Data model

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.

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.

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.)

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.

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.

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.

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.

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 ~object.__repr__ and ~object.__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:

numbers.Integral

These represent elements from the mathematical set of integers (positive and negative).

[!NOTE] .. index:: pair: integer; representation

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 and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.

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.

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.

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:

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 range 0 to 0x10FFFF can be represented in a string.

Python doesn't have a dedicated character type. Instead, every code point in the string is represented as a string object with length 1.

The built-in function ord converts a code point from its string form to an integer in the range 0 to 0x10FFFF; chr converts an integer in the range 0 to 0x10FFFF to the corresponding length 1 string object. str.encode can be used to convert a str to bytes using the given text encoding, and bytes.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 (like b'abc') and the built-in bytes constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the ~bytes.decode method.

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] .. index:: pair: module; array .. index:: pair: module; collections

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 immutable bytes objects.

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 as ~set.add.

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.

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 are two intrinsic mapping types:

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 dict).

The extension modules dbm.ndbm and dbm.gnu provide additional examples of mapping types, as does the collections module.

Frozen dictionaries

These represent an immutable dictionary. They are created by the built-in frozendict constructor. A frozendict is hashable if all of its keys and values are hashable, in which case it can be used as an element of a set, or as a key in another mapping. !frozendict is not a subclass of dict; it inherits directly from object.

Callable types

These are the types to which the function call operation (see section calls) can be applied:

User-defined functions

A user-defined function object is created by a function definition (see section function). It should be called with an argument list containing the same number of items as the function's formal parameter list.

Special read-only attributes

Special writable attributes

Most of these attributes check the type of the assigned value:

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.

Additional information about a function's definition can be retrieved from its code object <code-objects> (accessible via the ~function.__code__ attribute).

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:

Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object <user-defined-funcs>.

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 <user-defined-funcs> or a classmethod object.

When an instance method object is created by retrieving a classmethod object from a class or instance, its ~method.__self__ attribute is the class itself, and its ~method.__func__ attribute is the function object underlying the class method.

When an instance method object is called, the underlying function (~method.__func__) is called, inserting the class instance (~method.__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 ~method.__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.

Generator functions

A function or method which contains a yield expression (see section yieldexpr) 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 expression. 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.

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.

Asynchronous generator functions

A function or method which is defined using async def and which contains a yield expression 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__ <object.__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.

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, or None if unavailable. See function.__doc__.
• !__name__ is the function's name. See function.__name__.
• !__self__ is the module object the function was defined in or None if unavailable.
• !__module__ is the name of the module the function was defined in or None if unavailable. See function.__module__.

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 <method.__self__>.)

Classes

Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override ~object.__new__. The arguments of the call are passed to !__new__ and, in the typical case, to ~object.__init__ to initialize the new instance.

Class Instances

Instances of arbitrary classes can be made callable by defining a ~object.__call__ method in their class.

Modules

Modules are a basic organizational unit of Python code, and are created by the import system <importsystem> 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 <dict> object (this is the dictionary referenced by the ~function.__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.

Import-related attributes on module objects

Module objects have the following attributes that relate to the import system <importsystem>. When a module is created using the machinery associated with the import system, these attributes are filled in based on the module's spec <module spec>, before the loader executes and loads the module.

To create a module dynamically rather than using the import system, it's recommended to use importlib.util.module_from_spec, which will set the various import-controlled attributes to appropriate values. It's also possible to use the types.ModuleType constructor to create modules directly, but this technique is more error-prone, as most attributes must be manually set on the module object after it has been created when using this approach.

[!CAUTION] With the exception of ~module.__name__, it is strongly recommended that you rely on ~module.__spec__ and its attributes instead of any of the other individual attributes listed in this subsection. Note that updating an attribute on !__spec__ will not update the corresponding attribute on the module itself:

>>> import typing >>> typing.__name__, typing.__spec__.name ('typing', 'typing') >>> typing.__spec__.name = 'spelling' >>> typing.__name__, typing.__spec__.name ('typing', 'spelling') >>> typing.__name__ = 'keyboard_smashing' >>> typing.__name__, typing.__spec__.name ('keyboard_smashing', 'spelling')

Other writable attributes on module objects

As well as the import-related attributes listed above, module objects also have the following writable attributes:

Module dictionaries

Module objects also have the following special read-only attribute:

Custom classes

Custom class types are typically created by class definitions (see section class). 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 python_2.3_mro.

When a class attribute reference (for class !C, say) would yield a class method object, it is transformed into an instance method object whose ~method.__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 descriptors for another way in which attributes retrieved from a class may differ from those actually contained in its ~object.__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).

Special attributes

Special methods

In addition to the special attributes described above, all Python classes also have the following two methods available:

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 ~method.__self__ attribute is the instance. Static method and class method objects are also transformed; see above under "Classes". See section 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 ~object.__dict__. If no class attribute is found, and the object's class has a ~object.__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 ~object.__setattr__ or ~object.__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 specialnames.

Special attributes

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 ~socket.socket.makefile method of socket objects (and perhaps by other functions or methods provided by extension modules).

File objects implement common methods, listed below, to simplify usage in generic code. They are expected to be context-managers.

The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter's standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.

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.

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.

Special read-only attributes

The following flag bits are defined for ~codeobject.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 inspect-module-co-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 ~codeobject.co_flags to indicate whether a code object was compiled with a particular feature enabled. See ~__future__._Feature.compiler_flag.

Other bits in ~codeobject.co_flags are reserved for internal use.

If a code object represents a function and has a docstring, the ~inspect.CO_HAS_DOCSTRING bit is set in ~codeobject.co_flags and the first item in ~codeobject.co_consts is the docstring of the function.

Methods on code objects

Frame objects

Frame objects represent execution frames. They may occur in traceback objects <traceback-objects>, and are also passed to registered trace functions.

Special read-only attributes

Special writable attributes

Frame object methods

Frame objects support one method:

Traceback objects

Traceback objects represent the stack trace of an exception <tut-errors>. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling types.TracebackType.

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 try.) It is accessible as the third item of the tuple returned by sys.exc_info, and as the ~BaseException.__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 ~traceback.tb_next attributes should be linked to form a full stack trace.

Special read-only attributes:

The line number and last instruction in the traceback may differ from the line number of its frame object <frame-objects> if the exception occurred in a try statement with no matching except clause or with a finally clause.

Slice objects

Slice objects are used to represent slices for ~object.__getitem__ methods. They are also created by the built-in slice function.

Special read-only attributes: ~slice.start is the lower bound; ~slice.stop is the upper bound; ~slice.step is the step value; each is None if omitted. These attributes can have any type.

Slice objects support one method:

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.

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" <instance-methods>. Class method objects are created by the built-in classmethod constructor.

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 ~object.__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 ~object.__iter__ to None, the class is not iterable, so calling iter on its instances will raise a TypeError (without falling back to ~object.__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 <dom-nodelist-objects> interface in the W3C's Document Object Model.)

Basic customization

single: comparisons

These are the so-called "rich comparison" methods. The correspondence between operator symbols and method names is as follows: x<y calls x.__lt__(y), x<=y calls x.__le__(y), x==y calls x.__eq__(y), x!=y calls x.__ne__(y), x>y calls x.__gt__(y), and x>=y calls x.__ge__(y).

A rich comparison method may return the singleton NotImplemented if it does not implement the operation for a given pair of arguments. By convention, False and True are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an if statement), Python will call bool on the value to determine if the result is true or false.

By default, object implements __eq__ by using is, returning NotImplemented 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 is !NotImplemented. 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 imply x<=y. To automatically generate ordering operations from a single root operation, see functools.total_ordering.

By default, the object class provides implementations consistent with expressions-value-comparisons: equality compares according to object identity, and order comparisons raise TypeError. Each default method may generate these results directly, but may also return NotImplemented.

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 to is and is not, respectively.

def __hash__(self):
    return hash((self.name, self.nick, self.color))

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.

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.

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.

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' ~object.__dict__. The object class itself does not implement any of these protocols.

Instances of descriptors may also have the !__objclass__ attribute present:

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: ~object.__get__, ~object.__set__, and ~object.__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 searches a.__class__.__mro__ for a base class B following A and then returns B.__dict__['x'].__get__(a, A). If not a descriptor, x is returned unchanged.

For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of ~object.__get__, ~object.__set__ and ~object.__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 decorator is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property.

__slots__

__slots__ allow us to explicitly declare data members (like properties) and deny the creation of ~object.__dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)

The space saved over using ~object.__dict__ can be significant. Attribute lookup speed can be significantly improved as well.

• When inheriting from a class without __slots__, the ~object.__dict__ and __weakref__ attribute of the instances will always be accessible.
• Without a ~object.__dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises AttributeError. 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 <weakref> 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 <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 ~object.__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 __slots__ other than __dict__ and __weakref__ are defined for a class derived from a "variable-length" built-in type <PyTypeObject.tp_itemsize> such as int, bytes, and type, except tuple.
• Any non-string iterable may be assigned to __slots__.
• If a dictionary <dict> is used to assign __slots__, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by inspect.getdoc and displayed in the output of help.
• ~object.__class__ assignment works only if both classes have the same __slots__.
• Multiple inheritance <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.

Customizing class creation

Whenever a class inherits from another class, ~object.__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.

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

When a class is created, !type.__new__ scans the class variables and makes callbacks to those with a ~object.__set_name__ hook.

class A:
    x = C()  # Automatically calls: x.__set_name__(A, 'x')

class A:
   pass

c = C()
A.x = c                  # The hook is not called
c.__set_name__(A, 'x')   # Manually invoke the hook

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.

Resolving MRO entries

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.

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 <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.

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.

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.

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:

1. The type.__new__ method collects all of the attributes in the class namespace that define a ~object.__set_name__ method;
2. Those __set_name__ methods are called with the class being defined and the assigned name of that particular attribute;
3. The ~object.__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 ~type.__dict__ attribute of the class object.

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.

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.

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.

Emulating generic types

When using type annotations<annotation>, 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.

A class can generally only be parameterized if it defines the special class method __class_getitem__().

The purpose of __class_getitem__

The purpose of ~object.__class_getitem__ is to allow runtime parameterization of standard-library generic classes in order to more easily apply type hints<type hint> 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 ~object.__class_getitem__, or inherit from typing.Generic, which has its own implementation of __class_getitem__().

Custom implementations of ~object.__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.

__class_getitem__ versus __getitem__

Usually, the subscription<subscriptions> of an object using square brackets will call the ~object.__getitem__ instance method defined on the object's class. However, if the object being subscribed is itself a class, the class method ~object.__class_getitem__ may be called instead. __class_getitem__() should return a GenericAlias<types-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 ~object.__getitem__ or ~object.__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 ~object.__getitem__, meaning that expressions such as list[int], dict[str, float] and tuple[str, bytes] all result in ~object.__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 ~object.__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'>

Emulating callable objects

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 <sequence> (such as lists <list> or tuples <tuple>) or mappings <mapping> (like dictionaries <dictionary>), 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 <dict> objects. The collections.abc module provides a ~collections.abc.MutableMapping abstract base class to help create those methods from a base set of ~object.__getitem__, ~object.__setitem__, ~object.__delitem__, and !keys.

Mutable sequences should provide methods ~sequence.append, ~sequence.clear, ~sequence.count, ~sequence.extend, ~sequence.index, ~sequence.insert, ~sequence.pop, ~sequence.remove, and ~sequence.reverse, like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods ~object.__add__, ~object.__radd__, ~object.__iadd__, ~object.__mul__, ~object.__rmul__ and ~object.__imul__ described below; they should not define other numerical operators.

It is recommended that both mappings and sequences implement the ~object.__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 ~object.__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.

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.

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.

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 with), 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 typecontextmanager. The object class itself does not provide the context manager methods.

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.

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.

Emulating buffer types

The buffer protocol <bufferobjects> 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.

Annotations

Functions, classes, and modules may contain annotations <annotation>, which are a way to associate information (usually type hints <type hint>) with a symbol.

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 ~object.__hash__ and ~object.__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 ~object.__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 ~object.__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).

Coroutines

Awaitable Objects

An awaitable object generally implements an ~object.__await__ method. Coroutine objects <coroutine> 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 ~object.__await__.

Coroutine Objects

Coroutine objects <coroutine> are awaitable objects. A coroutine's execution can be controlled by calling ~object.__await__ and iterating over the result. When the coroutine has finished executing and returns, the iterator raises StopIteration, and the exception's ~StopIteration.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-methods). However, unlike generators, coroutines do not directly support iteration.

Coroutines are generic <generics> over the types of their yield, send, and return values, respectively.

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.

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

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.

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')

Footnotes

[^1]: It is possible in some cases to change an object's type, under certain controlled conditions. It generally isn't a good idea though, since it can lead to some very strange behaviour if it is handled incorrectly.

[^2]: The ~object.__hash__, ~object.__iter__, ~object.__reversed__, ~object.__contains__, ~object.__class_getitem__ and ~os.PathLike.__fspath__ methods have special handling for this. Others will still raise a TypeError, but may do so by relying on the behavior that None is not callable.

[^3]: "Does not support" here means that the class has no such method, or the method returns NotImplemented. Do not set the method to None if you want to force fallback to the right operand's reflected method—that will instead have the opposite effect of explicitly blocking such fallback.

[^4]: For operands of the same type, it is assumed that if the non-reflected method (such as ~object.__add__) fails then the operation is not supported, which is why the reflected method is not called.

[^5]: If the right operand's type is a subclass of the left operand's type, the reflected method having precedence allows subclasses to override their ancestors' operations.