object combines a class, a class instance and any callable object (normally a user-defined function).
Special read-only attributes:
method.__self__
Refers to the class instance object to which the method is
method.__func__
Refers to the original
method.__doc__
The method’s documentation (same as
). A
if the original function had a docstring, else None.
method.__name__
The name of the method (same as
)
method.__module__
The name of the module the method was defined in, or None if unavailable.
Methods also support accessing (but not setting) the arbitrary function attributes on the underlying
.
User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined
or a
object.
When an instance method object is created by retrieving a user-defined
from a class via one of its instances, its
attribute is the instance, and the method object is said to be bound. The new method’s
attribute is the original function object.
When an instance method object is created by retrieving a
object from a class or instance, its
attribute is the class itself, and its
attribute is the function object underlying the class method.
When an instance method object is called, the underlying function (
) is called, inserting the class instance (
) in front of the argument list. For instance, when C is a class which contains a definition for a function f(), and x is an instance of C, calling x.f(1) is equivalent to calling C.f(x,1).
When an instance method object is derived from a
object, the “class instance” stored in
will actually be the class itself, so that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1) where f is the underlying function.
It is important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.
3.2.8.3. Generator functions
A function or method which uses the
statement (see section
) is called a generator function. Such a function, when called, always returns an
object which can be used to execute the body of the function: calling the iterator’s
method will cause the function to execute until it provides a value using the yield statement. When the function executes a
statement or falls off the end, a
exception is raised and the iterator will have reached the end of the set of values to be returned.
3.2.8.4. Coroutine functions
A function or method which is defined using
is called a coroutine function. Such a function, when called, returns a
object. It may contain
expressions, as well as
and
statements. See also the
section.
3.2.8.5. Asynchronous generator functions
A function or method which is defined using
and which uses the
statement is called a asynchronous generator function. Such a function, when called, returns an
object which can be used in an
statement to execute the body of the function.
Calling the asynchronous iterator’s
method will return an
which when awaited will execute until it provides a value using the
expression. When the function executes an empty
statement or falls off the end, a
exception is raised and the asynchronous iterator will have reached the end of the set of values to be yielded.
3.2.8.6. Built-in functions
A built-in function object is a wrapper around a C function. Examples of built-in functions are
and
(
is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes:
__doc__ is the function’s documentation string, or None if unavailable. See
.
__name__ is the function’s name. See
.
__self__ is set to None (but see the next item).
__module__ is the name of the module the function was defined in or None if unavailable. See
.
3.2.8.7. Built-in methods
This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is alist.append(), assuming alist is a list object. In this case, the special read-only attribute __self__ is set to the object denoted by alist. (The attribute has the same semantics as it does with
.)
3.2.8.8. Classes
Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override
. The arguments of the call are passed to __new__() and, in the typical case, to
to initialize the new instance.
3.2.8.9. Class Instances
Instances of arbitrary classes can be made callable by defining a
method in their class.
3.2.9. Modules
Modules are a basic organizational unit of Python code, and are created by the
as invoked either by the
statement, or by calling functions such as
and built-in
. A module object has a namespace implemented by a
object (this is the dictionary referenced by the
attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"]. A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).
Attribute assignment updates the module’s namespace dictionary, e.g., m.x=1 is equivalent to m.__dict__["x"]=1.
3.2.9.2. Other writable attributes on module objects
As well as the import-related attributes listed above, module objects also have the following writable attributes:
module.__doc__
The module’s documentation string, or None if unavailable. See also:
.
module.__annotations__
A dictionary containing
collected during module body execution. For best practices on working with __annotations__, see
.
module.__annotate__
The
for this module, or None if the module has no annotations. See also:
attributes.
Added in version 3.14.
3.2.9.3. Module dictionaries
Module objects also have the following special read-only attribute:
module.__dict__
The module’s namespace as a dictionary object. Uniquely among the attributes listed here, __dict__ cannot be accessed as a global variable from within a module; it can only be accessed as an attribute on module objects.
CPython implementation detail: Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly.
3.2.10. Custom classes
Custom class types are typically created by class definitions (see section
). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found at
The Python 2.3 Method Resolution Order
.
When a class attribute reference (for class C, say) would yield a class method object, it is transformed into an instance method object whose
attribute is C. When it would yield a
object, it is transformed into the object wrapped by the static method object. See section
for another way in which attributes retrieved from a class may differ from those actually contained in its
.
Class attribute assignments update the class’s dictionary, never the dictionary of a base class.
A class object can be called (see above) to yield a class instance (see below).
3.2.10.1. Special attributes
Attribute
Meaning
type.__name__
The class’s name. See also:
.
type.__qualname__
The class’s
. See also:
.
type.__module__
The name of the module in which the class was defined.
type.__dict__
A
providing a read-only view of the class’s namespace. See also:
.
type.__bases__
A
containing the class’s bases. In most cases, for a class defined as classX(A,B,C), X.__bases__ will be exactly equal to (A,B,C).
type.__base__
CPython implementation detail: The single base class in the inheritance chain that is responsible for the memory layout of instances. This attribute corresponds to
at the C level.
type.__doc__
The class’s documentation string, or None if undefined. Not inherited by subclasses.
type.__annotations__
A dictionary containing
collected during class body execution. See also:
.
For best practices on working with
, please see
. Use
annotationlib.get_annotations()
instead of accessing this attribute directly.
Warning
Accessing the __annotations__ attribute directly on a class object may return annotations for the wrong class, specifically in certain cases where the class, its base class, or a metaclass is defined under from__future__importannotations. See
for details.
This attribute does not exist on certain builtin classes. On user-defined classes without __annotations__, it is an empty dictionary.
Changed in version 3.14: Annotations are now
. See
.
type.__annotate__()
The
for this class, or None if the class has no annotations. See also:
.
Added in version 3.14.
type.__type_params__
A
containing the
of a
.
Added in version 3.12.
type.__static_attributes__
A
containing names of attributes of this class which are assigned through self.X from any function in its body.
Added in version 3.13.
type.__firstlineno__
The line number of the first line of the class definition, including decorators. Setting the
attribute removes the __firstlineno__ item from the type’s dictionary.
Added in version 3.13.
type.__mro__
The
of classes that are considered when looking for base classes during method resolution.
3.2.10.2. Special methods
In addition to the special attributes described above, all Python classes also have the following two methods available:
type.mro()
This method can be overridden by a metaclass to customize the method resolution order for its instances. It is called at class instantiation, and its result is stored in
.
type.__subclasses__()
Each class keeps a list of weak references to its immediate subclasses. This method returns a list of all those references still alive. The list is in definition order. Example:
>>> classA:pass>>> classB(A):pass>>> A.__subclasses__()[<class 'B'>]3.2.11. Class instances
A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance’s class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose
attribute is the instance. Static method and class method objects are also transformed; see above under “Classes”. See section
for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class’s
. If no class attribute is found, and the object’s class has a
method, that is called to satisfy the lookup.
Attribute assignments and deletions update the instance’s dictionary, never a class’s dictionary. If the class has a
or
method, this is called instead of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section
.
3.2.11.1. Special attributes
object.__class__
The class to which a class instance belongs.
object.__dict__
A dictionary or other mapping object used to store an object’s (writable) attributes. Not all instances have a __dict__ attribute; see the section on
for more details.
3.2.12. I/O objects (also known as file objects)
A
represents an open file. Various shortcuts are available to create file objects: the
built-in function, and also
,
, and the
method of socket objects (and perhaps by other functions or methods provided by extension modules).
File objects implement common methods, listed below, to simplify usage in generic code. They are expected to be
With Statement Context Managers
.
The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter’s standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the
abstract class.
file.read(size=-1, /)
Retrieve up to size data from the file. As a convenience if size is unspecified or -1 retrieve all data available.
file.write(data, /)
Store data to the file.
file.close()
Flush any buffers and close the underlying file.
3.2.13. Internal types
A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.
3.2.13.1. Code objects
Code objects represent byte-compiled executable Python code, or
. The difference between a code object and a function object is that the function object contains an explicit reference to the function’s globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.
3.2.13.1.1. Special read-only attributes
codeobject.co_name
The function name
codeobject.co_qualname
The fully qualified function name
Added in version 3.11.
codeobject.co_argcount
The total number of positional
(including positional-only parameters and parameters with default values) that the function has
codeobject.co_posonlyargcount
The number of positional-only
(including arguments with default values) that the function has
codeobject.co_kwonlyargcount
The number of keyword-only
(including arguments with default values) that the function has
codeobject.co_nlocals
The number of
used by the function (including parameters)
codeobject.co_varnames
A
containing the names of the local variables in the function (starting with the parameter names)
codeobject.co_cellvars
A
containing the names of
that are referenced from at least one
inside the function
codeobject.co_freevars
A
containing the names of
that a
references in an outer scope. See also
.
Note: references to global and builtin names are not included.
codeobject.co_code
A string representing the sequence of
instructions in the function
codeobject.co_consts
A
containing the literals used by the
in the function
codeobject.co_names
A
containing the names used by the
in the function
codeobject.co_filename
The name of the file from which the code was compiled
codeobject.co_firstlineno
The line number of the first line of the function
codeobject.co_lnotab
A string encoding the mapping from
offsets to line numbers. For details, see the source code of the interpreter.
Deprecated since version 3.12: This attribute of code objects is deprecated, and may be removed in Python 3.15.
codeobject.co_stacksize
The required stack size of the code object
codeobject.co_flags
An
encoding a number of flags for the interpreter.
The following flag bits are defined for
: bit 0x04 is set if the function uses the *arguments syntax to accept an arbitrary number of positional arguments; bit 0x08 is set if the function uses the **keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set if the function is a generator. See
for details on the semantics of each flags that might be present.
Future feature declarations (for example, from__future__importdivision) also use bits in
to indicate whether a code object was compiled with a particular feature enabled. See
.
Other bits in
are reserved for internal use.
If a code object represents a function and has a docstring, the
bit is set in
and the first item in
is the docstring of the function.
3.2.13.1.2. Methods on code objects
codeobject.co_positions()
Returns an iterable over the source code positions of each
instruction in the code object.
The iterator returns
s containing the (start_line,end_line,start_column,end_column). The i-th tuple corresponds to the position of the source code that compiled to the i-th code unit. Column information is 0-indexed utf-8 byte offsets on the given source line.
This positional information can be missing. A non-exhaustive lists of cases where this may happen:
Running the interpreter with
no_debug_ranges.
Loading a pyc file compiled while using
no_debug_ranges.
Position tuples corresponding to artificial instructions.
Line and column numbers that can’t be represented due to implementation specific limitations.
When this occurs, some or all of the tuple elements can be
.
Added in version 3.11.
Note
This feature requires storing column positions in code objects which may result in a small increase of disk usage of compiled Python files or interpreter memory usage. To avoid storing the extra information and/or deactivate printing the extra traceback information, the
no_debug_ranges command line flag or the
environment variable can be used.
codeobject.co_lines()
Returns an iterator that yields information about successive ranges of
s. Each item yielded is a (start,end,lineno)
:
start (an
) represents the offset (inclusive) of the start of the
range
end (an
) represents the offset (exclusive) of the end of the
range
lineno is an
representing the line number of the
range, or None if the bytecodes in the given range have no line number
The items yielded will have the following properties:
The first range yielded will have a start of 0.
The (start,end) ranges will be non-decreasing and consecutive. That is, for any pair of
s, the start of the second will be equal to the end of the first.
No range will be backwards: end>=start for all triples.
The last
yielded will have end equal to the size of the
.
Zero-width ranges, where start==end, are allowed. Zero-width ranges are used for lines that are present in the source code, but have been eliminated by the
compiler.
Added in version 3.10.
See also
- Precise line numbers for debugging and other tools.The PEP that introduced the co_lines() method.
codeobject.replace(**kwargs)
Return a copy of the code object with new values for the specified fields.
Code objects are also supported by the generic function
.
Added in version 3.8.
3.2.13.2. Frame objects
Frame objects represent execution frames. They may occur in
, and are also passed to registered trace functions.
3.2.13.2.1. Special read-only attributes
frame.f_back
Points to the previous stack frame (towards the caller), or None if this is the bottom stack frame
frame.f_code
The
being executed in this frame. Accessing this attribute raises an
object.__getattr__ with arguments obj and "f_code".
frame.f_locals
The mapping used by the frame to look up
. If the frame refers to an
, this may return a write-through proxy object.
Changed in version 3.13: Return a proxy for optimized scopes.
frame.f_globals
The dictionary used by the frame to look up
frame.f_builtins
The dictionary used by the frame to look up
frame.f_lasti
The “precise instruction” of the frame object (this is an index into the
string of the
)
frame.f_generator
The
or
object that owns this frame, or None if the frame is a normal function.
Added in version 3.14.
3.2.13.2.2. Special writable attributes
frame.f_trace
If not None, this is a function called for various events during code execution (this is used by debuggers). Normally an event is triggered for each new source line (see
).
frame.f_trace_lines
Set this attribute to
to disable triggering a tracing event for each source line.
frame.f_trace_opcodes
Set this attribute to
to allow per-opcode events to be requested. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.
frame.f_lineno
The current line number of the frame – writing to this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to this attribute.
3.2.13.2.3. Frame object methods
Frame objects support one method:
frame.clear()
This method clears all references to
held by the frame. Also, if the frame belonged to a
, the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an
and storing its
for later use).
is raised if the frame is currently executing or suspended.
Added in version 3.4.
Changed in version 3.13: Attempting to clear a suspended frame raises
(as has always been the case for executing frames).
3.2.13.3. Traceback objects
Traceback objects represent the stack trace of an
. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling
.
Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code.
For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section
.) It is accessible as the third item of the tuple returned by
, and as the
attribute of the caught exception.
When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as
.
For explicitly created tracebacks, it is up to the creator of the traceback to determine how the
attributes should be linked to form a full stack trace.
Special read-only attributes:
traceback.tb_frame
Points to the execution
of the current level.
Accessing this attribute raises an
object.__getattr__ with arguments obj and "tb_frame".
traceback.tb_lineno
Gives the line number where the exception occurred
traceback.tb_lasti
Indicates the “precise instruction”.
The line number and last instruction in the traceback may differ from the line number of its
if the exception occurred in a
statement with no matching except clause or with a
clause.
traceback.tb_next
The special writable attribute tb_next is the next level in the stack trace (towards the frame where the exception occurred), or None if there is no next level.
Changed in version 3.7: This attribute is now writable
3.2.13.4. Slice objects
Slice objects are used to represent slices for
methods. They are also created by the built-in
function.
Special read-only attributes:
is the lower bound;
is the upper bound;
is the step value; each is None if omitted. These attributes can have any type.
Slice objects support one method:
slice.indices(self, length)
This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.
3.2.13.5. Static method objects
Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are also callable. Static method objects are created by the built-in
constructor.
3.2.13.6. Class method objects
A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under
. Class method objects are created by the built-in
constructor.
3.3. Special method names
A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named
, and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x,i). Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically
or
).
Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets
to None, the class is not iterable, so calling
on its instances will raise a
(without falling back to
).
When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the
interface in the W3C’s Document Object Model.)
3.3.1. Basic customization
object.__new__(cls[, ...])
Called to create a new instance of class cls. __new__() is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of __new__() should be the new object instance (usually an instance of cls).
Typical implementations create a new instance of the class by invoking the superclass’s __new__() method using super().__new__(cls[,...]) with appropriate arguments and then modifying the newly created instance as necessary before returning it.
If __new__() is invoked during object construction and it returns an instance of cls, then the new instance’s
method will be invoked like __init__(self[,...]), where self is the new instance and the remaining arguments are the same as were passed to the object constructor.
If __new__() does not return an instance of cls, then the new instance’s
method will not be invoked.
__new__() is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.
object.__init__(self[, ...])
Called after the instance has been created (by
), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an __init__() method, the derived class’s __init__() method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: super().__init__([args...]).
Because
and __init__() work together in constructing objects (__new__() to create it, and __init__() to customize it), no non-None value may be returned by __init__(); doing so will cause a
to be raised at runtime.
object.__del__(self)
Called when the instance is about to be destroyed. This is also called a finalizer or (improperly) a destructor. If a base class has a __del__() method, the derived class’s __del__() method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance.
It is possible (though not recommended!) for the __del__() method to postpone destruction of the instance by creating a new reference to it. This is called object resurrection. It is implementation-dependent whether __del__() is called a second time when a resurrected object is about to be destroyed; the current
implementation only calls it once.
It is not guaranteed that __del__() methods are called for objects that still exist when the interpreter exits.
provides a straightforward way to register a cleanup function to be called when an object is garbage collected.
Note
delx doesn’t directly call x.__del__() — the former decrements the reference count for x by one, and the latter is only called when x’s reference count reaches zero.
CPython implementation detail: It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by the
. A common cause of reference cycles is when an exception has been caught in a local variable. The frame’s locals then reference the exception, which references its own traceback, which references the locals of all frames caught in the traceback.
See also
Documentation for the
module.
Warning
Due to the precarious circumstances under which __del__() methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to sys.stderr instead. In particular:
__del__() can be invoked when arbitrary code is being executed, including from any arbitrary thread. If __del__() needs to take a lock or invoke any other blocking resource, it may deadlock as the resource may already be taken by the code that gets interrupted to execute __del__().
__del__() can be executed during interpreter shutdown. As a consequence, the global variables it needs to access (including other modules) may already have been deleted or set to None. Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the __del__() method is called.
object.__repr__(self)
Called by the
built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form <...someusefuldescription...> should be returned. The return value must be a string object. If a class defines __repr__() but not
, then __repr__() is also used when an “informal” string representation of instances of that class is required.
This is typically used for debugging, so it is important that the representation is information-rich and unambiguous. A default implementation is provided by the
class itself.
object.__str__(self)
Called by
, the default
implementation, and the built-in function
, to compute the “informal” or nicely printable string representation of an object. The return value must be a
object.
This method differs from
in that there is no expectation that __str__() return a valid Python expression: a more convenient or concise representation can be used.
The default implementation defined by the built-in type
calls
.
object.__bytes__(self)
Called by
to compute a byte-string representation of an object. This should return a
object. The
class itself does not provide this method.
object.__format__(self, format_spec)
Called by the
built-in function, and by extension, evaluation of
and the
method, to produce a “formatted” string representation of an object. The format_spec argument is a string that contains a description of the formatting options desired. The interpretation of the format_spec argument is up to the type implementing __format__(), however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.
See
Format specification mini-language
for a description of the standard formatting syntax.
The return value must be a string object.
The default implementation by the
class should be given an empty format_spec string. It delegates to
.
Changed in version 3.4: The __format__ method of object itself raises a
if passed any non-empty string.
Changed in version 3.7: object.__format__(x,'') is now equivalent to str(x) rather than format(str(x),'').
object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)
These are the so-called “rich comparison” methods. The correspondence between operator symbols and method names is as follows: x<y calls x.__lt__(y), x<=y calls x.__le__(y), x==y calls x.__eq__(y), x!=y calls x.__ne__(y), x>y calls x.__gt__(y), and x>=y calls x.__ge__(y).
A rich comparison method may return the singleton
if it does not implement the operation for a given pair of arguments. By convention, False and True are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an if statement), Python will call
on the value to determine if the result is true or false.
By default, object implements __eq__() by using is, returning
in the case of a false comparison: TrueifxisyelseNotImplemented. For __ne__(), by default it delegates to __eq__() and inverts the result unless it is NotImplemented. There are no other implied relationships among the comparison operators or default implementations; for example, the truth of (x<yorx==y) does not imply x<=y. To automatically generate ordering operations from a single root operation, see
.
By default, the
class provides implementations consistent with
: equality compares according to object identity, and order comparisons raise
. Each default method may generate these results directly, but may also return
.
See the paragraph on
for some important notes on creating
objects which support custom comparison operations and are usable as dictionary keys.
There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, __lt__() and __gt__() are each other’s reflection, __le__() and __ge__() are each other’s reflection, and __eq__() and __ne__() are their own reflection. If the operands are of different types, and the right operand’s type is a direct or indirect subclass of the left operand’s type, the reflected method of the right operand has priority, otherwise the left operand’s method has priority. Virtual subclassing is not considered.
When no appropriate method returns any value other than
, the == and != operators will fall back to is and isnot, respectively.
object.__hash__(self)
Called by built-in function
and for operations on members of hashed collections including
,
, and
. The __hash__() method should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:
def__hash__(self):returnhash((self.name,self.nick,self.color))Note
truncates the value returned from an object’s custom __hash__() method to the size of a
. This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s __hash__() must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with python-c"importsys;print(sys.hash_info.width)".
If a class does not define an
method it should not define a __hash__() operation either; if it defines __eq__() but not __hash__(), its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an __eq__() method, it should not implement __hash__(), since the implementation of
collections requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).
User-defined classes have
and __hash__() methods by default (inherited from the
class); with them, all objects compare unequal (except with themselves) and x.__hash__() returns an appropriate value such that x==y implies both that xisy and hash(x)==hash(y).
A class that overrides
and does not define __hash__() will have its __hash__() implicitly set to None. When the __hash__() method of a class is None, instances of the class will raise an appropriate
when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking isinstance(obj,collections.abc.Hashable).
If a class that overrides
needs to retain the implementation of __hash__() from a parent class, the interpreter must be told this explicitly by setting __hash__=<ParentClass>.__hash__.
If a class that does not override
wishes to suppress hash support, it should include __hash__=None in the class definition. A class which defines its own __hash__() that explicitly raises a
would be incorrectly identified as hashable by an isinstance(obj,collections.abc.Hashable) call.
Note
By default, the __hash__() values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.
This is intended to provide protection against a denial-of-service caused by carefully chosen inputs that exploit the worst case performance of a dict insertion, O(n2) complexity. See
https://ocert.org/advisories/ocert-2011-003.html
for details.
Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).
See also
.
Changed in version 3.3: Hash randomization is enabled by default.
object.__bool__(self)
Called to implement truth value testing and the built-in operation bool(); should return False or True. When this method is not defined,
is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither __len__() nor __bool__() (which is true of the
class itself), all its instances are considered true.
3.3.2. Customizing attribute access
The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of x.name) for class instances.
object.__getattr__(self, name)
Called when the default attribute access fails with an
(either
raises an AttributeError because name is not an instance attribute or an attribute in the class tree for self; or
of a name property raises AttributeError). This method should either return the (computed) attribute value or raise an AttributeError exception. The
class itself does not provide this method.
Note that if the attribute is found through the normal mechanism, __getattr__() is not called. (This is an intentional asymmetry between __getattr__() and
.) This is done both for efficiency reasons and because otherwise __getattr__() would have no way to access other attributes of the instance. Note that at least for instance variables, you can take total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the
method below for a way to actually get total control over attribute access.
object.__getattribute__(self, name)
Called unconditionally to implement attribute accesses for instances of the class. If the class also defines
, the latter will not be called unless __getattribute__() either calls it explicitly or raises an
. This method should return the (computed) attribute value or raise an AttributeError exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, object.__getattribute__(self,name).
Note
This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or
. See
.
For certain sensitive attribute accesses, raises an
object.__getattr__ with arguments obj and name.
object.__setattr__(self, name, value)
Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it.
If __setattr__() wants to assign to an instance attribute, it should call the base class method with the same name, for example, object.__setattr__(self,name,value).
For certain sensitive attribute assignments, raises an
object.__setattr__ with arguments obj, name, value.
object.__delattr__(self, name)
Like
but for attribute deletion instead of assignment. This should only be implemented if delobj.name is meaningful for the object.
For certain sensitive attribute deletions, raises an
object.__delattr__ with arguments obj and name.
object.__dir__(self)
Called when
is called on the object. An iterable must be returned. dir() converts the returned iterable to a list and sorts it.
3.3.2.1. Customizing module attribute access
module.__getattr__()
module.__dir__()
Special names __getattr__ and __dir__ can be also used to customize access to module attributes. The __getattr__ function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an
. If an attribute is not found on a module object through the normal lookup, i.e.
, then __getattr__ is searched in the module __dict__ before raising an AttributeError. If found, it is called with the attribute name and the result is returned.
The __dir__ function should accept no arguments, and return an iterable of strings that represents the names accessible on module. If present, this function overrides the standard
search on a module.
module.__class__
For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the __class__ attribute of a module object to a subclass of
. For example:
importsysfromtypesimportModuleTypeclassVerboseModule(ModuleType):def__repr__(self):returnf'Verbose {self.__name__}'def__setattr__(self,attr,value):print(f'Setting {attr}...')super().__setattr__(attr,value)sys.modules[__name__].__class__=VerboseModuleNote
Defining module __getattr__ and setting module __class__ only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: __class__ module attribute is now writable.
Added in version 3.7: __getattr__ and __dir__ module attributes.
See also
- Module __getattr__ and __dir__Describes the __getattr__ and __dir__ functions on modules.
3.3.2.2. Implementing Descriptors
The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’
. The
class itself does not implement any of these protocols.
object.__get__(self, instance, owner=None)
Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional owner argument is the owner class, while instance is the instance that the attribute was accessed through, or None when the attribute is accessed through the owner.
This method should return the computed attribute value or raise an
exception.
specifies that __get__() is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own
implementation always passes in both arguments whether they are required or not.
object.__set__(self, instance, value)
Called to set the attribute on an instance instance of the owner class to a new value, value.
Note, adding __set__() or
changes the kind of descriptor to a “data descriptor”. See
for more details.
object.__delete__(self, instance)
Called to delete the attribute on an instance instance of the owner class.
Instances of descriptors may also have the __objclass__ attribute present:
object.__objclass__
The attribute __objclass__ is interpreted by the
module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C).
3.3.2.3. Invoking Descriptors
In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol:
,
, and
. If any of those methods are defined for an object, it is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, a.x has a lookup chain starting with a.__dict__['x'], then type(a).__dict__['x'], and continuing through the base classes of type(a) excluding metaclasses.
However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called.
The starting point for descriptor invocation is a binding, a.x. How the arguments are assembled depends on a:
Direct CallThe simplest and least common call is when user code directly invokes a descriptor method: x.__get__(a).
Instance BindingIf binding to an object instance, a.x is transformed into the call: type(a).__dict__['x'].__get__(a,type(a)).
Class BindingIf binding to a class, A.x is transformed into the call: A.__dict__['x'].__get__(None,A).
Super BindingA dotted lookup such as super(A,a).x searches a.__class__.__mro__ for a base class B following A and then returns B.__dict__['x'].__get__(a,A). If not a descriptor, x is returned unchanged.
For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of
,
and
. If it does not define __get__(), then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines __set__() and/or __delete__(), it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both __get__() and __set__(), while non-data descriptors have just the __get__() method. Data descriptors with __get__() and __set__() (and/or __delete__()) defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances.
Python methods (including those decorated with
and
) are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class.
The
decorator is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property.
3.3.2.4. __slots__
__slots__ allow us to explicitly declare data members (like properties) and deny the creation of
and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)
The space saved over using
can be significant. Attribute lookup speed can be significantly improved as well.
object.__slots__
This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. __slots__ reserves space for the declared variables and prevents the automatic creation of
and __weakref__ for each instance.
Notes on using __slots__:
When inheriting from a class without __slots__, the
and __weakref__ attribute of the instances will always be accessible.
Without a
variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises
. If dynamic assignment of new variables is desired, then add '__dict__' to the sequence of strings in the __slots__ declaration.
Without a __weakref__ variable for each instance, classes defining __slots__ do not support
to its instances. If weak reference support is needed, then add '__weakref__' to the sequence of strings in the __slots__ declaration.
__slots__ are implemented at the class level by creating
for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.
The action of a __slots__ declaration is not limited to the class where it is defined. __slots__ declared in parents are available in child classes. However, instances of a child subclass will get a
and __weakref__ unless the subclass also defines __slots__ (which should only contain names of any additional slots).
If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.
will be raised if nonempty __slots__ are defined for a class derived from a
"variable-length" built-in type
such as
,
, and
.
Any non-string
may be assigned to __slots__.
If a
is used to assign __slots__, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by
and displayed in the output of
.
assignment works only if both classes have the same __slots__.
with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise
.
If an
is used for __slots__ then a
is created for each of the iterator’s values. However, the __slots__ attribute will be an empty iterator.
3.3.3. Customizing class creation
Whenever a class inherits from another class,
is called on the parent class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, __init_subclass__ solely applies to future subclasses of the class defining the method.
classmethodobject.__init_subclass__(cls)
This method is called whenever the containing class is subclassed. cls is then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method.
Keyword arguments which are given to a new class are passed to the parent class’s __init_subclass__. For compatibility with other classes using __init_subclass__, one should take out the needed keyword arguments and pass the others over to the base class, as in:
classPhilosopher:def__init_subclass__(cls,/,default_name,**kwargs):super().__init_subclass__(**kwargs)cls.default_name=default_nameclassAustralianPhilosopher(Philosopher,default_name="Bruce"):passThe default implementation object.__init_subclass__ does nothing, but raises an error if it is called with any arguments.
Note
The metaclass hint metaclass is consumed by the rest of the type machinery, and is never passed to __init_subclass__ implementations. The actual metaclass (rather than the explicit hint) can be accessed as type(cls).
Added in version 3.6.
When a class is created, type.__new__() scans the class variables and makes callbacks to those with a
hook.
object.__set_name__(self, owner, name)
Automatically called at the time the owning class owner is created. The object has been assigned to name in that class:
classA:x=C()# Automatically calls: x.__set_name__(A, 'x')If the class variable is assigned after the class is created, __set_name__() will not be called automatically. If needed, __set_name__() can be called directly:
classA:passc=C()A.x=c# The hook is not calledc.__set_name__(A,'x')# Manually invoke the hookSee
for more details.
Added in version 3.6.
3.3.3.1. Metaclasses
By default, classes are constructed using
. The class body is executed in a new namespace and the class name is bound locally to the result of type(name,bases,namespace).
The class creation process can be customized by passing the metaclass keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, both MyClass and MySubclass are instances of Meta:
classMeta(type):passclassMyClass(metaclass=Meta):passclassMySubclass(MyClass):passAny other keyword arguments that are specified in the class definition are passed through to all metaclass operations described below.
When a class definition is executed, the following steps occur:
MRO entries are resolved;
the appropriate metaclass is determined;
the class namespace is prepared;
the class body is executed;
the class object is created.
3.3.3.2. Resolving MRO entries
object.__mro_entries__(self, bases)
If a base that appears in a class definition is not an instance of
, then an __mro_entries__() method is searched on the base. If an __mro_entries__() method is found, the base is substituted with the result of a call to __mro_entries__() when creating the class. The method is called with the original bases tuple passed to the bases parameter, and must return a tuple of classes that will be used instead of the base. The returned tuple may be empty: in these cases, the original base is ignored.
3.3.3.3. Determining the appropriate metaclass
The appropriate metaclass for a class definition is determined as follows:
if no bases and no explicit metaclass are given, then
is used;
if an explicit metaclass is given and it is not an instance of
, then it is used directly as the metaclass;
if an instance of
is given as the explicit metaclass, or bases are defined, then the most derived metaclass is used.
The most derived metaclass is selected from the explicitly specified metaclass (if any) and the metaclasses (i.e. type(cls)) of all specified base classes. The most derived metaclass is one which is a subtype of all of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail with TypeError.
3.3.3.4. Preparing the class namespace
Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a __prepare__ attribute, it is called as namespace=metaclass.__prepare__(name,bases,**kwds) (where the additional keyword arguments, if any, come from the class definition). The __prepare__ method should be implemented as a
. The namespace returned by __prepare__ is passed in to __new__, but when the final class object is created the namespace is copied into a new dict.
If the metaclass has no __prepare__ attribute, then the class namespace is initialised as an empty ordered mapping.
See also
- Metaclasses in Python 3000Introduced the __prepare__ namespace hook
3.3.3.5. Executing the class body
The class body is executed (approximately) as exec(body,globals(),namespace). The key difference from a normal call to
is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function.
However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, or through the implicit lexically scoped __class__ reference described in the next section.
3.3.3.6. Creating the class object
Once the class namespace has been populated by executing the class body, the class object is created by calling metaclass(name,bases,namespace,**kwds) (the additional keywords passed here are the same as those passed to __prepare__).
This class object is the one that will be referenced by the zero-argument form of
. __class__ is an implicit closure reference created by the compiler if any methods in a class body refer to either __class__ or super. This allows the zero argument form of super() to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method.
CPython implementation detail: In CPython 3.6 and later, the __class__ cell is passed to the metaclass as a __classcell__ entry in the class namespace. If present, this must be propagated up to the type.__new__ call in order for the class to be initialised correctly. Failing to do so will result in a
in Python 3.8.
When using the default metaclass
, or any metaclass that ultimately calls type.__new__, the following additional customization steps are invoked after creating the class object:
The type.__new__ method collects all of the attributes in the class namespace that define a
method;
Those __set_name__ methods are called with the class being defined and the assigned name of that particular attribute;
The
hook is called on the immediate parent of the new class in its method resolution order.
After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class.
When a new class is created by type.__new__, the object provided as the namespace parameter is copied to a new ordered mapping and the original object is discarded. The new copy is wrapped in a read-only proxy, which becomes the
attribute of the class object.
See also
- New superDescribes the implicit __class__ closure reference
3.3.3.7. Uses for metaclasses
The potential uses for metaclasses are boundless. Some ideas that have been explored include enum, logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.
3.3.4. Customizing instance and subclass checks
The following methods are used to override the default behavior of the
and
built-in functions.
In particular, the metaclass
implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.
type.__instancecheck__(self, instance)
Return true if instance should be considered a (direct or indirect) instance of class. If defined, called to implement isinstance(instance,class).
type.__subclasscheck__(self, subclass)
Return true if subclass should be considered a (direct or indirect) subclass of class. If defined, called to implement issubclass(subclass,class).
Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.
See also
- Introducing Abstract Base ClassesIncludes the specification for customizing
and
behavior through
and
, with motivation for this functionality in the context of adding Abstract Base Classes (see the
module) to the language.
3.3.5. Emulating generic types
When using
, it is often useful to parameterize a
using Python’s square-brackets notation. For example, the annotation list[int] might be used to signify a
in which all the elements are of type
.
See also
- Type HintsIntroducing Python’s framework for type annotations
Documentation for objects representing parameterized generic classes
,
and
Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.
A class can generally only be parameterized if it defines the special class method __class_getitem__().
classmethodobject.__class_getitem__(cls, key)
Return an object representing the specialization of a generic class by type arguments found in key.
When defined on a class, __class_getitem__() is automatically a class method. As such, there is no need for it to be decorated with
when it is defined.
3.3.5.1. The purpose of __class_getitem__
The purpose of
is to allow runtime parameterization of standard-library generic classes in order to more easily apply
to these classes.
To implement custom generic classes that can be parameterized at runtime and understood by static type-checkers, users should either inherit from a standard library class that already implements
, or inherit from
, which has its own implementation of __class_getitem__().
Custom implementations of
on classes defined outside of the standard library may not be understood by third-party type-checkers such as mypy. Using __class_getitem__() on any class for purposes other than type hinting is discouraged.
3.3.5.2. __class_getitem__ versus __getitem__
Usually, the
of an object using square brackets will call the
instance method defined on the object’s class. However, if the object being subscribed is itself a class, the class method
may be called instead. __class_getitem__() should return a
object if it is properly defined.
Presented with the
obj[x], the Python interpreter follows something like the following process to decide whether
or
should be called:
frominspectimportisclassdefsubscribe(obj,x):"""Return the result of the expression 'obj[x]'"""class_of_obj=type(obj)# If the class of obj defines __getitem__,# call class_of_obj.__getitem__(obj, x)ifhasattr(class_of_obj,'__getitem__'):returnclass_of_obj.__getitem__(obj,x)# Else, if obj is a class and defines __class_getitem__,# call obj.__class_getitem__(x)elifisclass(obj)andhasattr(obj,'__class_getitem__'):returnobj.__class_getitem__(x)# Else, raise an exceptionelse:raiseTypeError(f"'{class_of_obj.__name__}' object is not subscriptable")In Python, all classes are themselves instances of other classes. The class of a class is known as that class’s
, and most classes have the
class as their metaclass. type does not define
, meaning that expressions such as list[int], dict[str,float] and tuple[str,bytes] all result in
being called:
>>> # list has class "type" as its metaclass, like most classes:>>> type(list)<class 'type'>>>> type(dict)==type(list)==type(tuple)==type(str)==type(bytes)True>>> # "list[int]" calls "list.__class_getitem__(int)">>> list[int]list[int]>>> # list.__class_getitem__ returns a GenericAlias object:>>> type(list[int])<class 'types.GenericAlias'>However, if a class has a custom metaclass that defines
, subscribing the class may result in different behaviour. An example of this can be found in the
module:
>>> fromenumimportEnum>>> classMenu(Enum):... """A breakfast menu"""... SPAM='spam'... BACON='bacon'...>>> # Enum classes have a custom metaclass:>>> type(Menu)<class 'enum.EnumMeta'>>>> # EnumMeta defines __getitem__,>>> # so __class_getitem__ is not called,>>> # and the result is not a GenericAlias object:>>> Menu['SPAM']<Menu.SPAM: 'spam'>>>> type(Menu['SPAM'])<enum 'Menu'>See also
- Core Support for typing module and generic typesIntroducing
, and outlining when a
results in __class_getitem__() being called instead of
3.3.6. Emulating callable objects
object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method is defined, x(arg1,arg2,...) roughly translates to type(x).__call__(x,arg1,...). The
class itself does not provide this method.
3.3.7. Emulating container types
The following methods can be defined to implement container objects. None of them are provided by the
class itself. Containers usually are
(such as
or
) or
(like
), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which 0<=k<N where N is the length of the sequence, or
objects, which define a range of items. It is also recommended that mappings provide the methods keys(), values(), items(), get(), clear(), setdefault(), pop(), popitem(), copy(), and update() behaving similar to those for Python’s standard
objects. The
module provides a
to help create those methods from a base set of
,
,
, and keys().
Mutable sequences should provide methods
,
,
,
,
,
,
,
, and
, like Python standard
objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods
,
,
,
,
and
described below; they should not define other numerical operators.
It is recommended that both mappings and sequences implement the
method to allow efficient use of the in operator; for mappings, in should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the
method to allow efficient iteration through the container; for mappings, __iter__() should iterate through the object’s keys; for sequences, it should iterate through the values.
object.__len__(self)
Called to implement the built-in function
. Should return the length of the object, an integer >= 0. Also, an object that doesn’t define a
method and whose __len__() method returns zero is considered to be false in a Boolean context.
CPython implementation detail: In CPython, the length is required to be at most
. If the length is larger than sys.maxsize some features (such as
) may raise
. To prevent raising OverflowError by truth value testing, an object must define a
method.
object.__length_hint__(self)
Called to implement
. Should return an estimated length for the object (which may be greater or less than the actual length). The length must be an integer >= 0. The return value may also be
, which is treated the same as if the __length_hint__ method didn’t exist at all. This method is purely an optimization and is never required for correctness.
Added in version 3.4.
object.__getitem__(self, subscript)
Called to implement subscription, that is, self[subscript]. See
for details on the syntax.
There are two types of built-in objects that support subscription via __getitem__():
sequences, where subscript (also called
) should be an integer or a
object. See the
for the expected behavior, including handling slice objects and negative indices.
mappings, where subscript is also called the
. See
for the expected behavior.
If subscript is of an inappropriate type, __getitem__() should raise
. If subscript has an inappropriate value, __getitem__() should raise an
or one of its subclasses (
for sequences;
for mappings).
Note
Slicing is handled by __getitem__(),
, and
. A call like
a[1:2]=bis translated to
a[slice(1,2,None)]=band so forth. Missing slice items are always filled in with None.
Note
The sequence iteration protocol (used, for example, in
loops), expects that an
will be raised for illegal indexes to allow proper detection of the end of a sequence.
Note
When
a class, the special class method
may be called instead of __getitem__(). See
__class_getitem__ versus __getitem__
for more details.
object.__setitem__(self, key, value)
Called to implement assignment to self[key]. Same note as for
. This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the __getitem__() method.
object.__delitem__(self, key)
Called to implement deletion of self[key]. Same note as for
. This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the __getitem__() method.
object.__missing__(self, key)
Called by
.
to implement self[key] for dict subclasses when key is not in the dictionary.
object.__iter__(self)
This method is called when an
is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container.
object.__reversed__(self)
Called (if present) by the
built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order.
If the __reversed__() method is not provided, the
built-in will fall back to using the sequence protocol (
and
). Objects that support the sequence protocol should only provide __reversed__() if they can provide an implementation that is more efficient than the one provided by reversed().
The membership test operators (
and
) are normally implemented as an iteration through a container. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be iterable.
object.__contains__(self, item)
Called to implement membership test operators. Should return true if item is in self, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs.
For objects that don’t define __contains__(), the membership test first tries iteration via
, then the old sequence iteration protocol via
, see
this section in the language reference
.
3.3.8. Emulating numeric types
The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined.
object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__matmul__(self, other)
object.__truediv__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)
These methods are called to implement the binary arithmetic operations (+, -, *, @, /, //, %,
,
, **, <<, >>, &, ^, |). For instance, to evaluate the expression x+y, where x is an instance of a class that has an __add__() method, type(x).__add__(x,y) is called. The __divmod__() method should be the equivalent to using __floordiv__() and __mod__(); it should not be related to __truediv__(). Note that __pow__() should be defined to accept an optional third argument if the three-argument version of the built-in pow() function is to be supported.
If one of those methods does not support the operation with the supplied arguments, it should return
.
object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rmatmul__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other[, modulo])
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)
These methods are called to implement the binary arithmetic operations (+, -, *, @, /, //, %,
,
, **, <<, >>, &, ^, |) with reflected (swapped) operands. These functions are only called if the operands are of different types, when the left operand does not support the corresponding operation
, or the right operand’s class is derived from the left operand’s class.
For instance, to evaluate the expression x-y, where y is an instance of a class that has an __rsub__() method, type(y).__rsub__(y,x) is called if type(x).__sub__(x,y) returns
or type(y) is a subclass of type(x).
Note that __rpow__() should be defined to accept an optional third argument if the three-argument version of the built-in
function is to be supported.
Changed in version 3.14: Three-argument
now try calling __rpow__() if necessary. Previously it was only called in two-argument pow() and the binary power operator.
Note
If the right operand’s type is a subclass of the left operand’s type and that subclass provides a different implementation of the reflected method for the operation, this method will be called before the left operand’s non-reflected method. This behavior allows subclasses to override their ancestors’ operations.
object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)
These methods are called to implement the augmented arithmetic assignments (+=, -=, *=, @=, /=, //=, %=, **=, <<=, >>=, &=, ^=, |=). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, or if that method returns
, the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an __iadd__() method, x+=y is equivalent to x=x.__iadd__(y) . If __iadd__() does not exist, or if x.__iadd__(y) returns NotImplemented, x.__add__(y) and y.__radd__(x) are considered, as with the evaluation of x+y. In certain situations, augmented assignment can result in unexpected errors (see
Why does a_tuple[i] += [‘item’] raise an exception when the addition works?
), but this behavior is in fact part of the data model.
object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)
Called to implement the unary arithmetic operations (-, +,
and ~).
object.__complex__(self)
object.__int__(self)
object.__float__(self)
Called to implement the built-in functions
,
and
. Should return a value of the appropriate type.
object.__index__(self)
Called to implement
, and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-in
,
and
functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer.
If
,
and
are not defined then corresponding built-in functions
,
and
fall back to __index__().
object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)
Called to implement the built-in function
and
functions
,
and
. Unless ndigits is passed to __round__() all these methods should return the value of the object truncated to an
(typically an
).
Changed in version 3.14:
no longer delegates to the __trunc__() method.
3.3.9. With Statement Context Managers
A context manager is an object that defines the runtime context to be established when executing a
statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the with statement (described in section
), but can also be used by directly invoking their methods.
Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc.
For more information on context managers, see
. The
class itself does not provide the context manager methods.
object.__enter__(self)
Enter the runtime context related to this object. The
statement will bind this method’s return value to the target(s) specified in the as clause of the statement, if any.
object.__exit__(self, exc_type, exc_value, traceback)
Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be
.
If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.
Note that __exit__() methods should not reraise the passed-in exception; this is the caller’s responsibility.
See also
- The “with” statementThe specification, background, and examples for the Python
statement.
3.3.10. Customizing positional arguments in class pattern matching
When using a class name in a pattern, positional arguments in the pattern are not allowed by default, i.e. caseMyClass(x,y) is typically invalid without special support in MyClass. To be able to use that kind of pattern, the class needs to define a __match_args__ attribute.
object.__match_args__
This class variable can be assigned a tuple of strings. When this class is used in a class pattern with positional arguments, each positional argument will be converted into a keyword argument, using the corresponding value in __match_args__ as the keyword. The absence of this attribute is equivalent to setting it to ().
For example, if MyClass.__match_args__ is ("left","center","right") that means that caseMyClass(x,y) is equivalent to caseMyClass(left=x,center=y). Note that the number of arguments in the pattern must be smaller than or equal to the number of elements in __match_args__; if it is larger, the pattern match attempt will raise a
.
Added in version 3.10.
See also
- Structural Pattern MatchingThe specification for the Python match statement.
3.3.11. Emulating buffer types
The
provides a way for Python objects to expose efficient access to a low-level memory array. This protocol is implemented by builtin types such as
and
, and third-party libraries may define additional buffer types.
While buffer types are usually implemented in C, it is also possible to implement the protocol in Python.
object.__buffer__(self, flags)
Called when a buffer is requested from self (for example, by the
constructor). The flags argument is an integer representing the kind of buffer requested, affecting for example whether the returned buffer is read-only or writable.
provides a convenient way to interpret the flags. The method must return a memoryview object.
Thread safety: In
Python, implementations must manage any internal export counter using atomic operations. The method must be safe to call concurrently from multiple threads, and the returned buffer’s underlying data must remain valid until the corresponding
call completes. See
Thread safety for memoryview objects
for details.
object.__release_buffer__(self, buffer)
Called when a buffer is no longer needed. The buffer argument is a
object that was previously returned by
. The method must release any resources associated with the buffer. This method should return None.
Thread safety: In
Python, any export counter decrement must use atomic operations. Resource cleanup must be thread-safe, as the final release may race with concurrent releases from other threads.
Buffer objects that do not need to perform any cleanup are not required to implement this method.
Added in version 3.12.
See also
- Making the buffer protocol accessible in PythonIntroduces the Python __buffer__ and __release_buffer__ methods.
ABC for buffer types.
3.3.12. Annotations
Functions, classes, and modules may contain
, which are a way to associate information (usually
) with a symbol.
object.__annotations__
This attribute contains the annotations for an object. It is
, so accessing the attribute may execute arbitrary code and raise exceptions. If evaluation is successful, the attribute is set to a dictionary mapping from variable names to annotations.
Changed in version 3.14: Annotations are now lazily evaluated.
object.__annotate__(format)
An
. Returns a new dictionary object mapping attribute/parameter names to their annotation values.
Takes a format parameter specifying the format in which annotations values should be provided. It must be a member of the
enum, or an integer with a value corresponding to a member of the enum.
If an annotate function doesn’t support the requested format, it must raise
. Annotate functions must always support
format; they must not raise
when called with this format.
When called with
format, an annotate function may raise
; it must not raise NameError when called requesting any other format.
If an object does not have any annotations,
should preferably be set to None (it can’t be deleted), rather than set to a function that returns an empty dict.
Added in version 3.14.
See also
— Deferred evaluation of annotation using descriptorsIntroduces lazy evaluation of annotations and the __annotate__ function.
3.3.13. Special method lookup
For custom classes, implicit invocations of special methods are only guaranteed to work correctly if defined on an object’s type, not in the object’s instance dictionary. That behaviour is the reason why the following code raises an exception:
>>> classC:... pass...>>> c=C()>>> c.__len__=lambda:5>>> len(c)Traceback (most recent call last): File "<stdin>", line 1, in <module>TypeError: object of type 'C' has no len()The rationale behind this behaviour lies with a number of special methods such as
and
that are implemented by all objects, including type objects. If the implicit lookup of these methods used the conventional lookup process, they would fail when invoked on the type object itself:
>>> 1.__hash__()==hash(1)True>>> int.__hash__()==hash(int)Traceback (most recent call last): File "<stdin>", line 1, in <module>TypeError: descriptor '__hash__' of 'int' object needs an argumentIncorrectly attempting to invoke an unbound method of a class in this way is sometimes referred to as ‘metaclass confusion’, and is avoided by bypassing the instance when looking up special methods:
>>> type(1).__hash__(1)==hash(1)True>>> type(int).__hash__(int)==hash(int)TrueIn addition to bypassing any instance attributes in the interest of correctness, implicit special method lookup generally also bypasses the
method even of the object’s metaclass:
>>> classMeta(type):... def__getattribute__(*args):... print("Metaclass getattribute invoked")... returntype.__getattribute__(*args)...>>> classC(object,metaclass=Meta):... def__len__(self):... return10... def__getattribute__(*args):... print("Class getattribute invoked")... returnobject.__getattribute__(*args)...>>> c=C()>>> c.__len__()# Explicit lookup via instanceClass getattribute invoked10>>> type(c).__len__(c)# Explicit lookup via typeMetaclass getattribute invoked10>>> len(c)# Implicit lookup10Bypassing the
machinery in this fashion provides significant scope for speed optimisations within the interpreter, at the cost of some flexibility in the handling of special methods (the special method must be set on the class object itself in order to be consistently invoked by the interpreter).
3.4. Coroutines
3.4.1. Awaitable Objects
An
object generally implements an
method.
returned from
functions are awaitable.
object.__await__(self)
Must return an
. Should be used to implement
objects. For instance,
implements this method to be compatible with the
expression. The
class itself is not awaitable and does not provide this method.
Note
The language doesn’t place any restriction on the type or value of the objects yielded by the iterator returned by __await__, as this is specific to the implementation of the asynchronous execution framework (e.g.
) that will be managing the
object.
Added in version 3.5.
See also
for additional information about awaitable objects.
3.4.2. Coroutine Objects
are
objects. A coroutine’s execution can be controlled by calling
and iterating over the result. When the coroutine has finished executing and returns, the iterator raises
, and the exception’s
attribute holds the return value. If the coroutine raises an exception, it is propagated by the iterator. Coroutines should not directly raise unhandled StopIteration exceptions.
Coroutines also have the methods listed below, which are analogous to those of generators (see
). However, unlike generators, coroutines do not directly support iteration.
Coroutines are
over the types of their yield, send, and return values, respectively.
Changed in version 3.5.2: It is a
to await on a coroutine more than once.
coroutine.send(value)
Starts or resumes execution of the coroutine. If value is None, this is equivalent to advancing the iterator returned by
. If value is not None, this method delegates to the
method of the iterator that caused the coroutine to suspend. The result (return value,
, or other exception) is the same as when iterating over the __await__() return value, described above.
coroutine.throw(value)
coroutine.throw(type[, value[, traceback]])Raises the specified exception in the coroutine. This method delegates to the
method of the iterator that caused the coroutine to suspend, if it has such a method. Otherwise, the exception is raised at the suspension point. The result (return value,
, or other exception) is the same as when iterating over the
return value, described above. If the exception is not caught in the coroutine, it propagates back to the caller.
Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.
coroutine.close()
Causes the coroutine to clean itself up and exit. If the coroutine is suspended, this method first delegates to the
method of the iterator that caused the coroutine to suspend, if it has such a method. Then it raises
at the suspension point, causing the coroutine to immediately clean itself up. Finally, the coroutine is marked as having finished executing, even if it was never started.
Coroutine objects are automatically closed using the above process when they are about to be destroyed.
3.4.3. Asynchronous Iterators
An asynchronous iterator can call asynchronous code in its __anext__ method.
Asynchronous iterators can be used in an
statement.
The
class itself does not provide these methods.
object.__aiter__(self)
Must return an asynchronous iterator object.
object.__anext__(self)
Must return an awaitable resulting in a next value of the iterator. Should raise a
error when the iteration is over.
An example of an asynchronous iterable object:
classReader:asyncdefreadline(self):...def__aiter__(self):returnselfasyncdef__anext__(self):val=awaitself.readline()ifval==b'':raiseStopAsyncIterationreturnvalAdded in version 3.5.
Changed in version 3.7: Prior to Python 3.7,
could return an awaitable that would resolve to an
.
Starting with Python 3.7,
must return an asynchronous iterator object. Returning anything else will result in a
error.
3.4.4. Asynchronous Context Managers
An asynchronous context manager is a context manager that is able to suspend execution in its __aenter__ and __aexit__ methods.
Asynchronous context managers can be used in an
statement.
The
class itself does not provide these methods.
object.__aenter__(self)
Semantically similar to
, the only difference being that it must return an awaitable.
object.__aexit__(self, exc_type, exc_value, traceback)
Semantically similar to
, the only difference being that it must return an awaitable.
An example of an asynchronous context manager class:
classAsyncContextManager:asyncdef__aenter__(self):awaitlog('entering context')asyncdef__aexit__(self,exc_type,exc,tb):awaitlog('exiting context')Added in version 3.5.
Footnotes