Data model

3. Data model¶
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 is
operator compares the identity of two objects; the
id()
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 type()
function returns an object’s type (which is an object
itself). Like its identity, an object’s type is also unchangeable.
[1]
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 gc
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 try
…except
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 try
…finally
statement
and the with
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 int
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 NotImplemented
. 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 NotImplemented
in a boolean context was deprecated.
Changed in version 3.14: Evaluating NotImplemented
in a boolean context now raises a TypeError
.
It previously evaluated to True
and emitted a DeprecationWarning
since Python 3.9.
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. numbers.Number
¶
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
__repr__()
and __str__()
, 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. numbers.Integral
¶
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 (
int
) 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 (
bool
) These represent the truth values False and True. The two objects representing the values
False
andTrue
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. numbers.Real
(float
)¶
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. numbers.Complex
(complex
)¶
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 len()
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 IndexError
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 None
, 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:
- Strings
A string (
str
) is a sequence of values that represent characters, or more formally, Unicode code points. All the code points in the range0
to0x10FFFF
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
ord()
converts a code point from its string form to an integer in the range0
to0x10FFFF
;chr()
converts an integer in the range0
to0x10FFFF
to the corresponding length1
string object.str.encode()
can be used to convert astr
tobytes
using the given text encoding, andbytes.decode()
can be used to achieve the opposite.- Tuples
The items of a
tuple
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.- Bytes
A
bytes
object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (likeb'abc'
) and the built-inbytes()
constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via thedecode()
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 del
(delete) statements.
Note
The collections
and array
module provide
additional examples of mutable sequence types.
There are currently two intrinsic mutable sequence types:
- Lists
The 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 Arrays
A bytearray object is a mutable array. They are created by the built-in
bytearray()
constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutablebytes
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 len()
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:
- Sets
These represent a mutable set. They are created by the built-in
set()
constructor and can be modified afterwards by several methods, such asadd()
.- Frozen sets
These represent an immutable set. They are created by the built-in
frozenset()
constructor. As a frozenset is immutable and hashable, 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
del
statements. The built-in function len()
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 Dictionary displays).
The extension modules dbm.ndbm
and dbm.gnu
provide
additional examples of mapping types, as does the collections
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 Calls) can be applied:
3.2.8.1. User-defined functions¶
A user-defined function object is created by a function definition (see section Function definitions). 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 |
|---|---|
|
A reference to the Added in version 3.10. |
|
A reference to the |
|
A cell object has the attribute |
3.2.8.1.2. Special writable attributes¶
Most of these attributes check the type of the assigned value:
Attribute |
Meaning |
|---|---|
|
The function’s documentation string, or |
|
The function’s name.
See also: |
|
The function’s qualified name.
See also: Added in version 3.3. |
|
The name of the module the function was defined in,
or |
|
A |
|
The code object representing the compiled function body. |
|
The namespace supporting arbitrary function attributes.
See also: |
|
A Changed in version 3.14: Annotations are now lazily evaluated. See PEP 649. |
|
The annotate function for this function, or Added in version 3.14. |
|
A |
|
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 built-in functions may be supported in the future.
Additional information about a function’s definition can be retrieved from its
code object
(accessible via the __code__
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:
|
Refers to the class instance object to which the method is bound |
|
Refers to the original function object |
|
The method’s documentation
(same as |
|
The name of the method
(same as |
|
The name of the module the method was defined in, or |
Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object.
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 function object or a
classmethod
object.
When an instance method object is created by retrieving a user-defined
function object from a class via one of its
instances, its __self__
attribute is the instance, and the
method object is said to be bound. The new method’s __func__
attribute is the original function object.
When an instance method object is created by retrieving a classmethod
object from a class or instance, its __self__
attribute is the
class itself, and its __func__
attribute is the function object
underlying the class method.
When an instance method object is called, the underlying function
(__func__
) is called, inserting the class instance
(__self__
) 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 classmethod
object, the
“class instance” stored in __self__
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 yield
statement (see section
The yield statement) is called a generator function. Such a function, when
called, always returns an iterator object which can be used to
execute the body of the function: calling the iterator’s
iterator.__next__()
method will cause the function to execute until
it provides a value using the yield
statement. When the
function executes a return
statement or falls off the end, a
StopIteration
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 async def
is called
a coroutine function. Such a function, when called, returns a
coroutine object. It may contain await
expressions,
as well as async with
and async for
statements. See
also the Coroutine Objects section.
3.2.8.5. Asynchronous generator functions¶
A function or method which is defined using async def
and
which uses the yield
statement is called a
asynchronous generator function. Such a function, when called,
returns an asynchronous iterator object which can be used in an
async for
statement to execute the body of the function.
Calling the asynchronous iterator’s
aiterator.__anext__
method
will return an awaitable which when awaited
will execute until it provides a value using the yield
expression. When the function executes an empty return
statement or falls off the end, a StopAsyncIteration
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 len()
and math.sin()
(math
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, orNone
if unavailable. Seefunction.__doc__
.__name__
is the function’s name. Seefunction.__name__
.__self__
is set toNone
(but see the next item).__module__
is the name of the module the function was defined in orNone
if unavailable. Seefunction.__module__
.
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
other instance methods
.)
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 __new__()
. The arguments of the call are passed to
__new__()
and, in the typical case, to __init__()
to
initialize the new instance.
3.2.8.9. Class Instances¶
Instances of arbitrary classes can be made callable by defining a
__call__()
method in their class.
3.2.9. Modules¶
Modules are a basic organizational unit of Python code, and are created by
the import system as invoked either by the
import
statement, or by calling
functions such as importlib.import_module()
and built-in
__import__()
. A module object has a namespace implemented by a
dictionary
object (this is the dictionary referenced by the
__globals__
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:__doc__ attributes
.
- module.__annotations__¶
A dictionary containing variable annotations collected during module body execution. For best practices on working with
__annotations__
, seeannotationlib
.Changed in version 3.14: Annotations are now lazily evaluated. See PEP 649.
- module.__annotate__¶
The annotate function for this module, or
None
if the module has no annotations. See also:__annotate__
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
Class definitions). 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
__self__
attribute is C
.
When it would yield a staticmethod
object,
it is transformed into the object wrapped by the static method
object. See section Implementing Descriptors for another way in which attributes
retrieved from a class may differ from those actually contained in its
__dict__
.
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 |
|---|---|
|
The class’s name.
See also: |
|
The class’s qualified name.
See also: |
|
The name of the module in which the class was defined. |
|
A |
|
A |
|
CPython implementation detail: The single base class in the inheritance chain that is responsible
for the memory layout of instances. This attribute corresponds to
|
|
The class’s documentation string, or |
|
A dictionary containing
variable annotations
collected during class body execution. See also:
For best practices on working with Warning Accessing the This attribute does not exist on certain builtin classes. On
user-defined classes without Changed in version 3.14: Annotations are now lazily evaluated. See PEP 649. |
|
The annotate function for this class, or Added in version 3.14. |
|
A Added in version 3.12. |
|
A Added in version 3.13. |
|
The line number of the first line of the class definition,
including decorators.
Setting the Added in version 3.13. |
|
The |
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
__mro__
.
- 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:
>>> class A: pass >>> class B(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 __self__
attribute is the instance. Static method and
class method objects are also transformed; see above under “Classes”. See
section Implementing Descriptors for another way in which attributes of a class
retrieved via its instances may differ from the objects actually stored in
the class’s __dict__
. If no class attribute is found, and the
object’s class has a __getattr__()
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 __setattr__()
or
__delattr__()
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 Special method names.
3.2.11.1. Special attributes¶
- object.__class__¶
The class to which a class instance belongs.
3.2.12. I/O objects (also known as file objects)¶
A file object represents an open file. Various shortcuts are
available to create file objects: the open()
built-in function, and
also os.popen()
, os.fdopen()
, and the
makefile()
method of socket objects (and perhaps by
other functions or methods provided by extension modules).
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 io.TextIOBase
abstract class.
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 bytecode. 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¶
|
The function name |
|
The fully qualified function name Added in version 3.11. |
|
The total number of positional parameters (including positional-only parameters and parameters with default values) that the function has |
|
The number of positional-only parameters (including arguments with default values) that the function has |
|
The number of keyword-only parameters (including arguments with default values) that the function has |
|
The number of local variables used by the function (including parameters) |
|
A |
|
A |
|
A Note: references to global and builtin names are not included. |
|
A string representing the sequence of bytecode instructions in the function |
|
A |
|
A |
|
The name of the file from which the code was compiled |
|
The line number of the first line of the function |
|
A string encoding the mapping from bytecode 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. |
|
The required stack size of the code object |
|
An |
The following flag bits are defined for co_flags
:
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 Code Objects Bit Flags for details
on the semantics of each flags that might be present.
Future feature declarations (for example, from __future__ import division
) also use bits
in co_flags
to indicate whether a code object was compiled with a
particular feature enabled. See compiler_flag
.
Other bits in co_flags
are reserved for internal use.
If a code object represents a function and has a docstring,
the CO_HAS_DOCSTRING
bit is set in co_flags
and the first item in co_consts
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 bytecode instruction in the code object.
The iterator returns
tuple
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
-X
no_debug_ranges
.Loading a pyc file compiled while using
-X
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
None
.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
-X
no_debug_ranges
command line flag or thePYTHONNODEBUGRANGES
environment variable can be used.
- codeobject.co_lines()¶
Returns an iterator that yields information about successive ranges of bytecodes. Each item yielded is a
(start, end, lineno)
tuple
:start
(anint
) represents the offset (inclusive) of the start of the bytecode rangeend
(anint
) represents the offset (exclusive) of the end of the bytecode rangelineno
is anint
representing the line number of the bytecode range, orNone
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 oftuple
s, thestart
of the second will be equal to theend
of the first.No range will be backwards:
end >= start
for all triples.The last
tuple
yielded will haveend
equal to the size of the bytecode.
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 bytecode compiler.Added in version 3.10.
See also
- PEP 626 - 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
copy.replace()
.Added in version 3.8.
3.2.13.2. Frame objects¶
Frame objects represent execution frames. They may occur in traceback objects, and are also passed to registered trace functions.
3.2.13.2.1. Special read-only attributes¶
|
Points to the previous stack frame (towards the caller),
or |
|
The code object being executed in this frame.
Accessing this attribute raises an auditing event
|
|
The mapping used by the frame to look up local variables. If the frame refers to an optimized scope, this may return a write-through proxy object. Changed in version 3.13: Return a proxy for optimized scopes. |
|
The dictionary used by the frame to look up global variables |
|
The dictionary used by the frame to look up built-in (intrinsic) names |
|
The “precise instruction” of the frame object (this is an index into the bytecode string of the code object) |
|
The generator or coroutine object that owns this frame,
or Added in version 3.14. |
3.2.13.2.2. Special writable attributes¶
|
If not |
|
Set this attribute to |
|
Set this attribute to |
|
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 local variables held by the frame. Also, if the frame belonged to a generator, the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an exception and storing its traceback for later use).
RuntimeError
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
RuntimeError
(as has always been the case for executing frames).
3.2.13.3. Traceback objects¶
Traceback objects represent the stack trace of an exception.
A traceback object
is implicitly created when an exception occurs, and may also be explicitly
created by calling types.TracebackType
.
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
The try statement.) It is accessible as the third item of the
tuple returned by sys.exc_info()
, and as the
__traceback__
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 sys.last_traceback
.
For explicitly created tracebacks, it is up to the creator of the traceback
to determine how the tb_next
attributes should be linked to
form a full stack trace.
Special read-only attributes:
|
Points to the execution frame of the current level. Accessing this attribute raises an
auditing event |
|
Gives the line number where the exception occurred |
|
Indicates the “precise instruction”. |
The line number and last instruction in the traceback may differ from the
line number of its frame object if the exception
occurred in a
try
statement with no matching except clause or with a
finally
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), orNone
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
__getitem__()
methods. They are also created by the built-in slice()
function.
Special read-only attributes: start
is the lower bound;
stop
is the upper bound; step
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 staticmethod()
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 “instance methods”. Class method objects are created
by the built-in classmethod()
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
__getitem__()
,
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
AttributeError
or TypeError
).
Setting a special method to None
indicates that the corresponding
operation is not available. For example, if a class sets
__iter__()
to None
, the class is not iterable, so calling
iter()
on its instances will raise a TypeError
(without
falling back to __getitem__()
). [2]
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 NodeList 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 usingsuper().__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__init__()
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__init__()
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
__new__()
), 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
__new__()
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 aTypeError
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 CPython implementation only calls it once.It is not guaranteed that
__del__()
methods are called for objects that still exist when the interpreter exits.weakref.finalize
provides a straightforward way to register a cleanup function to be called when an object is garbage collected.Note
del x
doesn’t directly callx.__del__()
— the former decrements the reference count forx
by one, and the latter is only called whenx
’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 cyclic garbage collector. 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
gc
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 tosys.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 toNone
. 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
repr()
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<...some useful description...>
should be returned. The return value must be a string object. If a class defines__repr__()
but not__str__()
, 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
object
class itself.
- object.__str__(self)¶
Called by
str(object)
, the default__format__()
implementation, and the built-in functionprint()
, to compute the “informal” or nicely printable string representation of an object. The return value must be a str object.This method differs from
object.__repr__()
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
object
callsobject.__repr__()
.
- object.__bytes__(self)¶
Called by bytes to compute a byte-string representation of an object. This should return a
bytes
object. Theobject
class itself does not provide this method.
- object.__format__(self, format_spec)¶
Called by the
format()
built-in function, and by extension, evaluation of formatted string literals and thestr.format()
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
object
class should be given an empty format_spec string. It delegates to__str__()
.Changed in version 3.4: The __format__ method of
object
itself raises aTypeError
if passed any non-empty string.Changed in version 3.7:
object.__format__(x, '')
is now equivalent tostr(x)
rather thanformat(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
callsx.__lt__(y)
,x<=y
callsx.__le__(y)
,x==y
callsx.__eq__(y)
,x!=y
callsx.__ne__(y)
,x>y
callsx.__gt__(y)
, andx>=y
callsx.__ge__(y)
.A rich comparison method may return the singleton
NotImplemented
if it does not implement the operation for a given pair of arguments. By convention,False
andTrue
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 anif
statement), Python will callbool()
on the value to determine if the result is true or false.By default,
object
implements__eq__()
by usingis
, returningNotImplemented
in the case of a false comparison:True if x is y else NotImplemented
. For__ne__()
, by default it delegates to__eq__()
and inverts the result unless it isNotImplemented
. There are no other implied relationships among the comparison operators or default implementations; for example, the truth of(x<y or x==y)
does not implyx<=y
. To automatically generate ordering operations from a single root operation, seefunctools.total_ordering()
.By default, the
object
class provides implementations consistent with Value comparisons: equality compares according to object identity, and order comparisons raiseTypeError
. Each default method may generate these results directly, but may also returnNotImplemented
.See the paragraph on
__hash__()
for some important notes on creating hashable 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
NotImplemented
, the==
and!=
operators will fall back tois
andis not
, respectively.
- object.__hash__(self)¶
Called by built-in function
hash()
and for operations on members of hashed collections includingset
,frozenset
, anddict
. 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): return hash((self.name, self.nick, self.color))
Note
hash()
truncates the value returned from an object’s custom__hash__()
method to the size of aPy_ssize_t
. 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 withpython -c "import sys; print(sys.hash_info.width)"
.If a class does not define an
__eq__()
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 hashable 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
__eq__()
and__hash__()
methods by default (inherited from theobject
class); with them, all objects compare unequal (except with themselves) andx.__hash__()
returns an appropriate value such thatx == y
implies both thatx is y
andhash(x) == hash(y)
.A class that overrides
__eq__()
and does not define__hash__()
will have its__hash__()
implicitly set toNone
. When the__hash__()
method of a class isNone
, instances of the class will raise an appropriateTypeError
when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checkingisinstance(obj, collections.abc.Hashable)
.If a class that overrides
__eq__()
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
__eq__()
wishes to suppress hash support, it should include__hash__ = None
in the class definition. A class which defines its own__hash__()
that explicitly raises aTypeError
would be incorrectly identified as hashable by anisinstance(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 http://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
PYTHONHASHSEED
.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 returnFalse
orTrue
. When this method is not defined,__len__()
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 theobject
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
AttributeError
(either__getattribute__()
raises anAttributeError
because name is not an instance attribute or an attribute in the class tree forself
; or__get__()
of a name property raisesAttributeError
). This method should either return the (computed) attribute value or raise anAttributeError
exception. Theobject
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__setattr__()
.) 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__getattribute__()
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
__getattr__()
, the latter will not be called unless__getattribute__()
either calls it explicitly or raises anAttributeError
. This method should return the (computed) attribute value or raise anAttributeError
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 built-in functions. See Special method lookup.
For certain sensitive attribute accesses, raises an auditing event
object.__getattr__
with argumentsobj
andname
.
- 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 auditing event
object.__setattr__
with argumentsobj
,name
,value
.
- object.__delattr__(self, name)¶
Like
__setattr__()
but for attribute deletion instead of assignment. This should only be implemented ifdel obj.name
is meaningful for the object.For certain sensitive attribute deletions, raises an auditing event
object.__delattr__
with argumentsobj
andname
.
- object.__dir__(self)¶
Called when
dir()
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¶
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 AttributeError
. If an attribute is
not found on a module object through the normal lookup, i.e.
object.__getattribute__()
, 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 dir()
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 types.ModuleType
. For example:
import sys
from types import ModuleType
class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'
def __setattr__(self, attr, value):
print(f'Setting {attr}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note
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
- PEP 562 - 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’ __dict__
. The object
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
AttributeError
exception.PEP 252 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__getattribute__()
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__delete__()
changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors 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 theinspect
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: __get__()
, __set__()
, and
__delete__()
. 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 Call
The simplest and least common call is when user code directly invokes a descriptor method:
x.__get__(a)
.- Instance Binding
If binding to an object instance,
a.x
is transformed into the call:type(a).__dict__['x'].__get__(a, type(a))
.- Class Binding
If binding to a class,
A.x
is transformed into the call:A.__dict__['x'].__get__(None, A)
.- Super Binding
A dotted lookup such as
super(A, a).x
searchesa.__class__.__mro__
for a base classB
followingA
and then returnsB.__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 __get__()
, __set__()
and
__delete__()
. 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
@staticmethod
and @classmethod
) 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 property()
function 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 __dict__
and __weakref__
(unless explicitly declared in __slots__ or available in a parent.)
The space saved over using __dict__
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
__dict__
and __weakref__ for each instance.
Notes on using __slots__:
When inheriting from a class without __slots__, the
__dict__
and __weakref__ attribute of the instances will always be accessible.Without a
__dict__
variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raisesAttributeError
. 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
weak references
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 descriptors 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
__dict__
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.
TypeError
will be raised if nonempty __slots__ are defined for a class derived from a"variable-length" built-in type
such asint
,bytes
, andtuple
.Any non-string iterable may be assigned to __slots__.
If a
dictionary
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 byinspect.getdoc()
and displayed in the output ofhelp()
.__class__
assignment works only if both classes have the same __slots__.Multiple inheritance 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
TypeError
.If an iterator is used for __slots__ then a descriptor 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, __init_subclass__()
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.
- classmethod object.__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:class Philosopher: def __init_subclass__(cls, /, default_name, **kwargs): super().__init_subclass__(**kwargs) cls.default_name = default_name class AustralianPhilosopher(Philosopher, default_name="Bruce"): pass
The 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 astype(cls)
.Added in version 3.6.
When a class is created, type.__new__()
scans the class variables
and makes callbacks to those with a __set_name__()
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:
class A: 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:class A: pass c = C() A.x = c # The hook is not called c.__set_name__(A, 'x') # Manually invoke the hook
See Creating the class object for more details.
Added in version 3.6.
3.3.3.1. Metaclasses¶
By default, classes are constructed using type()
. 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
:
class Meta(type):
pass
class MyClass(metaclass=Meta):
pass
class MySubclass(MyClass):
pass
Any 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
type
, 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.
See also
types.resolve_bases()
Dynamically resolve bases that are not instances of
type
.types.get_original_bases()
Retrieve a class’s “original bases” prior to modifications by
__mro_entries__()
.- PEP 560
Core support for typing module and generic types.
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
type()
is used;if an explicit metaclass is given and it is not an instance of
type()
, then it is used directly as the metaclass;if an instance of
type()
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
classmethod
. 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
- PEP 3115 - Metaclasses in Python 3000
Introduced 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 exec()
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 super()
. __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 RuntimeError
in Python 3.8.
When using the default metaclass type
, 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__set_name__()
method;Those
__set_name__
methods are called with the class being defined and the assigned name of that particular attribute;The
__init_subclass__()
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 __dict__
attribute of the class object.
See also
- PEP 3135 - New super
Describes 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
isinstance()
and issubclass()
built-in functions.
In particular, the metaclass abc.ABCMeta
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
- PEP 3119 - Introducing Abstract Base Classes
Includes the specification for customizing
isinstance()
andissubclass()
behavior through__instancecheck__()
and__subclasscheck__()
, with motivation for this functionality in the context of adding Abstract Base Classes (see theabc
module) to the language.
3.3.5. Emulating generic types¶
When using type annotations, it is often useful to
parameterize a generic type using Python’s square-brackets notation.
For example, the annotation list[int]
might be used to signify a
list
in which all the elements are of type int
.
See also
- PEP 484 - Type Hints
Introducing Python’s framework for type annotations
- Generic Alias Types
Documentation for objects representing parameterized generic classes
- Generics, user-defined generics and
typing.Generic
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__()
.
- classmethod object.__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@classmethod
when it is defined.
3.3.5.1. The purpose of __class_getitem__¶
The purpose of __class_getitem__()
is to allow runtime
parameterization of standard-library generic classes in order to more easily
apply type hints 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 __class_getitem__()
, or
inherit from typing.Generic
, which has its own implementation of
__class_getitem__()
.
Custom implementations of __class_getitem__()
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 subscription of an object using square
brackets will call the __getitem__()
instance method defined on
the object’s class. However, if the object being subscribed is itself a class,
the class method __class_getitem__()
may be called instead.
__class_getitem__()
should return a GenericAlias
object if it is properly defined.
Presented with the expression obj[x]
, the Python interpreter
follows something like the following process to decide whether
__getitem__()
or __class_getitem__()
should be
called:
from inspect import isclass
def subscribe(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)
if hasattr(class_of_obj, '__getitem__'):
return class_of_obj.__getitem__(obj, x)
# Else, if obj is a class and defines __class_getitem__,
# call obj.__class_getitem__(x)
elif isclass(obj) and hasattr(obj, '__class_getitem__'):
return obj.__class_getitem__(x)
# Else, raise an exception
else:
raise TypeError(
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 metaclass, and most classes have the
type
class as their metaclass. type
does not define
__getitem__()
, meaning that expressions such as list[int]
,
dict[str, float]
and tuple[str, bytes]
all result in
__class_getitem__()
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
__getitem__()
, subscribing the class may result in different
behaviour. An example of this can be found in the enum
module:
>>> from enum import Enum
>>> class Menu(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
- PEP 560 - Core Support for typing module and generic types
Introducing
__class_getitem__()
, and outlining when a subscription results in__class_getitem__()
being called instead of__getitem__()
3.3.6. Emulating callable objects¶
3.3.7. Emulating container types¶
The following methods can be defined to implement container objects. None of them
are provided by the object
class itself. Containers usually are
sequences (such as lists
or
tuples
) or mappings (like
dictionaries),
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 slice
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 dictionary
objects. The collections.abc
module provides a
MutableMapping
abstract base class to help create those methods from a base set of
__getitem__()
, __setitem__()
,
__delitem__()
, and keys()
.
Mutable sequences should provide methods
append()
, clear()
, count()
,
extend()
, index()
, insert()
,
pop()
, remove()
, and reverse()
,
like Python standard list
objects.
Finally, sequence types should implement addition (meaning concatenation) and
multiplication (meaning repetition) by defining the methods
__add__()
, __radd__()
, __iadd__()
,
__mul__()
, __rmul__()
and __imul__()
described below; they should not define other numerical
operators.
It is recommended that both mappings and sequences implement the
__contains__()
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 __iter__()
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
len()
. Should return the length of the object, an integer>=
0. Also, an object that doesn’t define a__bool__()
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
sys.maxsize
. If the length is larger thansys.maxsize
some features (such aslen()
) may raiseOverflowError
. To prevent raisingOverflowError
by truth value testing, an object must define a__bool__()
method.
- object.__length_hint__(self)¶
Called to implement
operator.length_hint()
. 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 beNotImplemented
, 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.
Note
Slicing is done exclusively with the following three methods. A call like
a[1:2] = b
is translated to
a[slice(1, 2, None)] = b
and so forth. Missing slice items are always filled in with None
.
- object.__getitem__(self, subscript)¶
Called to implement subscription, that is,
self[subscript]
. See Subscriptions and slicings for details on the syntax.There are two types of built-in objects that support subscription via
__getitem__()
:sequences, where subscript (also called index) should be an integer or a
slice
object. See the sequence documentation for the expected behavior, including handlingslice
objects and negative indices.mappings, where subscript is also called the key. See mapping documentation for the expected behavior.
If subscript is of an inappropriate type,
__getitem__()
should raiseTypeError
. If subscript has an inappropriate value,__getitem__()
should raise anLookupError
or one of its subclasses (IndexError
for sequences;KeyError
for mappings).Note
The sequence iteration protocol (used, for example, in
for
loops), expects that anIndexError
will be raised for illegal indexes to allow proper detection of the end of a sequence.Note
When subscripting a class, the special class method
__class_getitem__()
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__getitem__()
. 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__getitem__()
. 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
dict
.__getitem__()
to implementself[key]
for dict subclasses when key is not in the dictionary.
- object.__iter__(self)¶
This method is called when an iterator 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
reversed()
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, thereversed()
built-in will fall back to using the sequence protocol (__len__()
and__getitem__()
). Objects that support the sequence protocol should only provide__reversed__()
if they can provide an implementation that is more efficient than the one provided byreversed()
.
The membership test operators (in
and not in
) 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__iter__()
, then the old sequence iteration protocol via__getitem__()
, 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 (
+
,-
,*
,@
,/
,//
,%
,divmod()
,pow()
,**
,<<
,>>
,&
,^
,|
). For instance, to evaluate the expressionx + 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-inpow()
function is to be supported.If one of those methods does not support the operation with the supplied arguments, it should return
NotImplemented
.
- 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 (
+
,-
,*
,@
,/
,//
,%
,divmod()
,pow()
,**
,<<
,>>
,&
,^
,|
) 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 [3], or the right operand’s class is derived from the left operand’s class. [4] For instance, to evaluate the expressionx - y
, where y is an instance of a class that has an__rsub__()
method,type(y).__rsub__(y, x)
is called iftype(x).__sub__(x, y)
returnsNotImplemented
ortype(y)
is a subclass oftype(x)
. [5]Note that
__rpow__()
should be defined to accept an optional third argument if the three-argument version of the built-inpow()
function is to be supported.Changed in version 3.14: Three-argument
pow()
now try calling__rpow__()
if necessary. Previously it was only called in two-argumentpow()
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 returnsNotImplemented
, 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 tox = x.__iadd__(y)
. If__iadd__()
does not exist, or ifx.__iadd__(y)
returnsNotImplemented
,x.__add__(y)
andy.__radd__(x)
are considered, as with the evaluation ofx + 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 (
-
,+
,abs()
and~
).
- object.__complex__(self)¶
- object.__int__(self)¶
- object.__float__(self)¶
Called to implement the built-in functions
complex()
,int()
andfloat()
. Should return a value of the appropriate type.
- object.__index__(self)¶
Called to implement
operator.index()
, and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-inbin()
,hex()
andoct()
functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer.If
__int__()
,__float__()
and__complex__()
are not defined then corresponding built-in functionsint()
,float()
andcomplex()
fall back to__index__()
.
- object.__round__(self[, ndigits])¶
- object.__trunc__(self)¶
- object.__floor__(self)¶
- object.__ceil__(self)¶
Called to implement the built-in function
round()
andmath
functionstrunc()
,floor()
andceil()
. Unless ndigits is passed to__round__()
all these methods should return the value of the object truncated to anIntegral
(typically anint
).Changed in version 3.14:
int()
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 with
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 The with statement), 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 Context Manager Types.
The object
class itself does not provide the context manager methods.
- object.__enter__(self)¶
Enter the runtime context related to this object. The
with
statement will bind this method’s return value to the target(s) specified in theas
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
None
.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.
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. case MyClass(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 case MyClass(x, y)
is equivalent to case MyClass(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 TypeError
.
Added in version 3.10.
See also
- PEP 634 - Structural Pattern Matching
The specification for the Python
match
statement.
3.3.11. Emulating buffer types¶
The buffer protocol 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 bytes
and memoryview
,
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
memoryview
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.inspect.BufferFlags
provides a convenient way to interpret the flags. The method must return amemoryview
object.
- object.__release_buffer__(self, buffer)¶
Called when a buffer is no longer needed. The buffer argument is a
memoryview
object that was previously returned by__buffer__()
. The method must release any resources associated with the buffer. This method should returnNone
. Buffer objects that do not need to perform any cleanup are not required to implement this method.
Added in version 3.12.
See also
- PEP 688 - Making the buffer protocol accessible in Python
Introduces the Python
__buffer__
and__release_buffer__
methods.collections.abc.Buffer
ABC for buffer types.
3.3.12. Annotations¶
Functions, classes, and modules may contain annotations, which are a way to associate information (usually type hints) with a symbol.
- object.__annotations__¶
This attribute contains the annotations for an object. It is lazily evaluated, 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 annotate function. 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
annotationlib.Format
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
NotImplementedError
. Annotate functions must always supportVALUE
format; they must not raiseNotImplementedError()
when called with this format.When called with
VALUE
format, an annotate function may raiseNameError
; it must not raiseNameError
when called requesting any other format.If an object does not have any annotations,
__annotate__
should preferably be set toNone
(it can’t be deleted), rather than set to a function that returns an empty dict.Added in version 3.14.
See also
- PEP 649 — Deferred evaluation of annotation using descriptors
Introduces 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:
>>> class C:
... 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 __hash__()
and __repr__()
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 argument
Incorrectly 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)
True
In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses the
__getattribute__()
method even of the object’s metaclass:
>>> class Meta(type):
... def __getattribute__(*args):
... print("Metaclass getattribute invoked")
... return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
... def __len__(self):
... return 10
... def __getattribute__(*args):
... print("Class getattribute invoked")
... return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__() # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c) # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c) # Implicit lookup
10
Bypassing the __getattribute__()
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 awaitable object generally implements an __await__()
method.
Coroutine objects returned from async def
functions
are awaitable.
Note
The generator iterator objects returned from generators
decorated with types.coroutine()
are also awaitable, but they do not implement __await__()
.
- object.__await__(self)¶
Must return an iterator. Should be used to implement awaitable objects. For instance,
asyncio.Future
implements this method to be compatible with theawait
expression. Theobject
class itself is not awaitable and does not provide this method.
Added in version 3.5.
See also
PEP 492 for additional information about awaitable objects.
3.4.2. Coroutine Objects¶
Coroutine objects are awaitable objects.
A coroutine’s execution can be controlled by calling __await__()
and
iterating over the result. When the coroutine has finished executing and
returns, the iterator raises StopIteration
, and the exception’s
value
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 Generator-iterator methods). However, unlike generators, coroutines do not directly support iteration.
Changed in version 3.5.2: It is a RuntimeError
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__await__()
. If value is notNone
, this method delegates to thesend()
method of the iterator that caused the coroutine to suspend. The result (return value,StopIteration
, 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
throw()
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,StopIteration
, or other exception) is the same as when iterating over the__await__()
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
close()
method of the iterator that caused the coroutine to suspend, if it has such a method. Then it raisesGeneratorExit
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 async for
statement.
The object
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
StopAsyncIteration
error when the iteration is over.
An example of an asynchronous iterable object:
class Reader:
async def readline(self):
...
def __aiter__(self):
return self
async def __anext__(self):
val = await self.readline()
if val == b'':
raise StopAsyncIteration
return val
Added in version 3.5.
Changed in version 3.7: Prior to Python 3.7, __aiter__()
could return an awaitable
that would resolve to an
asynchronous iterator.
Starting with Python 3.7, __aiter__()
must return an
asynchronous iterator object. Returning anything else
will result in a TypeError
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 async with
statement.
The object
class itself does not provide these methods.
- object.__aenter__(self)¶
Semantically similar to
__enter__()
, the only difference being that it must return an awaitable.
- object.__aexit__(self, exc_type, exc_value, traceback)¶
Semantically similar to
__exit__()
, the only difference being that it must return an awaitable.
An example of an asynchronous context manager class:
class AsyncContextManager:
async def __aenter__(self):
await log('entering context')
async def __aexit__(self, exc_type, exc, tb):
await log('exiting context')
Added in version 3.5.
Footnotes