3.1. Objects, values and types¶

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

Every object has an identity, a type and a value. An object’sidentity neverchanges once it has been created; you may think of it as the object’s address inmemory. The ‘is’ operator compares the identity of two objects; theid() function returns an integer representing its identity.

An object’s type determines the operations that the object supports (e.g., “doesit have a length?”) and also defines the possible values for objects of thattype. Thetype() function returns an object’s type (which is an objectitself). Like its identity, an object’stype is also unchangeable.[1]

Thevalue of some objects can change. Objects whose value canchange are said to bemutable; objects whose value is unchangeable once theyare created are calledimmutable. (The value of an immutable container objectthat contains a reference to a mutable object can change when the latter’s valueis changed; however the container is still considered immutable, because thecollection of objects it contains cannot be changed. So, immutability is notstrictly the same as having an unchangeable value, it is more subtle.) Anobject’s mutability is determined by its type; for instance, numbers, stringsand tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachablethey may be garbage-collected. An implementation is allowed to postpone garbagecollection or omit it altogether — it is a matter of implementation qualityhow garbage collection is implemented, as long as no objects are collected thatare still reachable.

Note that the use of the implementation’s tracing or debugging facilities maykeep objects alive that would normally be collectable. Also note that catchingan exception with a ‘try…except’ statement may keepobjects alive.

Some objects contain references to “external” resources such as open files orwindows. It is understood that these resources are freed when the object isgarbage-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 explicitlyclose such objects. The ‘try…finally’ statementand the ‘with’ statement 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 arepart of a container’s value. In most cases, when we talk about the value of acontainer, we imply the values, not the identities of the contained objects;however, when we talk about the mutability of a container, only the identitiesof the immediately contained objects are implied. So, if an immutable container(like a tuple) contains a reference to a mutable object, its value changes ifthat mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance ofobject identity is affected in some sense: for immutable types, operations thatcompute new values may actually return a reference to any existing object withthe same type and value, while for mutable objects this is not allowed. E.g.,aftera=1;b=1,a andb may or may not refer to the same objectwith the value one, depending on the implementation, but afterc=[];d=[],c andd are guaranteed to refer to two different, unique, newlycreated empty lists. (Note thatc=d=[] assigns the same object to bothc andd.)

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) candefine additional types. Future versions of Python may add types to the typehierarchy (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 ‘specialattributes.’ These are attributes that provide access to the implementation andare not intended for general use. Their definition may change in the future.

None

This type has a single value. There is a single object with this value. Thisobject is accessed through the built-in nameNone. It is used to signify theabsence of a value in many situations, e.g., it is returned from functions thatdon’t explicitly return anything. Its truth value is false.

NotImplemented

This type has a single value. There is a single object with this value. Thisobject is accessed through the built-in nameNotImplemented. Numeric methodsand rich comparison methods should return this value if they do not implement theoperation for the operands provided. (The interpreter will then try thereflected operation, or some other fallback, depending on the operator.) Itstruth value is true.

SeeImplementing the arithmetic operationsfor more details.

Ellipsis

This type has a single value. There is a single object with this value. Thisobject is accessed through the literal... or the built-in nameEllipsis. Its truth value is true.

numbers.Number

These are created by numeric literals and returned as results by arithmeticoperators and arithmetic built-in functions. Numeric objects are immutable;once created their value never changes. Python numbers are of course stronglyrelated to mathematical numbers, but subject to the limitations of numericalrepresentation in computers.

Python distinguishes between integers, floating point numbers, and complexnumbers:

numbers.Integral

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

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 binaryrepresentation is assumed, and negative numbers are represented in a variant of2’s complement which gives the illusion of an infinite string of sign bitsextending to the left.

Booleans (bool)

These represent the truth values False and True. The two objects representingthe valuesFalse andTrue are the only Boolean objects. The Boolean type is asubtype 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 toa string, the strings"False" or"True" are returned, respectively.

The rules for integer representation are intended to give the most meaningfulinterpretation of shift and mask operations involving negative integers.

numbers.Real (float)

These represent machine-level double precision floating point numbers. You areat the mercy of the underlying machine architecture (and C or Javaimplementation) for the accepted range and handling of overflow. Python does notsupport single-precision floating point numbers; the savings in processor andmemory usage that are usually the reason for using these are dwarfed by theoverhead of using objects in Python, so there is no reason to complicate thelanguage with two kinds of floating point numbers.

numbers.Complex (complex)

These represent complex numbers as a pair of machine-level double precisionfloating point numbers. The same caveats apply as for floating point numbers.The real and imaginary parts of a complex numberz can be retrieved throughthe read-only attributesz.real andz.imag.

Sequences

These represent finite ordered sets indexed by non-negative numbers. Thebuilt-in functionlen() returns the number of items of a sequence. Whenthe length of a sequence isn, the index set contains the numbers 0, 1,…,n-1. Itemi of sequencea is selected bya[i].

Sequences also support slicing:a[i:j] selects all items with indexk suchthati<=k<j. When used as an expression, a slice is asequence of the same type. This implies that the index set is renumbered sothat it starts at 0.

Some sequences also support “extended slicing” with a third “step” parameter:a[i:j:k] selects all items ofa with indexx wherex=i+n*k,n>=0 andi<=x<j.

Sequences are distinguished according to their mutability:

Immutable sequences

An object of an immutable sequence type cannot change once it is created. (Ifthe object contains references to other objects, these other objects may bemutable and may be changed; however, the collection of objects directlyreferenced 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+10FFFF can berepresented in a string. Python doesn’t have achar type;instead, every code point in the string is represented as a stringobject with length1. The built-in functionord()converts a code point from its string form to an integer in therange0-10FFFF;chr() converts an integer in the range0-10FFFF to the corresponding length1 string object.str.encode() can be used to convert astr tobytes using the given text encoding, andbytes.decode() can be used to achieve the opposite.

Tuples

The items of a tuple are arbitrary Python objects. Tuples of two ormore items are formed by comma-separated lists of expressions. A tupleof one item (a ‘singleton’) can be formed by affixing a comma to anexpression (an expression by itself does not create a tuple, sinceparentheses must be usable for grouping of expressions). An emptytuple 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-in functionbytes() can be used toconstruct bytes objects. Also, bytes objects can be decoded to stringsvia thedecode() method.

Mutable sequences

Mutable sequences can be changed after they are created. The subscription andslicing notations can be used as the target of assignment anddel(delete) statements.

There are currently two intrinsic mutable sequence types:

Lists

The items of a list are arbitrary Python objects. Lists are formed byplacing a comma-separated list of expressions in square brackets. (Notethat 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-inbytearray() constructor. Aside from being mutable (and henceunhashable), byte arrays otherwise provide the same interface andfunctionality as immutable bytes objects.

The extension modulearray provides an additional example of amutable sequence type, as does thecollections module.

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, andthe built-in functionlen() returns the number of items in a set. Commonuses 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. Notethat numeric types obey the normal rules for numeric comparison: if two numberscompare equal (e.g.,1 and1.0), only one of them can be contained in aset.

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 asadd().

Frozen sets

These represent an immutable set. They are created by the built-infrozenset() constructor. As a frozenset is immutable andhashable, it can be used again as an element of another set, or asa dictionary key.

Mappings

These represent finite sets of objects indexed by arbitrary index sets. Thesubscript notationa[k] selects the item indexed byk from the mappinga; this can be used in expressions and as the target of assignments ordel statements. The built-in functionlen() returns the numberof items in a mapping.

There is currently a single intrinsic mapping type:

Dictionaries

These represent finite sets of objects indexed by nearly arbitrary values. Theonly types of values not acceptable as keys are values containing lists ordictionaries or other mutable types that are compared by value rather than byobject identity, the reason being that the efficient implementation ofdictionaries requires a key’s hash value to remain constant. Numeric types usedfor keys obey the normal rules for numeric comparison: if two numbers compareequal (e.g.,1 and1.0) then they can be used interchangeably to indexthe same dictionary entry.

Dictionaries are mutable; they can be created by the{...} notation (seesectionDictionary displays).

The extension modulesdbm.ndbm anddbm.gnu provideadditional examples of mapping types, as does thecollectionsmodule.

Callable types

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

User-defined functions

A user-defined function object is created by a function definition (seesectionFunction definitions). It should be called with an argument listcontaining the same number of items as the function’s formal parameterlist.

Special attributes:

Attribute	Meaning
__doc__	The function’s documentationstring, orNone ifunavailable; not inherited bysubclasses	Writable
__name__	The function’s name	Writable
__qualname__	The function’squalified name New in version 3.3.	Writable
__module__	The name of the module thefunction was defined in, orNone if unavailable.	Writable
__defaults__	A tuple containing defaultargument values for thosearguments that have defaults,orNone if no argumentshave a default value	Writable
__code__	The code object representingthe compiled function body.	Writable
__globals__	A reference to the dictionarythat holds the function’sglobal variables — theglobal namespace of themodule in which the functionwas defined.	Read-only
__dict__	The namespace supportingarbitrary functionattributes.	Writable
__closure__	None or a tuple of cellsthat contain bindings for thefunction’s free variables.	Read-only
__annotations__	A dict containing annotationsof parameters. The keys ofthe dict are the parameternames, and'return' forthe return annotation, ifprovided.	Writable
__kwdefaults__	A dict containing defaultsfor keyword-only parameters.	Writable

Most of the attributes labelled “Writable” check the type of the assigned value.

Function objects also support getting and setting arbitrary attributes, whichcan be used, for example, to attach metadata to functions. Regular attributedot-notation is used to get and set such attributes.Note that the currentimplementation only supports function attributes on user-defined functions.Function attributes on built-in functions may be supported in the future.

Additional information about a function’s definition can be retrieved from itscode object; see the description of internal types below.

Instance methods

An instance method object combines a class, a class instance and anycallable object (normally a user-defined function).

Special read-only attributes:__self__ is the class instance object,__func__ is the function object;__doc__ is the method’sdocumentation (same as__func__.__doc__);__name__ is themethod name (same as__func__.__name__);__module__ is thename of the module the method was defined in, orNone if unavailable.

Methods also support accessing (but not setting) the arbitrary functionattributes on the underlying function object.

User-defined method objects may be created when getting an attribute of aclass (perhaps via an instance of that class), if that attribute is auser-defined function object or a class method object.

When an instance method object is created by retrieving a user-definedfunction object from a class via one of its instances, its__self__ attribute is the instance, and the method object is saidto be bound. The new method’s__func__ attribute is the originalfunction object.

When a user-defined method object is created by retrieving another methodobject from a class or instance, the behaviour is the same as for afunction object, except that the__func__ attribute of the newinstance is not the original method object but its__func__attribute.

When an instance method object is created by retrieving a class methodobject from a class or instance, its__self__ attribute is theclass itself, and its__func__ attribute is the function objectunderlying 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, whenC is a class which contains a definition for a functionf(), andx is an instance ofC, callingx.f(1) isequivalent to callingC.f(x,1).

When an instance method object is derived from a class method object, the“class instance” stored in__self__ will actually be the classitself, so that calling eitherx.f(1) orC.f(1) is equivalent tocallingf(C,1) wheref is the underlying function.

Note that the transformation from function object to instance methodobject happens each time the attribute is retrieved from the instance. Insome cases, a fruitful optimization is to assign the attribute to a localvariable and call that local variable. Also notice that thistransformation only happens for user-defined functions; other callableobjects (and all non-callable objects) are retrieved withouttransformation. It is also important to note that user-defined functionswhich are attributes of a class instance are not converted to boundmethods; thisonly happens when the function is an attribute of theclass.

Generator functions

A function or method which uses theyield statement (see sectionThe yield statement) is called agenerator function. Such a function, whencalled, always returns an iterator object which can be used to execute thebody of the function: calling the iterator’siterator.__next__()method will cause the function to execute until it provides a valueusing theyield statement. When the function executes areturn statement or falls off the end, aStopIterationexception is raised and the iterator will have reached the end of the set ofvalues to be returned.

Coroutine functions

A function or method which is defined usingasyncdef is calledacoroutine function. Such a function, when called, returns acoroutine object. It may containawait expressions,as well asasyncwith andasyncfor statements. Seealso theCoroutine Objects section.

Built-in functions

A built-in function object is a wrapper around a C function. Examples ofbuilt-in functions arelen() andmath.sin() (math is astandard built-in module). The number and type of the arguments aredetermined by the C function. Special read-only attributes:__doc__ is the function’s documentation string, orNone ifunavailable;__name__ is the function’s name;__self__ isset toNone (but see the next item);__module__ is the name ofthe module the function was defined in orNone if unavailable.

Built-in methods

This is really a different disguise of a built-in function, this time containingan object passed to the C function as an implicit extra argument. An example ofa built-in method isalist.append(), assumingalist is a list object. Inthis case, the special read-only attribute__self__ is set to the objectdenoted byalist.

Classes

Classes are callable. These objects normally act as factories for newinstances of themselves, but variations are possible for class types thatoverride__new__(). The arguments of the call are passed to__new__() and, in the typical case, to__init__() toinitialize the new instance.

Class Instances

Instances of arbitrary classes can be made callable by defining a__call__() method in their class.

Modules

Modules are a basic organizational unit of Python code, and are created bytheimport system as invoked either by theimport statement (seeimport), or by callingfunctions such asimportlib.import_module() and built-in__import__(). A module object has a namespace implemented by adictionary object (this is the dictionary referenced by the__globals__attribute of functions defined in the module). Attribute references aretranslated to lookups in this dictionary, e.g.,m.x is equivalent tom.__dict__["x"]. A module object does not contain the code object usedto initialize the module (since it isn’t needed once the initialization isdone).

Attribute assignment updates the module’s namespace dictionary, e.g.,m.x=1 is equivalent tom.__dict__["x"]=1.

Special read-only attribute:__dict__ is the module’s namespace as adictionary object.

CPython implementation detail: Because of the way CPython clears module dictionaries, the moduledictionary will be cleared when the module falls out of scope even if thedictionary still has live references. To avoid this, copy the dictionaryor keep the module around while using its dictionary directly.

Predefined (writable) attributes:__name__ is the module’s name;__doc__ is the module’s documentation string, orNone ifunavailable;__file__ is the pathname of the file from which themodule was loaded, if it was loaded from a file. The__file__attribute may be missing for certain types of modules, such as C modulesthat are statically linked into the interpreter; for extension modulesloaded dynamically from a shared library, it is the pathname of the sharedlibrary file.

Custom classes

Custom class types are typically created by class definitions (see sectionClass definitions). A class has a namespace implemented by a dictionary object.Class attribute references are translated to lookups in this dictionary, e.g.,C.x is translated toC.__dict__["x"] (although there are a number ofhooks which allow for other means of locating attributes). When the attributename is not found there, the attribute search continues in the base classes.This search of the base classes uses the C3 method resolution order whichbehaves correctly even in the presence of ‘diamond’ inheritance structureswhere there are multiple inheritance paths leading back to a common ancestor.Additional details on the C3 MRO used by Python can be found in thedocumentation accompanying the 2.3 release athttps://www.python.org/download/releases/2.3/mro/.

When a class attribute reference (for classC, say) would yield aclass method object, it is transformed into an instance method object whose__self__ attributes isC. When it would yield a staticmethod object, it is transformed into the object wrapped by the static methodobject. See sectionImplementing Descriptors for another way in which attributesretrieved from a class may differ from those actually contained in its__dict__.

Class attribute assignments update the class’s dictionary, never the dictionaryof a base class.

A class object can be called (see above) to yield a class instance (see below).

Special attributes:__name__ is the class name;__module__ isthe module name in which the class was defined;__dict__ is thedictionary containing the class’s namespace;__bases__ is atuple containing the base classes, in the order of their occurrence in thebase class list;__doc__ is the class’s documentation string, orNone if undefined.

Class instances

A class instance is created by calling a class object (see above). A classinstance has a namespace implemented as a dictionary which is the first placein which attribute references are searched. When an attribute is not foundthere, and the instance’s class has an attribute by that name, the searchcontinues with the class attributes. If a class attribute is found that is auser-defined function object, it is transformed into an instance methodobject whose__self__ attribute is the instance. Static method andclass method objects are also transformed; see above under “Classes”. SeesectionImplementing Descriptors for another way in which attributes of a classretrieved via its instances may differ from the objects actually stored inthe class’s__dict__. If no class attribute is found, and theobject’s class has a__getattr__() method, that is called to satisfythe lookup.

Attribute assignments and deletions update the instance’s dictionary, never aclass’s dictionary. If the class has a__setattr__() or__delattr__() method, this is called instead of updating the instancedictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they havemethods with certain special names. See sectionSpecial method names.

Special attributes:__dict__ is the attribute dictionary;__class__ is the instance’s class.

I/O objects (also known as file objects)

Afile object represents an open file. Various shortcuts areavailable to create file objects: theopen() built-in function, andalsoos.popen(),os.fdopen(), and themakefile() method of socket objects (and perhaps byother functions or methods provided by extension modules).

The objectssys.stdin,sys.stdout andsys.stderr areinitialized to file objects corresponding to the interpreter’s standardinput, output and error streams; they are all open in text mode andtherefore follow the interface defined by theio.TextIOBaseabstract class.

Internal types

A few types used internally by the interpreter are exposed to the user. Theirdefinitions may change with future versions of the interpreter, but they arementioned here for completeness.

Code objects

Code objects representbyte-compiled executable Python code, orbytecode.The difference between a code object and a function object is that the functionobject contains an explicit reference to the function’s globals (the module inwhich it was defined), while a code object contains no context; also the defaultargument values are stored in the function object, not in the code object(because they represent values calculated at run-time). Unlike functionobjects, code objects are immutable and contain no references (directly orindirectly) to mutable objects.

Special read-only attributes:co_name gives the function name;co_argcount is the number of positional arguments (including argumentswith default values);co_nlocals is the number of local variables usedby the function (including arguments);co_varnames is a tuple containingthe names of the local variables (starting with the argument names);co_cellvars is a tuple containing the names of local variables that arereferenced by nested functions;co_freevars is a tuple containing thenames of free variables;co_code is a string representing the sequenceof bytecode instructions;co_consts is a tuple containing the literalsused by the bytecode;co_names is a tuple containing the names used bythe bytecode;co_filename is the filename from which the code wascompiled;co_firstlineno is the first line number of the function;co_lnotab is a string encoding the mapping from bytecode offsets toline numbers (for details see the source code of the interpreter);co_stacksize is the required stack size (including local variables);co_flags is an integer encoding a number of flags for the interpreter.

The following flag bits are defined forco_flags: bit0x04 is set ifthe function uses the*arguments syntax to accept an arbitrary number ofpositional arguments; bit0x08 is set if the function uses the**keywords syntax to accept arbitrary keyword arguments; bit0x20 is setif the function is a generator.

Future feature declarations (from__future__importdivision) also use bitsinco_flags to indicate whether a code object was compiled with aparticular feature enabled: bit0x2000 is set if the function was compiledwith future division enabled; bits0x10 and0x1000 were used in earlierversions of Python.

Other bits inco_flags are reserved for internal use.

If a code object represents a function, the first item inco_consts isthe documentation string of the function, orNone if undefined.

Frame objects

Frame objects represent execution frames. They may occur in traceback objects(see below).

Special read-only attributes:f_back is to the previous stack frame(towards the caller), orNone if this is the bottom stack frame;f_code is the code object being executed in this frame;f_localsis the dictionary used to look up local variables;f_globals is used forglobal variables;f_builtins is used for built-in (intrinsic) names;f_lasti gives the precise instruction (this is an index into thebytecode string of the code object).

Special writable attributes:f_trace, if notNone, is a functioncalled at the start of each source code line (this is used by the debugger);f_lineno is the current line number of the frame — writing to thisfrom within a trace function jumps to the given line (only for the bottom-mostframe). A debugger can implement a Jump command (aka Set Next Statement)by writing to f_lineno.

Frame objects support one method:

frame.clear()¶

This method clears all references to local variables held by theframe. Also, if the frame belonged to a generator, the generatoris finalized. This helps break reference cycles involving frameobjects (for example when catching an exception and storing itstraceback for later use).

RuntimeError is raised if the frame is currently executing.

New in version 3.4.

Traceback objects

Traceback objects represent a stack trace of an exception. A traceback objectis created when an exception occurs. When the search for an exception handlerunwinds the execution stack, at each unwound level a traceback object isinserted in front of the current traceback. When an exception handler isentered, the stack trace is made available to the program. (See sectionThe try statement.) It is accessible as the third item of thetuple returned bysys.exc_info(). When the program contains no suitablehandler, the stack trace is written (nicely formatted) to the standard errorstream; if the interpreter is interactive, it is also made available to the userassys.last_traceback.

Special read-only attributes:tb_next is the next level in the stacktrace (towards the frame where the exception occurred), orNone if there isno next level;tb_frame points to the execution frame of the currentlevel;tb_lineno gives the line number where the exception occurred;tb_lasti indicates the precise instruction. The line number and lastinstruction in the traceback may differ from the line number of its frame objectif the exception occurred in atry statement with no matching exceptclause or with a finally clause.

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:start is the lower bound;stop is the upper bound;step is the stepvalue; each isNone if omitted. These attributes can have any type.

Slice objects support one method:

slice.indices(self,length)¶

This method takes a single integer argumentlength and computesinformation about the slice that the slice object would describe ifapplied to a sequence oflength items. It returns a tuple of threeintegers; respectively these are thestart andstop indices and thestep or stride length of the slice. Missing or out-of-bounds indicesare handled in a manner consistent with regular slices.

Static method objects

Static method objects provide a way of defeating the transformation of functionobjects to method objects described above. A static method object is a wrapperaround any other object, usually a user-defined method object. When a staticmethod object is retrieved from a class or a class instance, the object actuallyreturned is the wrapped object, which is not subject to any furthertransformation. Static method objects are not themselves callable, although theobjects they wrap usually are. Static method objects are created by the built-instaticmethod() constructor.

Class method objects

A class method object, like a static method object, is a wrapper around anotherobject that alters the way in which that object is retrieved from classes andclass instances. The behaviour of class method objects upon such retrieval isdescribed above, under “User-defined methods”. Class method objects are createdby 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 methodswith special names. This is Python’s approach tooperator overloading,allowing classes to define their own behavior with respect to languageoperators. For instance, if a class defines a method named__getitem__(),andx is an instance of this class, thenx[i] is roughly equivalenttotype(x).__getitem__(x,i). Except where mentioned, attempts to execute anoperation raise an exception when no appropriate method is defined (typicallyAttributeError orTypeError).

When implementing a class that emulates any built-in type, it is important thatthe emulation only be implemented to the degree that it makes sense for theobject being modelled. For example, some sequences may work well with retrievalof individual elements, but extracting a slice may not make sense. (One exampleof this is theNodeList interface in the W3C’s DocumentObject Model.)

classMeta(type):passclassMyClass(metaclass=Meta):passclassMySubclass(MyClass):pass

classOrderedClass(type):@classmethoddef__prepare__(metacls,name,bases,**kwds):returncollections.OrderedDict()def__new__(cls,name,bases,namespace,**kwds):result=type.__new__(cls,name,bases,dict(namespace))result.members=tuple(namespace)returnresultclassA(metaclass=OrderedClass):defone(self):passdeftwo(self):passdefthree(self):passdeffour(self):pass>>>A.members('__module__','one','two','three','four')

a[1:2]=b

a[slice(1,2,None)]=b

>>>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()

>>>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

>>>type(1).__hash__(1)==hash(1)True>>>type(int).__hash__(int)==hash(int)True

>>>classMeta(type):...def__getattribute__(*args):...print("Metaclass getattribute invoked")...returntype.__getattribute__(*args)...>>>classC(object,metaclass=Meta):...def__len__(self):...return10...def__getattribute__(*args):...print("Class getattribute invoked")...returnobject.__getattribute__(*args)...>>>c=C()>>>c.__len__()# Explicit lookup via instanceClass getattribute invoked10>>>type(c).__len__(c)# Explicit lookup via typeMetaclass getattribute invoked10>>>len(c)# Implicit lookup10

3.4. Coroutines¶

classReader:asyncdefreadline(self):...def__aiter__(self):returnselfasyncdef__anext__(self):val=awaitself.readline()ifval==b'':raiseStopAsyncIterationreturnval

importfunctoolsimportsysifsys.version_info<(3,5,2):defaiter_compat(func):@functools.wraps(func)asyncdefwrapper(self):returnfunc(self)returnwrapperelse:defaiter_compat(func):returnfunc

classAsyncIterator:@aiter_compatdef__aiter__(self):returnselfasyncdef__anext__(self):...

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