3.Data model

3.1.Objects, values and types

Objectsare Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.)

Every object has an identity, a type and a value. An object’sidentitynever changes once it has been created; you may think of it as the object’s address in memory. Theisoperator 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 wherexis 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. Thetype()function returns an object’s type (which is an object itself). Like its identity, an object’stypeis also unchangeable. [1]

Thevalueof some objects can change. Objects whose value can change are said to bemutable;objects whose value is unchangeable once they are created are calledimmutable.(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 thegc 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 atry…exceptstatement 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 aclose()method. Programs are strongly recommended to explicitly close such objects. Thetry…finallystatement and thewithstatement provide convenient ways to do this.

Some objects contain references to other objects; these are calledcontainers. 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, aftera=1;b=1,aandbmay or may not refer to the same object with the value one, depending on the implementation. This is becauseintis an immutable type, so the reference to1 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, afterc=[];d=[],canddare guaranteed to refer to two different, unique, newly created empty lists. (Note thate=f=[]assigns thesameobject to botheandf.)

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 nameNone.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 nameNotImplemented.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:EvaluatingNotImplementedin a boolean context is deprecated. While it currently evaluates as true, it will emit aDeprecationWarning. It will raise aTypeErrorin a future version of Python.

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 valuesFalseandTrueare 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 numberzcan be retrieved through the read-only attributesz.realandz.imag.

3.2.5.Sequences

These represent finite ordered sets indexed by non-negative numbers. The built-in functionlen()returns the number of items of a sequence. When the length of a sequence isn,the index set contains the numbers 0, 1, …,n-1. Itemiof sequenceais selected bya[i].Some sequences, including built-in sequences, interpret negative subscripts by adding the sequence length. For example,a[-2]equalsa[n-2],the second to last item of sequence a with lengthn.

Sequences also support slicing:a[i:j]selects all items with indexksuch thati<=k<j.When used as an expression, a slice is a sequence of the same type. The comment above about negative indexes also applies to negative slice positions.

Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k]selects all items ofawith indexxwherex=i+n*k,n >=0andi<=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 is a sequence of values that represent Unicode code points. All the code points in the rangeU+0000-U+10FFFFcan be represented in a string. Python doesn’t have achartype; instead, every code point in the string is represented as a string object with length1.The built-in functionord() converts a code point from its string form to an integer in the range0-10FFFF;chr()converts an integer in the range 0-10FFFFto the corresponding length1string object. str.encode()can be used to convert astrto bytesusing 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 (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 anddel (delete) statements.

Note

Thecollectionsandarraymodule 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 immutablebytesobjects.

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 functionlen()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.,1and1.0), 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-inset() constructor and can be modified afterwards by several methods, such as 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.

3.2.7.Mappings

These represent finite sets of objects indexed by arbitrary index sets. The subscript notationa[k]selects the item indexed bykfrom the mapping a;this can be used in expressions and as the target of assignments or delstatements. The built-in functionlen()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.,1and1.0) 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 sectionDictionary displays).

The extension modulesdbm.ndbmanddbm.gnuprovide additional examples of mapping types, as does thecollections 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 sectionFunction 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
function.__globals__	A reference to thedictionarythat holds the function’s global variables– the global namespace of the module in which the function was defined.
function.__closure__	Noneor atupleof cells that contain bindings for the function’s free variables. A cell object has the attributecell_contents. This can be used to get the value of the cell, as well as set the value.

3.2.8.1.2.Special writable attributes

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

Attribute	Meaning
function.__doc__	The function’s documentation string, orNoneif unavailable. Not inherited by subclasses.
function.__name__	The function’s name. See also:__name__attributes.
function.__qualname__	The function’squalified name. See also:__qualname__attributes. Added in version 3.3.
function.__module__	The name of the module the function was defined in, orNoneif unavailable.
function.__defaults__	Atuplecontaining defaultparametervalues for those parameters that have defaults, orNoneif no parameters have a default value.
function.__code__	Thecode objectrepresenting the compiled function body.
function.__dict__	The namespace supporting arbitrary function attributes. See also:__dict__attributes.
function.__annotations__	Adictionarycontaining annotations of parameters. The keys of the dictionary are the parameter names, and'return'for the return annotation, if provided. See also:Annotations Best Practices.
function.__kwdefaults__	Adictionarycontaining defaults for keyword-only parameters.
function.__type_params__	Atuplecontaining thetype parametersof ageneric function. 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 functionsmay 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:

method.__self__	Refers to the class instance object to which the method is bound
method.__func__	Refers to the originalfunction object
method.__doc__	The method’s documentation (same asmethod.__func__.__doc__). Astringif the original function had a docstring, else None.
method.__name__	The name of the method (same asmethod.__func__.__name__)
method.__module__	The name of the module the method was defined in, orNoneif unavailable.

Methods also support accessing (but not setting) the arbitrary function attributes on the underlyingfunction 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-definedfunction objector a classmethodobject.

When an instance method object is created by retrieving a user-defined function objectfrom a class via one of its instances, its__self__attribute is the instance, and the method object is said to bebound.The new method’s__func__ attribute is the original function object.

When an instance method object is created by retrieving aclassmethod 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 Cis a class which contains a definition for a function f(),andxis an instance ofC,callingx.f(1)is equivalent to callingC.f(x,1).

When an instance method object is derived from aclassmethodobject, the “class instance” stored in__self__will actually be the class itself, so that calling eitherx.f(1)orC.f(1)is equivalent to callingf(C,1)wherefis 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; thisonlyhappens when the function is an attribute of the class.

3.2.8.3.Generator functions

A function or method which uses theyieldstatement (see section The yield statement) is called agenerator function.Such a function, when called, always returns aniteratorobject 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 theyieldstatement. When the function executes areturnstatement or falls off the end, a StopIterationexception 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 usingasyncdefis called acoroutine function.Such a function, when called, returns a coroutineobject. It may containawaitexpressions, as well asasyncwithandasyncforstatements. See also theCoroutine Objectssection.

3.2.8.5.Asynchronous generator functions

A function or method which is defined usingasyncdefand which uses theyieldstatement is called a asynchronous generator function.Such a function, when called, returns anasynchronous iteratorobject which can be used in an asyncforstatement to execute the body of the function.

Calling the asynchronous iterator’s aiterator.__anext__method will return anawaitablewhich when awaited will execute until it provides a value using theyield expression. When the function executes an emptyreturn statement or falls off the end, aStopAsyncIterationexception 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 arelen()andmath.sin()(mathis 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, orNoneif 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 orNoneif 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 isalist.append(),assumingalistis a list object. In this case, the special read-only attribute__self__is set to the object denoted byalist.(The attribute has the same semantics as it does with otherinstancemethods.)

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 theimport systemas invoked either by the importstatement, or by calling functions such asimportlib.import_module()and built-in __import__().A module object has a namespace implemented by a dictionaryobject (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.xis 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=1is equivalent tom.__dict__[ "x" ]=1.

Predefined (writable) attributes:

__name__

The module’s name.

__doc__

The module’s documentation string, orNoneif unavailable.

__file__

The pathname of the file from which the module was loaded, if it was loaded from a file. The__file__ attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter. For extension modules loaded dynamically from a shared library, it’s the pathname of the shared library file.

__annotations__

A dictionary containing variable annotationscollected during module body execution. For best practices on working with__annotations__,please seeAnnotations Best Practices.

Special read-only attribute:__dict__is the module’s namespace as a dictionary object.

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.xis translated toC.__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 classC,say) would yield a class method object, it is transformed into an instance method object whose __self__attribute isC. When it would yield astaticmethodobject, it is transformed into the object wrapped by the static method object. See sectionImplementing Descriptorsfor 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).

Special attributes:

__name__

The class name.

__module__

The name of the module in which the class was defined.

__dict__

The dictionary containing the class’s namespace.

__bases__

A tuple containing the base classes, in the order of their occurrence in the base class list.

__doc__

The class’s documentation string, orNoneif undefined.

__annotations__

A dictionary containing variable annotations collected during class body execution. For best practices on working with__annotations__,please see Annotations Best Practices.

__type_params__

A tuple containing thetype parametersof ageneric class.

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 sectionImplementing Descriptorsfor 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 sectionSpecial method names.

Special attributes:__dict__is the attribute dictionary; __class__is the instance’s class.

3.2.12.I/O objects (also known as file objects)

Afile objectrepresents an open file. Various shortcuts are available to create file objects: theopen()built-in function, and alsoos.popen(),os.fdopen(),and the makefile()method of socket objects (and perhaps by other functions or methods provided by extension modules).

The objectssys.stdin,sys.stdoutandsys.stderrare 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 theio.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 representbyte-compiledexecutable Python code, orbytecode. The difference between a code object and a function object is that the function object contains an explicit reference to the function’s globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.

3.2.13.1.1.Special read-only attributes

codeobject.co_name	The function name
codeobject.co_qualname	The fully qualified function name Added in version 3.11.
codeobject.co_argcount	The total number of positionalparameters (including positional-only parameters and parameters with default values) that the function has
codeobject.co_posonlyargcount	The number of positional-onlyparameters (including arguments with default values) that the function has
codeobject.co_kwonlyargcount	The number of keyword-onlyparameters (including arguments with default values) that the function has
codeobject.co_nlocals	The number oflocal variablesused by the function (including parameters)
codeobject.co_varnames	Atuplecontaining the names of the local variables in the function (starting with the parameter names)
codeobject.co_cellvars	Atuplecontaining the names oflocal variables that are referenced by nested functions inside the function
codeobject.co_freevars	Atuplecontaining the names of free variables in the function
codeobject.co_code	A string representing the sequence ofbytecodeinstructions in the function
codeobject.co_consts	Atuplecontaining the literals used by thebytecodein the function
codeobject.co_names	Atuplecontaining the names used by thebytecodein the function
codeobject.co_filename	The name of the file from which the code was compiled
codeobject.co_firstlineno	The line number of the first line of the function
codeobject.co_lnotab	A string encoding the mapping frombytecodeoffsets 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 3.14.
codeobject.co_stacksize	The required stack size of the code object
codeobject.co_flags	Anintegerencoding a number of flags for the interpreter.

The following flag bits are defined forco_flags: bit0x04is set if the function uses the*argumentssyntax to accept an arbitrary number of positional arguments; bit0x08is set if the function uses the **keywordssyntax to accept arbitrary keyword arguments; bit0x20is set if the function is a generator. SeeCode Objects Bit Flagsfor details on the semantics of each flags that might be present.

Future feature declarations (from__future__importdivision) also use bits inco_flagsto indicate whether a code object was compiled with a particular feature enabled: bit0x2000is set if the function was compiled with future division enabled; bits0x10and0x1000were used in earlier versions of Python.

Other bits inco_flagsare reserved for internal use.

If a code object represents a function, the first item in co_constsis the documentation string of the function, orNoneif undefined.

3.2.13.1.2.Methods on code objects

codeobject.co_positions()

Returns an iterable over the source code positions of eachbytecode instruction in the code object.

The iterator returnstuples containing the(start_line,end_line, start_column,end_column).Thei-thtuple corresponds to the position of the source code that compiled to thei-thcode 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-Xno_debug_ranges.

• Loading a pyc file compiled while using-Xno_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 -Xno_debug_rangescommand 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 thebytecoderange

• end(anint) represents the offset (exclusive) of the end of thebytecoderange

• linenois anintrepresenting the line number of the bytecoderange, orNoneif the bytecodes in the given range have no line number

The items yielded will have the following properties:

• The first range yielded will have astartof 0.

• The(start,end)ranges will be non-decreasing and consecutive. That is, for any pair oftuples, thestartof the second will be equal to theendof the first.

• No range will be backwards:end>=startfor all triples.

• The lasttupleyielded will haveendequal to the size of the bytecode.

Zero-width ranges, wherestart==end,are allowed. Zero-width ranges are used for lines that are present in the source code, but have been eliminated by thebytecodecompiler.

Added in version 3.10.

See also

PEP 626- Precise line numbers for debugging and other tools.

The PEP that introduced theco_lines()method.

codeobject.replace(**kwargs)

Return a copy of the code object with new values for the specified fields.

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

frame.f_back	Points to the previous stack frame (towards the caller), orNoneif this is the bottom stack frame
frame.f_code	Thecode objectbeing executed in this frame. Accessing this attribute raises anauditing event object.__getattr__with argumentsobjand"f_code".
frame.f_locals	The dictionary used by the frame to look up local variables
frame.f_globals	The dictionary used by the frame to look up global variables
frame.f_builtins	The dictionary used by the frame to look up built-in (intrinsic) names
frame.f_lasti	The “precise instruction” of the frame object (this is an index into thebytecodestring of the code object)

3.2.13.2.2.Special writable attributes

frame.f_trace	If notNone,this is a function called for various events during code execution (this is used by debuggers). Normally an event is triggered for each new source line (seef_trace_lines).
frame.f_trace_lines	Set this attribute toFalseto disable triggering a tracing event for each source line.
frame.f_trace_opcodes	Set this attribute toTrueto allow per-opcode events to be requested. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.
frame.f_lineno	The current line number of the frame – writing to this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to this attribute.

3.2.13.2.3.Frame object methods

Frame objects support one method:

frame.clear()

This method clears all references tolocal variablesheld by the frame. Also, if the frame belonged to agenerator,the generator is finalized. This helps break reference cycles involving frame objects (for example when catching anexception and storing itstracebackfor later use).

RuntimeErroris raised if the frame is currently executing.

Added in version 3.4.

3.2.13.3.Traceback objects

Traceback objects represent the stack trace of anexception. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by callingtypes.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 bysys.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 assys.last_traceback.

For explicitly created tracebacks, it is up to the creator of the traceback to determine how thetb_nextattributes should be linked to form a full stack trace.

Special read-only attributes:

traceback.tb_frame	Points to the executionframeof the current level. Accessing this attribute raises an auditing eventobject.__getattr__with arguments objand"tb_frame".
traceback.tb_lineno	Gives the line number where the exception occurred
traceback.tb_lasti	Indicates the “precise instruction”.

The line number and last instruction in the traceback may differ from the line number of itsframe objectif the exception occurred in a trystatement with no matching except clause or with a finallyclause.

traceback.tb_next

The special writable attributetb_nextis the next level in the stack trace (towards the frame where the exception occurred), orNoneif 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-inslice()function.

Special read-only attributes:startis the lower bound; stopis the upper bound;stepis the step value; each isNoneif omitted. These attributes can have any type.

Slice objects support one method:

slice.indices(self,length)

This method takes a single integer argumentlengthand computes information about the slice that the slice object would describe if applied to a sequence oflengthitems. It returns a tuple of three integers; respectively these are thestartandstopindices and the stepor 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-instaticmethod()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-inclassmethod()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 tooperator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__(), andxis an instance of this class, thenx[i]is roughly equivalent totype(x).__getitem__(x,i).Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeErrororTypeError).

Setting a special method toNoneindicates that the corresponding operation is not available. For example, if a class sets __iter__()toNone,the class is not iterable, so calling iter()on its instances will raise aTypeError(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 theNodeListinterface in the W3C’s Document Object Model.)

3.3.1.Basic customization

object.__new__(cls[,...])

Called to create a new instance of classcls.__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 ofcls).

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 ofcls,then the new instance’s__init__()method will be invoked like__init__(self[,...]),whereselfis the new instance and the remaining arguments are the same as were passed to the object constructor.

If__new__()does not return an instance ofcls,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-Nonevalue may be returned by__init__();doing so will cause aTypeErrorto 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 objectresurrection.It is implementation-dependent whether__del__()is called a second time when a resurrected object is about to be destroyed; the currentCPythonimplementation only calls it once.

It is not guaranteed that__del__()methods are called for objects that still exist when the interpreter exits. weakref.finalizeprovides a straightforward way to register a cleanup function to be called when an object is garbage collected.

Note

delxdoesn’t directly callx.__del__()— the former decrements the reference count forxby one, and the latter is only called when x’s reference count reaches zero.

CPython implementation detail:It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by thecyclic 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 thegcmodule.

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.stderrinstead. 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 therepr()built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form<...someusefuldescription...>should be returned. The return value must be a string object. If a class defines__repr__() but not__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.

object.__str__(self)

Called bystr(object)and the built-in functions format()andprint()to compute the “informal” or nicely printable string representation of an object. The return value must be a stringobject.

This method differs fromobject.__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 typeobject callsobject.__repr__().

object.__bytes__(self)

Called bybytesto compute a byte-string representation of an object. This should return abytesobject.

object.__format__(self,format_spec)

Called by theformat()built-in function, and by extension, evaluation offormatted string literalsand thestr.format()method, to produce a “formatted” string representation of an object. Theformat_specargument is a string that contains a description of the formatting options desired. The interpretation of theformat_specargument 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.

SeeFormat Specification Mini-Languagefor a description of the standard formatting syntax.

The return value must be a string object.

Changed in version 3.4:The __format__ method ofobjectitself 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<ycallsx.__lt__(y), x<=ycallsx.__le__(y),x==ycallsx.__eq__(y),x!=ycalls x.__ne__(y),x>ycallsx.__gt__(y),andx>=ycalls x.__ge__(y).

A rich comparison method may return the singletonNotImplementedif it does not implement the operation for a given pair of arguments. By convention, FalseandTrueare 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 anifstatement), Python will call bool()on the value to determine if the result is true or false.

By default,objectimplements__eq__()by usingis,returning NotImplementedin the case of a false comparison: TrueifxisyelseNotImplemented.For__ne__(),by default it delegates to__eq__()and inverts the result unless it is NotImplemented.There are no other implied relationships among the comparison operators or default implementations; for example, the truth of (x<yorx==y)does not implyx<=y.To automatically generate ordering operations from a single root operation, seefunctools.total_ordering().

See the paragraph on__hash__()for some important notes on creatinghashableobjects 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 thanNotImplemented,the ==and!=operators will fall back toisandisnot,respectively.

object.__hash__(self)

Called by built-in functionhash()and for operations on members of hashed collections includingset,frozenset,and dict.The__hash__()method should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:

def__hash__(self):
returnhash((self.name,self.nick,self.color))

Note

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 with python-c"importsys;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 ofhashablecollections 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; with them, all objects compare unequal (except with themselves) andx.__hash__()returns an appropriate value such thatx==y implies both thatxisyandhash(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 appropriateTypeErrorwhen 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__=Nonein the class definition. A class which defines its own__hash__()that explicitly raises aTypeErrorwould 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.htmlfor 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 alsoPYTHONHASHSEED.

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 returnFalseorTrue.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__(),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 ofx.name) for class instances.

object.__getattr__(self,name)

Called when the default attribute access fails with anAttributeError (either__getattribute__()raises anAttributeErrorbecause nameis not an instance attribute or an attribute in the class tree forself;or__get__()of anameproperty raises AttributeError). This method should either return the (computed) attribute value or raise anAttributeErrorexception.

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 fake 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 an AttributeError.This method should return the (computed) attribute value or raise anAttributeErrorexception. 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. SeeSpecial method lookup.

For certain sensitive attribute accesses, raises an auditing eventobject.__getattr__with arguments objandname.

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). nameis the attribute name,valueis 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 eventobject.__setattr__with arguments obj,name,value.

object.__delattr__(self,name)

Like__setattr__()but for attribute deletion instead of assignment. This should only be implemented ifdelobj.nameis meaningful for the object.

For certain sensitive attribute deletions, raises an auditing eventobject.__delattr__with arguments objandname.

object.__dir__(self)

Called whendir()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 anAttributeError.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 anAttributeError.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 standarddir()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 oftypes.ModuleType.For example:

importsys
fromtypesimportModuleType

classVerboseModule(ModuleType):
def__repr__(self):
returnf'Verbose{self.__name__}'

def__setattr__(self,attr,value):
print(f'Setting{attr}...')
super().__setattr__(attr,value)

sys.modules[__name__].__class__=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-calleddescriptorclass) appears in anownerclass (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__.

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 ownerargument is the owner class, whileinstanceis the instance that the attribute was accessed through, orNonewhen the attribute is accessed through theowner.

This method should return the computed attribute value or raise an AttributeErrorexception.

PEP 252specifies 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 instanceinstanceof the owner class to a new value,value.

Note, adding__set__()or__delete__()changes the kind of descriptor to a “data descriptor”. SeeInvoking Descriptorsfor more details.

object.__delete__(self,instance)

Called to delete the attribute on an instanceinstanceof the owner class.

Instances of descriptors may also have the__objclass__attribute present:

object.__objclass__

The attribute__objclass__is interpreted by theinspectmodule 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.xhas a lookup chain starting witha.__dict__['x'],thentype(a).__dict__['x'],and continuing through the base classes oftype(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 ona:

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.xis transformed into the call: type(a).__dict__['x'].__get__(a,type(a)).

Class Binding

If binding to a class,A.xis transformed into the call: A.__dict__['x'].__get__(None,A).

Super Binding

A dotted lookup such assuper(A,a).xsearches a.__class__.__mro__for a base classBfollowingAand then returnsB.__dict__['x'].__get__(a,A).If not a descriptor,xis 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 @staticmethodand@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.

Theproperty()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 supportweakreferencesto 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 creatingdescriptors 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, child subclasses will get a__dict__and __weakref__unless they also define__slots__(which should only contain names of anyadditionalslots).

• 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.

• TypeErrorwill be raised if nonempty__slots__are defined for a class derived from a "variable-length"built-intypesuch as int,bytes,andtuple.

• Any non-stringiterablemay be assigned to__slots__.

• If adictionaryis 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 ofhelp().

• __class__assignment works only if both classes have the same__slots__.

• Multiple inheritancewith 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 aniteratoris used for__slots__then adescriptoris 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.

classmethodobject.__init_subclass__(cls)

This method is called whenever the containing class is subclassed. clsis then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method.

Keyword arguments which are given to a new class are passed to the parent class’s__init_subclass__.For compatibility with other classes using__init_subclass__,one should take out the needed keyword arguments and pass the others over to the base class, as in:

classPhilosopher:
def__init_subclass__(cls,/,default_name,**kwargs):
super().__init_subclass__(**kwargs)
cls.default_name=default_name

classAustralianPhilosopher(Philosopher,default_name="Bruce"):
pass

The default implementationobject.__init_subclass__does nothing, but raises an error if it is called with any arguments.

Note

The metaclass hintmetaclassis consumed by the rest of the type machinery, and is never passed to__init_subclass__implementations. The actual metaclass (rather than the explicit hint) can be accessed as type(cls).

Added in version 3.6.

When a class is created,type.__new__()scans the class variables and makes callbacks to those with a__set_name__()hook.

object.__set_name__(self,owner,name)

Automatically called at the time the owning classowneris created. The object has been assigned tonamein that class:

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

If the class variable is assigned after the class is created, __set_name__()will not be called automatically. If needed,__set_name__()can be called directly:

classA:
pass

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

SeeCreating the class objectfor more details.

Added in version 3.6.

3.3.3.1.Metaclasses

By default, classes are constructed usingtype().The class body is executed in a new namespace and the class name is bound locally to the result oftype(name,bases,namespace).

The class creation process can be customized by passing themetaclass keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, bothMyClassandMySubclassare instances ofMeta:

classMeta(type):
pass

classMyClass(metaclass=Meta):
pass

classMySubclass(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 thebasesparameter, 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 oftype.

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, thentype()is used;

• if an explicit metaclass is given and it isnotan instance of type(),then it is used directly as the metaclass;

• if an instance oftype()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 ofall of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail withTypeError.

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 asnamespace=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 newdict.

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 toexec()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 ofsuper().__class__is an implicit closure reference created by the compiler if any methods in a class body refer to either __class__orsuper.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 thetype.__new__call in order for the class to be initialised correctly. Failing to do so will result in aRuntimeErrorin Python 3.8.

When using the default metaclasstype,or any metaclass that ultimately callstype.__new__,the following additional customization steps are invoked after creating the class object:

1. Thetype.__new__method collects all of the attributes in the class namespace that define a__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__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 bytype.__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()andissubclass()built-in functions.

In particular, the metaclassabc.ABCMetaimplements 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.

class.__instancecheck__(self,instance)

Return true ifinstanceshould be considered a (direct or indirect) instance ofclass.If defined, called to implementisinstance(instance, class).

class.__subclasscheck__(self,subclass)

Return true ifsubclassshould be considered a (direct or indirect) subclass ofclass.If defined, called to implementissubclass(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 customizingisinstance()and issubclass()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 usingtype annotations,it is often useful to parameterizeageneric typeusing Python’s square-brackets notation. For example, the annotationlist[int]might be used to signify a listin which all the elements are of typeint.

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 genericsandtyping.Generic

Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.

A class cangenerallyonly be parameterized if it defines the special class method__class_getitem__().

classmethodobject.__class_getitem__(cls,key)

Return an object representing the specialization of a generic class by type arguments found inkey.

When defined on a class,__class_getitem__()is automatically a class method. As such, there is no need for it to be decorated with @classmethodwhen 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 applytype hintsto 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 fromtyping.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, thesubscriptionof 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 aGenericAlias object if it is properly defined.

Presented with theexpressionobj[x],the Python interpreter follows something like the following process to decide whether __getitem__()or__class_getitem__()should be called:

frominspectimportisclass

defsubscribe(obj,x):
"""Return the result of the expression 'obj[x]'" ""

class_of_obj=type(obj)

# If the class of obj defines __getitem__,
# call class_of_obj.__getitem__(obj, x)
ifhasattr(class_of_obj,'__getitem__'):
returnclass_of_obj.__getitem__(obj,x)

# Else, if obj is a class and defines __class_getitem__,
# call obj.__class_getitem__(x)
elifisclass(obj)andhasattr(obj,'__class_getitem__'):
returnobj.__class_getitem__(x)

# Else, raise an exception
else:
raiseTypeError(
f"'{class_of_obj.__name__}' object is not subscriptable "
)

In Python, all classes are themselves instances of other classes. The class of a class is known as that class’smetaclass,and most classes have the typeclass as their metaclass.typedoes not define __getitem__(),meaning that expressions such aslist[int], dict[str,float]andtuple[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 theenummodule:

>>>fromenumimportEnum
>>>classMenu(Enum):
..."""A breakfast menu" ""
...SPAM='spam'
...BACON='bacon'
...
>>># Enum classes have a custom metaclass:
>>>type(Menu)
<class 'enum.EnumMeta'>
>>># EnumMeta defines __getitem__,
>>># so __class_getitem__ is not called,
>>># and the result is not a GenericAlias object:
>>>Menu['SPAM']
<Menu.SPAM: 'spam'>
>>>type(Menu['SPAM'])
<enum 'Menu'>

See also

PEP 560- Core Support for typing module and generic types

Introducing__class_getitem__(),and outlining when a subscriptionresults in__class_getitem__() being called instead of__getitem__()

3.3.6.Emulating callable objects

object.__call__(self[,args...])

Called when the instance is “called” as a function; if this method is defined, x(arg1,arg2,...)roughly translates totype(x).__call__(x,arg1,...).

3.3.7.Emulating container types

The following methods can be defined to implement container objects. Containers usually aresequences(such aslistsor tuples) ormappings(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 integerskfor which0<=k< NwhereNis the length of the sequence, orsliceobjects, 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 standarddictionary objects. Thecollections.abcmodule provides a MutableMapping abstract base classto help create those methods from a base set of __getitem__(),__setitem__(), __delitem__(),andkeys(). Mutable sequences should provide methodsappend(),count(), index(),extend(),insert(),pop(),remove(), reverse()andsort(),like Python standardlist 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 thein operator; for mappings,inshould 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 functionlen().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 mostsys.maxsize. If the length is larger thansys.maxsizesome features (such as len()) may raiseOverflowError.To prevent raising OverflowErrorby truth value testing, an object must define a __bool__()method.

object.__length_hint__(self)

Called to implementoperator.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 be NotImplemented,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 withNone.

object.__getitem__(self,key)

Called to implement evaluation ofself[key].Forsequencetypes, the accepted keys should be integers. Optionally, they may support sliceobjects as well. Negative index support is also optional. Ifkeyis of an inappropriate type,TypeErrormay be raised; ifkeyis a value outside the set of indexes for the sequence (after any special interpretation of negative values),IndexErrorshould be raised. For mappingtypes, ifkeyis missing (not in the container), KeyErrorshould be raised.

Note

forloops expect that anIndexErrorwill be raised for illegal indexes to allow proper detection of the end of the sequence.

Note

Whensubscriptingaclass,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 toself[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 improperkeyvalues as for the__getitem__()method.

object.__delitem__(self,key)

Called to implement deletion ofself[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 improperkey values as for the__getitem__()method.

object.__missing__(self,key)

Called bydict.__getitem__()to implementself[key]for dict subclasses when key is not in the dictionary.

object.__iter__(self)

This method is called when aniteratoris 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 thereversed()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 (inandnotin) 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 ifitem is inself,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__(),seethis 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,wherexis 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 ternary 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 returnNotImplemented.

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 left operand does not support the corresponding operation[3]and the operands are of different types.[4]For instance, to evaluate the expressionx-y,whereyis an instance of a class that has an__rsub__()method, type(y).__rsub__(y,x)is called iftype(x).__sub__(x,y)returns NotImplemented.

Note that ternarypow()will not try calling__rpow__()(the coercion rules would become too complicated).

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 (modifyingself) 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, ifx is an instance of a class with an__iadd__()method,x+=yis equivalent tox=x.__iadd__(y).If__iadd__()does not exist, or ifx.__iadd__(y) returnsNotImplemented,x.__add__(y)and y.__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 functionscomplex(), int()andfloat().Should return a value of the appropriate type.

object.__index__(self)

Called to implementoperator.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 functionround()andmath functionstrunc(),floor()andceil(). Unlessndigitsis passed to__round__()all these methods should return the value of the object truncated to anIntegral (typically anint).

The built-in functionint()falls back to__trunc__()if neither __int__()nor__index__()is defined.

Changed in version 3.11:The delegation ofint()to__trunc__()is deprecated.

3.3.9.With Statement Context Managers

Acontext manageris an object that defines the runtime context to be established when executing awithstatement. 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 withstatement (described in sectionThe 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, seeContext Manager Types.

object.__enter__(self)

Enter the runtime context related to this object. Thewithstatement will bind this method’s return value to the target(s) specified in the asclause 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 beNone.

If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.

Note that__exit__()methods should not reraise the passed-in exception; this is the caller’s responsibility.

See also

PEP 343- The “with” statement

The specification, background, and examples for the Pythonwith statement.

3.3.10.Customizing positional arguments in class pattern matching

When using a class name in a pattern, positional arguments in the pattern are not allowed by default, i.e.caseMyClass(x,y)is typically invalid without special support inMyClass.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, ifMyClass.__match_args__is( "left","center","right" )that means thatcaseMyClass(x,y)is equivalent tocaseMyClass(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 aTypeError.

Added in version 3.10.

See also

PEP 634- Structural Pattern Matching

The specification for the Pythonmatchstatement.

3.3.11.Emulating buffer types

Thebuffer protocolprovides a way for Python objects to expose efficient access to a low-level memory array. This protocol is implemented by builtin types such asbytesandmemoryview, 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 fromself(for example, by the memoryviewconstructor). Theflagsargument 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 amemoryviewobject.

object.__release_buffer__(self,buffer)

Called when a buffer is no longer needed. Thebufferargument is a memoryviewobject 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.Special method lookup

For custom classes, implicit invocations of special methods are only guaranteed to work correctly if defined on an object’s type, not in the object’s instance dictionary. That behaviour is the reason why the following code raises an exception:

>>>classC:
...pass
...
>>>c=C()
>>>c.__len__=lambda:5
>>>len(c)
Traceback (most recent call last):
File"<stdin>",line1,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>",line1,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:

>>>classMeta(type):
...def__getattribute__(*args):
...print("Metaclass getattribute invoked")
...returntype.__getattribute__(*args)
...
>>>classC(object,metaclass=Meta):
...def__len__(self):
...return10
...def__getattribute__(*args):
...print("Class getattribute invoked")
...returnobject.__getattribute__(*args)
...
>>>c=C()
>>>c.__len__()# Explicit lookup via 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 methodmustbe set on the class object itself in order to be consistently invoked by the interpreter).

3.4.Coroutines

3.4.1.Awaitable Objects

Anawaitableobject generally implements an__await__()method. Coroutine objectsreturned fromasyncdeffunctions are awaitable.

Note

Thegenerator iteratorobjects returned from generators decorated withtypes.coroutine() are also awaitable, but they do not implement__await__().

object.__await__(self)

Must return aniterator.Should be used to implement awaitableobjects. For instance,asyncio.Futureimplements this method to be compatible with theawaitexpression.

Note

The language doesn’t place any restriction on the type or value of the objects yielded by the iterator returned by__await__,as this is specific to the implementation of the asynchronous execution framework (e.g.asyncio) that will be managing theawaitableobject.

Added in version 3.5.

See also

PEP 492for additional information about awaitable objects.

3.4.2.Coroutine Objects

Coroutine objectsareawaitableobjects. 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 raisesStopIteration,and the exception’s valueattribute holds the return value. If the coroutine raises an exception, it is propagated by the iterator. Coroutines should not directly raise unhandledStopIterationexceptions.

Coroutines also have the methods listed below, which are analogous to those of generators (seeGenerator-iterator methods). However, unlike generators, coroutines do not directly support iteration.

Changed in version 3.5.2:It is aRuntimeErrorto await on a coroutine more than once.

coroutine.send(value)

Starts or resumes execution of the coroutine. IfvalueisNone, this is equivalent to advancing the iterator returned by __await__().Ifvalueis 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 thethrow()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 theclose() method of the iterator that caused the coroutine to suspend, if it has such a method. Then it raisesGeneratorExitat 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

Anasynchronous iteratorcan call asynchronous code in its__anext__method.

Asynchronous iterators can be used in anasyncforstatement.

object.__aiter__(self)

Must return anasynchronous iteratorobject.

object.__anext__(self)

Must return anawaitableresulting in a next value of the iterator. Should raise aStopAsyncIterationerror when the iteration is over.

An example of an asynchronous iterable object:

classReader:
asyncdefreadline(self):
...

def__aiter__(self):
returnself

asyncdef__anext__(self):
val=awaitself.readline()
ifval==b'':
raiseStopAsyncIteration
returnval

Added in version 3.5.

Changed in version 3.7:Prior to Python 3.7,__aiter__()could return anawaitable 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 aTypeErrorerror.

3.4.4.Asynchronous Context Managers

Anasynchronous context manageris acontext managerthat is able to suspend execution in its__aenter__and__aexit__methods.

Asynchronous context managers can be used in anasyncwithstatement.

object.__aenter__(self)

Semantically similar to__enter__(),the only difference being that it must return anawaitable.

object.__aexit__(self,exc_type,exc_value,traceback)

Semantically similar to__exit__(),the only difference being that it must return anawaitable.

An example of an asynchronous context manager class:

classAsyncContextManager:
asyncdef__aenter__(self):
awaitlog('entering context')

asyncdef__aexit__(self,exc_type,exc,tb):
awaitlog('exiting context')

Added in version 3.5.

Footnotes

[1]

Itispossible 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__hash__(),__iter__(), __reversed__(),and__contains__()methods have special handling for this; others will still raise aTypeError,but may do so by relying on the behavior thatNoneis not callable.

[3]

“Does not support” here means that the class has no such method, or the method returnsNotImplemented.Do not set the method to Noneif you want to force fallback to the right operand’s reflected method—that will instead have the opposite effect of explicitly blockingsuch fallback.

[4]

For operands of the same type, it is assumed that if the non-reflected method – such as__add__()– fails then the overall operation is not supported, which is why the reflected method is not called.