3.Data model¶

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 (currentlyimplemented as its address). An object’stype is also unchangeable.1An 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). 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.

CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayeddetection of cyclically linked garbage, which collects most objects as soonas they become unreachable, but is not guaranteed to collect garbagecontaining circular references. See the documentation of thegcmodule 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 becomeunreachable (ex: always close files).

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’ statementprovides a convenient way 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.).

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 may 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.

Ellipsis

This type has a single value. There is a single object with this value. Thisobject is accessed through the built-in nameEllipsis. It is used toindicate the presence of the... syntax in a slice. Its truth value istrue.

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 three types of integers:

Plain integers: These represent numbers in the range -2147483648 through 2147483647.(The range may be larger on machines with a larger natural word size,but not smaller.) When the result of an operation would fall outsidethis range, the result is normally returned as a long integer (in somecases, the exceptionOverflowError is raised instead). For thepurpose of shift and mask operations, integers are assumed to have abinary, 2’s complement notation using 32 or more bits, and hiding nobits from the user (i.e., all 4294967296 different bit patternscorrespond to different values).
Long integers: These represent numbers in an unlimited range, subject to available(virtual) memory only. For the purpose of shift and mask operations, abinary representation is assumed, and negative numbers are representedin a variant of 2’s complement which gives the illusion of an infinitestring of sign bits extending to the left.
Booleans: These represent the truth values False and True. The two objectsrepresenting the valuesFalse andTrue are the only Boolean objects.The Boolean type is a subtype of plain integers, and Boolean valuesbehave 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.

The rules for integer representation are intended to give the mostmeaningful interpretation of shift and mask operations involving negativeintegers and the least surprises when switching between the plain and longinteger domains. Any operation, if it yields a result in the plaininteger domain, will yield the same result in the long integer domain orwhen using mixed operands. The switch between domains is transparent tothe programmer.

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

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

The items of a string are characters. There is no separate character type; acharacter is represented by a string of one item. Characters represent (atleast) 8-bit bytes. The built-in functionschr() andord() convertbetween characters and nonnegative integers representing the byte values. Byteswith the values 0–127 usually represent the corresponding ASCII values, but theinterpretation of values is up to the program. The string data type is alsoused to represent arrays of bytes, e.g., to hold data read from a file.

(On systems whose native character set is not ASCII, strings may use EBCDIC intheir internal representation, provided the functionschr() andord() implement a mapping between ASCII and EBCDIC, and string comparisonpreserves the ASCII order. Or perhaps someone can propose a better rule?)

Unicode

The items of a Unicode object are Unicode code units. A Unicode code unit isrepresented by a Unicode object of one item and can hold either a 16-bit or32-bit value representing a Unicode ordinal (the maximum value for the ordinalis given insys.maxunicode, and depends on how Python is configured atcompile time). Surrogate pairs may be present in the Unicode object, and willbe reported as two separate items. The built-in functionsunichr() andord() convert between code units and nonnegative integers representing theUnicode ordinals as defined in the Unicode Standard 3.0. Conversion from and toother encodings are possible through the Unicode methodencode() and thebuilt-in functionunicode().

Tuples

The items of a tuple are arbitrary Python objects. Tuples of two or more itemsare 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 expressionby itself does not create a tuple, since parentheses must be usable for groupingof expressions). An empty tuple can be formed by an empty pair of parentheses.

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 by placing acomma-separated list of expressions in square brackets. (Note that there are nospecial 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 a mutablesequence type.

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,gdbm, andbsddb provideadditional examples of mapping types.

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__func_doc`	The function’s documentationstring, or`None` ifunavailable.	Writable
`__name__func_name`	The function’s name	Writable
`__module__`	The name of the module thefunction was defined in, or`None` if unavailable.	Writable
`__defaults__func_defaults`	A tuple containing defaultargument values for thosearguments that have defaults,or`None` if no argumentshave a default value.	Writable
`__code__func_code`	The code object representingthe compiled function body.	Writable
`__globals__func_globals`	A reference to the dictionarythat holds the function’sglobal variables — theglobal namespace of themodule in which the functionwas defined.	Read-only
`__dict__func_dict`	The namespace supportingarbitrary functionattributes.	Writable
`__closure__func_closure`	`None` or a tuple of cellsthat contain bindings for thefunction’s free variables.	Read-only

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

Changed in version 2.4:func_name is now writable.

Changed in version 2.6:The double-underscore attributes__closure__,__code__,__defaults__, and__globals__ were introduced as aliases forthe correspondingfunc_* attributes for forwards compatibilitywith Python 3.

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.

User-defined methods

A user-defined method object combines a class, a class instance (orNone)and any callable object (normally a user-defined function).

Special read-only attributes:im_self is the class instance object,im_func is the function object;im_class is the class ofim_self for bound methods or the class that asked for the method forunbound methods;__doc__ is the method’s documentation (same asim_func.__doc__);__name__ is the method name (same asim_func.__name__);__module__ is the name of the module the methodwas defined in, orNone if unavailable.

Changed in version 2.2:im_self used to refer to the class that defined the method.

Changed in version 2.6:For Python 3 forward-compatibility,im_func is also available as__func__, andim_self as__self__.

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 a class(perhaps via an instance of that class), if that attribute is a user-definedfunction object, an unbound user-defined method object, or a class methodobject. When the attribute is a user-defined method object, a new method objectis only created if the class from which it is being retrieved is the same as, ora derived class of, the class stored in the original method object; otherwise,the original method object is used as it is.

When a user-defined method object is created by retrieving a user-definedfunction object from a class, itsim_self attribute isNoneand the method object is said to be unbound. When one is created byretrieving a user-defined function object from a class via one of itsinstances, itsim_self attribute is the instance, and the methodobject is said to be bound. In either case, the new method’sim_class attribute is the class from which the retrieval takesplace, and itsim_func attribute is the original function object.

When a user-defined method object is created by retrieving another method objectfrom a class or instance, the behaviour is the same as for a function object,except that theim_func attribute of the new instance is not theoriginal method object but itsim_func attribute.

When a user-defined method object is created by retrieving a class method objectfrom a class or instance, itsim_self attribute is the class itself, anditsim_func attribute is the function object underlying the class method.

When an unbound user-defined method object is called, the underlying function(im_func) is called, with the restriction that the first argument mustbe an instance of the proper class (im_class) or of a derived classthereof.

When a bound user-defined method object is called, the underlying function(im_func) is called, inserting the class instance (im_self) infront of the argument list. For instance, whenC is a class whichcontains a definition for a functionf(), andx is an instance ofC, callingx.f(1) is equivalent to callingC.f(x,1).

When a user-defined method object is derived from a class method object, the“class instance” stored inim_self will actually be the class itself, sothat calling eitherx.f(1) orC.f(1) is equivalent to callingf(C,1)wheref is the underlying function.

Note that the transformation from function object to (unbound or bound) methodobject happens each time the attribute is retrieved from the class or instance.In some cases, a fruitful optimization is to assign the attribute to a localvariable and call that local variable. Also notice that this transformation onlyhappens for user-defined functions; other callable objects (and all non-callableobjects) are retrieved without transformation. It is also important to notethat user-defined functions which are attributes of a class instance are notconverted to bound methods; thisonly happens when the function is anattribute of the class.

Generator functions

A function or method which uses theyield statement (see sectionThe yield statement) is called ageneratorfunction. Such a function, when called, always returns an iterator objectwhich can be used to execute the body of the function: calling the iterator’snext() method will cause the function to execute untilit 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.

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.

Class Types

Class types, or “new-style classes,” are callable. These objects normally actas factories for new instances of themselves, but variations are possible forclass types that override__new__(). The arguments of the call are passedto__new__() and, in the typical case, to__init__() to initializethe new instance.

Classic Classes

Class objects are described below. When a class object is called, a new classinstance (also described below) is created and returned. This implies a call tothe class’s__init__() method if it has one. Any arguments are passed onto the__init__() method. If there is no__init__() method, theclass must be called without arguments.

Class instances

Class instances are described below. Class instances are callable only when theclass has a__call__() method;x(arguments) is a shorthand forx.__call__(arguments).

Modules

Modules are imported by theimport statement (see sectionThe import statement). A module object has anamespace implemented by a dictionary object (this is the dictionary referencedby the func_globals attribute of functions defined in the module). Attributereferences are translated to lookups in this dictionary, e.g.,m.x isequivalent tom.__dict__["x"]. A module object does not contain the codeobject used to initialize the module (since it isn’t needed once theinitialization is done).

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 the modulewas loaded, if it was loaded from a file. The__file__ attribute is notpresent for C modules that are statically linked into the interpreter; forextension modules loaded dynamically from a shared library, it is the pathnameof the shared library file.

Classes

Both class types (new-style classes) and class objects (old-style/classicclasses) 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 for new-style classesin particular there are a number of hooks which allow for other means oflocating attributes). When the attribute name is not found there, theattribute search continues in the base classes. For old-style classes, thesearch is depth-first, left-to-right in the order of occurrence in the baseclass list. New-style classes use the more complex C3 method resolutionorder which behaves correctly even in the presence of ‘diamond’inheritance structures where there are multiple inheritance pathsleading back to a common ancestor. Additional details on the C3 MRO used bynew-style classes can be found in the documentation accompanying the2.3 release athttps://www.python.org/download/releases/2.3/mro/.

When a class attribute reference (for classC, say) would yield auser-defined function object or an unbound user-defined method object whoseassociated class is eitherC or one of its base classes, it istransformed into an unbound user-defined method object whoseim_classattribute isC. When it would yield a class method object, it istransformed into a bound user-defined method object whoseim_self attribute isC. When it would yield astatic method object, it is transformed into the object wrapped by the staticmethod object. See sectionImplementing Descriptors for another way in whichattributes retrieved from a class may differ from those actually contained inits__dict__ (note that only new-style classes support descriptors).

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 (possibly empty or a singleton) containing the base classes, in theorder of their occurrence in the base class list;__doc__ is theclass’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 place inwhich attribute references are searched. When an attribute is not found there,and the instance’s class has an attribute by that name, the search continueswith the class attributes. If a class attribute is found that is a user-definedfunction object or an unbound user-defined method object whose associated classis the class (call itC) of the instance for which the attributereference was initiated or one of its bases, it is transformed into a bounduser-defined method object whoseim_class attribute isC andwhoseim_self attribute is the instance. Static method and class methodobjects are also transformed, as if they had been retrieved from classC; see above under “Classes”. See sectionImplementing Descriptors foranother way in which attributes of a class retrieved via its instances maydiffer from the objects actually stored in the class’s__dict__. If noclass 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 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.

Files

A file object represents an open file. File objects are created by theopen() built-in function, and also byos.popen(),os.fdopen(), and themakefile() method of socket objects (andperhaps by other functions or methods provided by extension modules). Theobjectssys.stdin,sys.stdout andsys.stderr are initialized tofile objects corresponding to the interpreter’s standard input, output anderror streams. SeeFile Objects for complete documentation offile objects.

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_restricted is a flag indicating whether the function is executing inrestricted execution mode;f_lasti gives the precise instruction (thisis an index into the bytecode 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_exc_type,f_exc_value,f_exc_traceback represent thelast exception raised in the parent frame provided another exception was everraised in the current frame (in all other cases they areNone);f_linenois the current line number of the frame — writing to this from within a tracefunction jumps to the given line (only for the bottom-most frame). A debuggercan implement a Jump command (aka Set Next Statement) by writing to f_lineno.

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 assys.exc_traceback,and also as the third item of the tuple returned bysys.exc_info(). Thelatter is the preferred interface, since it works correctly when the program isusing multiple threads. When the program contains no suitable handler, the stacktrace is written (nicely formatted) to the standard error stream; if theinterpreter is interactive, it is also made available to the user assys.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 whenextended slice syntax is used.This is a slice using two colons, or multiple slices or ellipses separated bycommas, e.g.,a[i:j:step],a[i:j,k:l], ora[...,i:j]. They arealso 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 computes informationabout the extended slice that the slice object would describe if applied to asequence oflength items. It returns a tuple of three integers; respectivelythese are thestart andstop indices and thestep or stride length of theslice. Missing or out-of-bounds indices are handled in a manner consistent withregular slices.
New in version 2.3.

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.New-style and classic classes¶

Classes and instances come in two flavors: old-style (or classic) and new-style.

Up to Python 2.1 the concept ofclass was unrelated to the concept oftype, and old-style classes were the only flavor available. For anold-style class, the statementx.__class__ provides the class ofx, buttype(x) is always<type'instance'>. This reflects the fact that allold-style instances, independent of their class, are implemented with a singlebuilt-in type, calledinstance.

New-style classes were introduced in Python 2.2 to unify the concepts ofclass andtype. A new-style class is simply a user-defined type,no more, no less. Ifx is an instance of a new-style class, thentype(x)is typically the same asx.__class__ (although this is not guaranteed – anew-style class instance is permitted to override the value returned forx.__class__).

The major motivation for introducing new-style classes is to provide a unifiedobject model with a full meta-model. It also has a number of practicalbenefits, like the ability to subclass most built-in types, or the introductionof “descriptors”, which enable computed properties.

For compatibility reasons, classes are still old-style by default. New-styleclasses are created by specifying another new-style class (i.e. a type) as aparent class, or the “top-level type”object if no other parent isneeded. The behaviour of new-style classes differs from that of old-styleclasses in a number of important details in addition to whattype()returns. Some of these changes are fundamental to the new object model, likethe way special methods are invoked. Others are “fixes” that could not beimplemented before for compatibility concerns, like the method resolution orderin case of multiple inheritance.

While this manual aims to provide comprehensive coverage of Python’s classmechanics, it may still be lacking in some areas when it comes to its coverageof new-style classes. Please seehttps://www.python.org/doc/newstyle/ forsources of additional information.

Old-style classes are removed in Python 3, leaving only new-style classes.

3.4.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 equivalenttox.__getitem__(i) for old-style classes andtype(x).__getitem__(x,i)for new-style classes. 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.)

3.4.1.Basic customization¶

object.__new__(cls[,...])¶

Called to create a new instance of classcls.__new__() is a staticmethod (special-cased so you need not declare it as such) that takes the classof which an instance was requested as its first argument. The remainingarguments are those passed to the object constructor expression (the call to theclass). 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 thesuperclass’s__new__() method usingsuper(currentclass,cls).__new__(cls[,...]) with appropriate arguments and then modifying thenewly-created instance as necessary before returning it.

If__new__() returns an instance ofcls, then the new instance’s__init__() method will be invoked like__init__(self[,...]), whereself is the new instance and the remaining arguments are the same as werepassed to__new__().

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 (likeint, str, or tuple) to customize instance creation. It is also commonlyoverridden in custom metaclasses in order to customize class creation.

object.__init__(self[,...])¶

Called after the instance has been created (by__new__()), but beforeit is returned to the caller. The arguments are those passed to theclass constructor expression. If a base class has an__init__() method,the derived class’s__init__() method, if any, must explicitly call it toensure proper initialization of the base class part of the instance; forexample:BaseClass.__init__(self,[args...]).

Because__new__() and__init__() work together in constructingobjects (__new__() to create it, and__init__() to customise it),no non-None value may be returned by__init__(); doing so willcause aTypeError to be raised at runtime.

object.__del__(self)¶

Called when the instance is about to be destroyed. This is also called adestructor. If a base class has a__del__() method, the derived class’s__del__() method, if any, must explicitly call it to ensure properdeletion of the base class part of the instance. Note that it is possible(though not recommended!) for the__del__() method to postpone destructionof the instance by creating a new reference to it. It may then be called at alater time when this new reference is deleted. It is not guaranteed that__del__() methods are called for objects that still exist when theinterpreter exits.

Note

delx doesn’t directly callx.__del__() — the former decrementsthe reference count forx by one, and the latter is only called whenx’s reference count reaches zero. Some common situations that mayprevent the reference count of an object from going to zero include:circular references between objects (e.g., a doubly-linked list or a treedata structure with parent and child pointers); a reference to the objecton the stack frame of a function that caught an exception (the tracebackstored insys.exc_traceback keeps the stack frame alive); or areference to the object on the stack frame that raised an unhandledexception in interactive mode (the traceback stored insys.last_traceback keeps the stack frame alive). The first situationcan only be remedied by explicitly breaking the cycles; the latter twosituations can be resolved by storingNone insys.exc_traceback orsys.last_traceback. Circular references which are garbage aredetected when the option cycle detector is enabled (it’s on by default),but can only be cleaned up if there are no Python-level__del__()methods involved. Refer to the documentation for thegc module formore information about how__del__() methods are handled by thecycle detector, particularly the description of thegarbage value.

Warning

Due to the precarious circumstances under which__del__() methods areinvoked, exceptions that occur during their execution are ignored, and a warningis printed tosys.stderr instead. Also, when__del__() is invoked inresponse to a module being deleted (e.g., when execution of the program isdone), other globals referenced by the__del__() method may already havebeen deleted or in the process of being torn down (e.g. the importmachinery shutting down). For this reason,__del__() methodsshould do the absoluteminimum needed to maintain external invariants. Starting with version 1.5,Python guarantees that globals whose name begins with a single underscore aredeleted from their module before other globals are deleted; if no otherreferences to such globals exist, this may help in assuring that importedmodules are still available at the time when the__del__() method iscalled.

3.4.2.Customizing attribute access¶

The following methods can be defined to customize the meaning of attributeaccess (use of, assignment to, or deletion ofx.name) for class instances.

object.__getattr__(self,name)¶

Called when an attribute lookup has not found the attribute in the usual places(i.e. it is not an instance attribute nor is it found in the class tree forself).name is the attribute name. This method should return the(computed) attribute value or raise anAttributeError exception.

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 efficiencyreasons and because otherwise__getattr__() would have no way to accessother attributes of the instance. Note that at least for instance variables,you can fake total control by not inserting any values in the instance attributedictionary (but instead inserting them in another object). See the__getattribute__() method below for a way to actually get total control innew-style classes.

object.__setattr__(self,name,value)¶

Called when an attribute assignment is attempted. This is called instead of thenormal mechanism (i.e. store the value in the instance dictionary).name isthe attribute name,value is the value to be assigned to it.

If__setattr__() wants to assign to an instance attribute, it should notsimply executeself.name=value — this would cause a recursive call toitself. Instead, it should insert the value in the dictionary of instanceattributes, e.g.,self.__dict__[name]=value. For new-style classes,rather than accessing the instance dictionary, it should call the base classmethod with the same name, for example,object.__setattr__(self,name,value).

object.__delattr__(self,name)¶: Like__setattr__() but for attribute deletion instead of assignment. Thisshould only be implemented ifdelobj.name is meaningful for the object.

3.4.2.1.More attribute access for new-style classes¶

The following methods only apply to new-style classes.

object.__getattribute__(self,name)¶: Called unconditionally to implement attribute accesses for instances of theclass. If the class also defines__getattr__(), the latter will not becalled unless__getattribute__() either calls it explicitly or raises anAttributeError. This method should return the (computed) attribute valueor raise anAttributeError exception. In order to avoid infiniterecursion in this method, its implementation should always call the base classmethod 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 theresult of implicit invocation via language syntax or built-in functions.SeeSpecial method lookup for new-style classes.

3.4.2.2.Implementing Descriptors¶

The following methods only apply when an instance of the class containing themethod (a so-calleddescriptor class) appears in anowner class (thedescriptor must be in either the owner’s class dictionary or in the classdictionary 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 ownerclass’__dict__.

object.__get__(self,instance,owner)¶: Called to get the attribute of the owner class (class attribute access) or of aninstance of that class (instance attribute access).owner is always the ownerclass, whileinstance is the instance that the attribute was accessed through,orNone when the attribute is accessed through theowner. This methodshould return the (computed) attribute value or raise anAttributeErrorexception.

object.__set__(self,instance,value)¶: Called to set the attribute on an instanceinstance of the owner class to anew value,value.

object.__delete__(self,instance)¶: Called to delete the attribute on an instanceinstance of the owner class.

3.4.2.3.Invoking Descriptors¶

In general, a descriptor is an object attribute with “binding behavior”, onewhose attribute access has been overridden by methods in the descriptorprotocol:__get__(),__set__(), and__delete__(). If any ofthose 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 theattribute from an object’s dictionary. For instance,a.x has a lookup chainstarting witha.__dict__['x'], thentype(a).__dict__['x'], andcontinuing through the base classes oftype(a) excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptormethods, then Python may override the default behavior and invoke the descriptormethod instead. Where this occurs in the precedence chain depends on whichdescriptor methods were defined and how they were called. Note that descriptorsare only invoked for new style objects or classes (ones that subclassobject() ortype()).

The starting point for descriptor invocation is a binding,a.x. How thearguments are assembled depends ona:

Direct Call: The simplest and least common call is when user code directly invokes adescriptor method:x.__get__(a).
Instance Binding: If binding to a new-style object instance,a.x is transformed into the call:type(a).__dict__['x'].__get__(a,type(a)).
Class Binding: If binding to a new-style class,A.x is transformed into the call:A.__dict__['x'].__get__(None,A).
Super Binding: Ifa is an instance ofsuper, then the bindingsuper(B,obj).m() searchesobj.__class__.__mro__ for the base classAimmediately precedingB and then invokes the descriptor with the call:A.__dict__['m'].__get__(obj,obj.__class__).

For instance bindings, the precedence of descriptor invocation depends on thewhich descriptor methods are defined. A descriptor can define any combinationof__get__(),__set__() and__delete__(). If it does notdefine__get__(), then accessing the attribute will return the descriptorobject itself unless there is a value in the object’s instance dictionary. Ifthe descriptor defines__set__() and/or__delete__(), it is a datadescriptor; if it defines neither, it is a non-data descriptor. Normally, datadescriptors define both__get__() and__set__(), while non-datadescriptors have just the__get__() method. Data descriptors with__set__() and__get__() defined always override a redefinition in aninstance dictionary. In contrast, non-data descriptors can be overridden byinstances.

Python methods (includingstaticmethod() andclassmethod()) areimplemented as non-data descriptors. Accordingly, instances can redefine andoverride methods. This allows individual instances to acquire behaviors thatdiffer from other instances of the same class.

Theproperty() function is implemented as a data descriptor. Accordingly,instances cannot override the behavior of a property.

3.4.2.4.slots¶

By default, instances of both old and new-style classes have a dictionary forattribute storage. This wastes space for objects having very few instancevariables. The space consumption can become acute when creating large numbersof instances.

The default can be overridden by defining__slots__ in a new-style classdefinition. The__slots__ declaration takes a sequence of instance variablesand reserves just enough space in each instance to hold a value for eachvariable. Space is saved because__dict__ is not created for each instance.

__slots__¶: This class variable can be assigned a string, iterable, or sequence of stringswith variable names used by instances. If defined in a new-style class,__slots__ reserves space for the declared variables and prevents the automaticcreation of__dict__ and__weakref__ for each instance.
New in version 2.2.

Notes on using__slots__

When inheriting from a class without__slots__, the__dict__ attribute ofthat class will always be accessible, so a__slots__ definition in thesubclass is meaningless.
Without a__dict__ variable, instances cannot be assigned new variables notlisted in the__slots__ definition. Attempts to assign to an unlistedvariable name raisesAttributeError. If dynamic assignment of newvariables is desired, then add'__dict__' to the sequence of strings in the__slots__ declaration.
Changed in version 2.3:Previously, adding'__dict__' to the__slots__ declaration would notenable the assignment of new attributes not specifically listed in the sequenceof instance variable names.
Without a__weakref__ variable for each instance, classes defining__slots__ do not support weak references to its instances. If weak referencesupport is needed, then add'__weakref__' to the sequence of strings in the__slots__ declaration.
Changed in version 2.3:Previously, adding'__weakref__' to the__slots__ declaration would notenable support for weak references.
__slots__ are implemented at the class level by creating descriptors(Implementing Descriptors) for each variable name. As a result, class attributescannot be used to set default values for instance variables defined by__slots__; otherwise, the class attribute would overwrite the descriptorassignment.
The action of a__slots__ declaration is limited to the class where it isdefined. As a result, subclasses will have a__dict__ unless they also define__slots__ (which must only contain names of anyadditional slots).
If a class defines a slot also defined in a base class, the instance variabledefined by the base class slot is inaccessible (except by retrieving itsdescriptor directly from the base class). This renders the meaning of theprogram undefined. In the future, a check may be added to prevent this.
Nonempty__slots__ does not work for classes derived from “variable-length”built-in types such aslong,str andtuple.
Any non-string iterable may be assigned to__slots__. Mappings may also beused; however, in the future, special meaning may be assigned to the valuescorresponding to each key.
__class__ assignment works only if both classes have the same__slots__.
Changed in version 2.6:Previously,__class__ assignment raised an error if either new or old classhad__slots__.

3.4.3.Customizing class creation¶

By default, new-style classes are constructed usingtype(). A classdefinition is read into a separate namespace and the value of class name isbound to the result oftype(name,bases,dict).

When the class definition is read, if__metaclass__ is defined then thecallable assigned to it will be called instead oftype(). This allowsclasses or functions to be written which monitor or alter the class creationprocess:

Modifying the class dictionary prior to the class being created.
Returning an instance of another class – essentially performing the role of afactory function.

These steps will have to be performed in the metaclass’s__new__() method–type.__new__() can then be called from this method to create a classwith different properties. This example adds a new element to the classdictionary before creating the class:

classmetacls(type):def__new__(mcs,name,bases,dict):dict['foo']='metacls was here'returntype.__new__(mcs,name,bases,dict)

You can of course also override other class methods (or add new methods); forexample defining a custom__call__() method in the metaclass allows custombehavior when the class is called, e.g. not always creating a new instance.

__metaclass__¶: This variable can be any callable accepting arguments forname,bases,anddict. Upon class creation, the callable is used instead of the built-intype().
New in version 2.2.

The appropriate metaclass is determined by the following precedence rules:

Ifdict['__metaclass__'] exists, it is used.
Otherwise, if there is at least one base class, its metaclass is used (thislooks for a__class__ attribute first and if not found, uses its type).
Otherwise, if a global variable named __metaclass__ exists, it is used.
Otherwise, the old-style, classic metaclass (types.ClassType) is used.

The potential uses for metaclasses are boundless. Some ideas that have beenexplored including logging, interface checking, automatic delegation, automaticproperty creation, proxies, frameworks, and automatic resourcelocking/synchronization.

3.4.4.Customizing instance and subclass checks¶

New in version 2.6.

The following methods are used to override the default behavior of theisinstance() andissubclass() built-in functions.

In particular, the metaclassabc.ABCMeta implements these methods inorder to allow the addition of Abstract Base Classes (ABCs) as “virtual baseclasses” to any class or type (including built-in types), including otherABCs.

class.__instancecheck__(self,instance)¶: Return true ifinstance should be considered a (direct or indirect)instance ofclass. If defined, called to implementisinstance(instance,class).

class.__subclasscheck__(self,subclass)¶: Return true ifsubclass should 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. Theycannot be defined as class methods in the actual class. This is consistent withthe lookup of special methods that are called on instances, only in thiscase the instance is itself a class.

3.4.5.Emulating callable objects¶

object.__call__(self[,args...])¶: Called when the instance is “called” as a function; if this method is defined,x(arg1,arg2,...) is a shorthand forx.__call__(arg1,arg2,...).

3.4.6.Emulating container types¶

The following methods can be defined to implement container objects. Containersusually are sequences (such as lists or tuples) or mappings (like dictionaries),but can represent other containers as well. The first set of methods is usedeither to emulate a sequence or to emulate a mapping; the difference is that fora sequence, the allowable keys should be the integersk for which0<=k<N whereN is the length of the sequence, or slice objects, which define arange of items. (For backwards compatibility, the method__getslice__()(see below) can also be defined to handle simple, but not extended slices.) Itis also recommended that mappings provide the methodskeys(),values(),items(),has_key(),get(),clear(),setdefault(),iterkeys(),itervalues(),iteritems(),pop(),popitem(),copy(), andupdate() behaving similarto those for Python’s standard dictionary objects. TheUserDict moduleprovides aDictMixin class to help create those methods from a base setof__getitem__(),__setitem__(),__delitem__(), andkeys(). Mutable sequences should provide methodsappend(),count(),index(),extend(),insert(),pop(),remove(),reverse() andsort(), like Python standard listobjects. Finally, sequence types should implement addition (meaningconcatenation) and multiplication (meaning repetition) by defining the methods__add__(),__radd__(),__iadd__(),__mul__(),__rmul__() and__imul__() described below; they should not define__coerce__() or other numerical operators. It is recommended that bothmappings and sequences implement the__contains__() method to allowefficient use of thein operator; for mappings,in should be equivalentofhas_key(); for sequences, it should search through the values. It isfurther recommended that both mappings and sequences implement the__iter__() method to allow efficient iteration through the container; formappings,__iter__() should be the same asiterkeys(); forsequences, it should iterate through the values.

object.__len__(self)¶: Called to implement the built-in functionlen(). Should return the lengthof the object, an integer>= 0. Also, an object that doesn’t define a__nonzero__() method and whose__len__() method returns zero isconsidered 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.maxsize some features (such aslen()) may raiseOverflowError. To prevent raisingOverflowError by truth value testing, an object must define a__nonzero__() method.

object.__getitem__(self,key)¶: Called to implement evaluation ofself[key]. For sequence types, theaccepted keys should be integers and slice objects. Note that the specialinterpretation of negative indexes (if the class wishes to emulate a sequencetype) is up to the__getitem__() method. Ifkey is of an inappropriatetype,TypeError may be raised; if of a value outside the set of indexesfor the sequence (after any special interpretation of negative values),IndexError should be raised. For mapping types, ifkey is missing (notin the container),KeyError should be raised.
Note
for loops expect that anIndexError will be raised for illegalindexes to allow proper detection of the end of the sequence.

object.__setitem__(self,key,value)¶: Called to implement assignment toself[key]. Same note as for__getitem__(). This should only be implemented for mappings if theobjects support changes to the values for keys, or if new keys can be added, orfor sequences if elements can be replaced. The same exceptions should be raisedfor improperkey values 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 theobjects support removal of keys, or for sequences if elements can be removedfrom the sequence. The same exceptions should be raised for improperkeyvalues as for the__getitem__() method.

object.__missing__(self,key)¶: Called bydict.__getitem__() to implementself[key] for dict subclasseswhen key is not in the dictionary.

object.__iter__(self)¶

This method is called when an iterator is required for a container. This methodshould return a new iterator object that can iterate over all the objects in thecontainer. For mappings, it should iterate over the keys of the container, andshould also be made available as the methoditerkeys().

Iterator objects also need to implement this method; they are required to returnthemselves. For more information on iterator objects, seeIterator Types.

object.__reversed__(self)¶

Called (if present) by thereversed() built-in to implementreverse iteration. It should return a new iterator object that iteratesover 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 shouldonly provide__reversed__() if they can provide an implementationthat is more efficient than the one provided byreversed().

New in version 2.6.

The membership test operators (in andnotin) are normallyimplemented as an iteration through a sequence. However, container objects cansupply the following special method with a more efficient implementation, whichalso does not require the object be a sequence.

object.__contains__(self,item)¶

Called to implement membership test operators. Should return true ifitemis inself, false otherwise. For mapping objects, this should consider thekeys of the mapping rather than the values or the key-item pairs.

For objects that don’t define__contains__(), the membership test firsttries iteration via__iter__(), then the old sequence iterationprotocol via__getitem__(), seethis section in the languagereference.

3.4.7.Additional methods for emulation of sequence types¶

The following optional methods can be defined to further emulate sequenceobjects. Immutable sequences methods should at most only define__getslice__(); mutable sequences might define all three methods.

object.__getslice__(self,i,j)¶: Deprecated since version 2.0:Support slice objects as parameters to the__getitem__() method.(However, built-in types in CPython currently still implement__getslice__(). Therefore, you have to override it in derivedclasses when implementing slicing.)
Called to implement evaluation ofself[i:j]. The returned object shouldbe of the same type asself. Note that missingi orj in the sliceexpression are replaced by zero orsys.maxsize, respectively. Ifnegative indexes are used in the slice, the length of the sequence is addedto that index. If the instance does not implement the__len__() method,anAttributeError is raised. No guarantee is made that indexesadjusted this way are not still negative. Indexes which are greater than thelength of the sequence are not modified. If no__getslice__() is found,a slice object is created instead, and passed to__getitem__() instead.

object.__setslice__(self,i,j,sequence)¶

Called to implement assignment toself[i:j]. Same notes fori andj asfor__getslice__().

This method is deprecated. If no__setslice__() is found, or for extendedslicing of the formself[i:j:k], a slice object is created, and passed to__setitem__(), instead of__setslice__() being called.

object.__delslice__(self,i,j)¶: Called to implement deletion ofself[i:j]. Same notes fori andj as for__getslice__(). This method is deprecated. If no__delslice__() isfound, or for extended slicing of the formself[i:j:k], a slice object iscreated, and passed to__delitem__(), instead of__delslice__()being called.

Notice that these methods are only invoked when a single slice with a singlecolon is used, and the slice method is available. For slice operationsinvolving extended slice notation, or in absence of the slice methods,__getitem__(),__setitem__() or__delitem__() is called with aslice object as argument.

The following example demonstrate how to make your program or module compatiblewith earlier versions of Python (assuming that methods__getitem__(),__setitem__() and__delitem__() support slice objects asarguments):

classMyClass:...def__getitem__(self,index):...def__setitem__(self,index,value):...def__delitem__(self,index):...ifsys.version_info<(2,0):# They won't be defined if version is at least 2.0 finaldef__getslice__(self,i,j):returnself[max(0,i):max(0,j):]def__setslice__(self,i,j,seq):self[max(0,i):max(0,j):]=seqdef__delslice__(self,i,j):delself[max(0,i):max(0,j):]...

Note the calls tomax(); these are necessary because of the handling ofnegative indices before the__*slice__() methods are called. Whennegative indexes are used, the__*item__() methods receive them asprovided, but the__*slice__() methods get a “cooked” form of the indexvalues. For each negative index value, the length of the sequence is added tothe index before calling the method (which may still result in a negativeindex); this is the customary handling of negative indexes by the built-insequence types, and the__*item__() methods are expected to do this aswell. However, since they should already be doing that, negative indexes cannotbe passed in; they must be constrained to the bounds of the sequence beforebeing passed to the__*item__() methods. Callingmax(0,i)conveniently returns the proper value.

3.4.8.Emulating numeric types¶

The following methods can be defined to emulate numeric objects. Methodscorresponding to operations that are not supported by the particular kind ofnumber implemented (e.g., bitwise operations for non-integral numbers) should beleft undefined.

object.__add__(self,other)¶

object.__sub__(self,other)¶

object.__mul__(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, wherex is an instance of a class that has an__add__()method,x.__add__(y) is called. The__divmod__() method should be theequivalent to using__floordiv__() and__mod__(); it should not berelated to__truediv__() (described below). Note that__pow__()should be defined to accept an optional third argument if the ternary version ofthe built-inpow() function is to be supported.

If one of those methods does not support the operation with the suppliedarguments, it should returnNotImplemented.

object.__div__(self,other)¶
object.__truediv__(self,other)¶: The division operator (/) is implemented by these methods. The__truediv__() method is used when__future__.division is in effect,otherwise__div__() is used. If only one of these two methods is defined,the object will not support division in the alternate context;TypeErrorwill be raised instead.

object.__radd__(self,other)¶

object.__rsub__(self,other)¶

object.__rmul__(self,other)¶

object.__rdiv__(self,other)¶

object.__rtruediv__(self,other)¶

object.__rfloordiv__(self,other)¶

object.__rmod__(self,other)¶

object.__rdivmod__(self,other)¶

object.__rpow__(self,other)¶

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 areonly called if the left operand does not support the corresponding operation andthe operands are of different types.2 For instance, to evaluate theexpressionx-y, wherey is an instance of a class that has an__rsub__() method,y.__rsub__(x) is called ifx.__sub__(y) returnsNotImplemented.

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

Note

If the right operand’s type is a subclass of the left operand’s type and thatsubclass provides the reflected method for the operation, this method will becalled before the left operand’s non-reflected method. This behavior allowssubclasses to override their ancestors’ operations.

object.__iadd__(self,other)¶
object.__isub__(self,other)¶
object.__imul__(self,other)¶
object.__idiv__(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 operationin-place (modifyingself) and return the result (which could be, but doesnot have to be,self). If a specific method is not defined, the augmentedassignment falls back to the normal methods. For instance, to execute thestatementx+=y, wherex is an instance of a class that has an__iadd__() method,x.__iadd__(y) is called. Ifx is an instanceof a class that does not define a__iadd__() method,x.__add__(y)andy.__radd__(x) are considered, as with the evaluation ofx+y.

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.__long__(self)¶
object.__float__(self)¶: Called to implement the built-in functionscomplex(),int(),long(), andfloat(). Should return a value of the appropriate type.

object.__oct__(self)¶
object.__hex__(self)¶: Called to implement the built-in functionsoct() andhex(). Shouldreturn a string value.

object.__index__(self)¶: Called to implementoperator.index(). Also called whenever Python needsan integer object (such as in slicing). Must return an integer (int or long).
New in version 2.5.

object.__coerce__(self,other)¶: Called to implement “mixed-mode” numeric arithmetic. Should either return a2-tuple containingself andother converted to a common numeric type, orNone if conversion is impossible. When the common type would be the type ofother, it is sufficient to returnNone, since the interpreter will alsoask the other object to attempt a coercion (but sometimes, if the implementationof the other type cannot be changed, it is useful to do the conversion to theother type here). A return value ofNotImplemented is equivalent toreturningNone.

3.4.9.Coercion rules¶

This section used to document the rules for coercion. As the language hasevolved, the coercion rules have become hard to document precisely; documentingwhat one version of one particular implementation does is undesirable. Instead,here are some informal guidelines regarding coercion. In Python 3, coercionwill not be supported.

If the left operand of a % operator is a string or Unicode object, no coerciontakes place and the string formatting operation is invoked instead.
It is no longer recommended to define a coercion operation. Mixed-modeoperations on types that don’t define coercion pass the original arguments tothe operation.
New-style classes (those derived fromobject) never invoke the__coerce__() method in response to a binary operator; the only time__coerce__() is invoked is when the built-in functioncoerce() iscalled.
For most intents and purposes, an operator that returnsNotImplemented istreated the same as one that is not implemented at all.
Below,__op__() and__rop__() are used to signify the generic methodnames corresponding to an operator;__iop__() is used for thecorresponding in-place operator. For example, for the operator ‘+’,__add__() and__radd__() are used for the left and right variant ofthe binary operator, and__iadd__() for the in-place variant.
For objectsx andy, firstx.__op__(y) is tried. If this is notimplemented or returnsNotImplemented,y.__rop__(x) is tried. If thisis also not implemented or returnsNotImplemented, aTypeErrorexception is raised. But see the following exception:
Exception to the previous item: if the left operand is an instance of a built-intype or a new-style class, and the right operand is an instance of a propersubclass of that type or class and overrides the base’s__rop__() method,the right operand’s__rop__() method is triedbefore the left operand’s__op__() method.
This is done so that a subclass can completely override binary operators.Otherwise, the left operand’s__op__() method would always accept theright operand: when an instance of a given class is expected, an instance of asubclass of that class is always acceptable.
When either operand type defines a coercion, this coercion is called before thattype’s__op__() or__rop__() method is called, but no sooner. Ifthe coercion returns an object of a different type for the operand whosecoercion is invoked, part of the process is redone using the new object.
When an in-place operator (like ‘+=’) is used, if the left operandimplements__iop__(), it is invoked without any coercion. When theoperation falls back to__op__() and/or__rop__(), the normalcoercion rules apply.
Inx+y, ifx is a sequence that implements sequence concatenation,sequence concatenation is invoked.
Inx*y, if one operand is a sequence that implements sequencerepetition, and the other is an integer (int orlong),sequence repetition is invoked.
Rich comparisons (implemented by methods__eq__() and so on) never usecoercion. Three-way comparison (implemented by__cmp__()) does usecoercion under the same conditions as other binary operations use it.
In the current implementation, the built-in numeric typesint,long,float, andcomplex do not use coercion.All these types implement a__coerce__() method, for use by the built-incoerce() function.
Changed in version 2.7:The complex type no longer makes implicit calls to the__coerce__()method for mixed-type binary arithmetic operations.

3.4.10.With Statement Context Managers¶

New in version 2.5.

Acontext manager is an object that defines the runtime context to beestablished when executing awith statement. The context managerhandles the entry into, and the exit from, the desired runtime context for theexecution of the block of code. Context managers are normally invoked using thewith statement (described in sectionThe with statement), but can also beused by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds ofglobal 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. Thewith statementwill bind this method’s return value to the target(s) specified in theas clause of the statement, if any.

object.__exit__(self,exc_type,exc_value,traceback)¶

Exit the runtime context related to this object. The parameters describe theexception that caused the context to be exited. If the context was exitedwithout 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.

3.4.11.Special method lookup for old-style classes¶

For old-style classes, special methods are always looked up in exactly thesame way as any other method or attribute. This is the case regardless ofwhether the method is being looked up explicitly as inx.__getitem__(i)or implicitly as inx[i].

This behaviour means that special methods may exhibit different behaviourfor different instances of a single old-style class if the appropriatespecial attributes are set differently:

>>>classC:...pass...>>>c1=C()>>>c2=C()>>>c1.__len__=lambda:5>>>c2.__len__=lambda:9>>>len(c1)5>>>len(c2)9

3.4.12.Special method lookup for new-style classes¶

For new-style classes, implicit invocations of special methods are only guaranteedto work correctly if defined on an object’s type, not in the object’s instancedictionary. That behaviour is the reason why the following code raises anexception (unlike the equivalent example with old-style classes):

>>>classC(object):...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 suchas__hash__() and__repr__() that are implemented by all objects,including type objects. If the implicit lookup of these methods used theconventional lookup process, they would fail when invoked on the type objectitself:

>>>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 issometimes referred to as ‘metaclass confusion’, and is avoided by bypassingthe 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 ofcorrectness, 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 instanceClass getattribute invoked10>>>type(c).__len__(c)# Explicit lookup via typeMetaclass getattribute invoked10>>>len(c)# Implicit lookup10

Bypassing the__getattribute__() machinery in this fashionprovides significant scope for speed optimisations within theinterpreter, at the cost of some flexibility in the handling ofspecial methods (the special methodmust be set on the classobject itself in order to be consistently invoked by the interpreter).

Footnotes

1: Itis possible in some cases to change an object’s type, under certaincontrolled conditions. It generally isn’t a good idea though, since it canlead to some very strange behaviour if it is handled incorrectly.
2: For operands of the same type, it is assumed that if the non-reflected method(such as__add__()) fails the operation is not supported, which is why thereflected method is not called.

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3.Data model¶

3.1.Objects, values and types¶

3.2.The standard type hierarchy¶

3.3.New-style and classic classes¶

3.4.Special method names¶

3.4.1.Basic customization¶

3.4.2.Customizing attribute access¶

3.4.2.1.More attribute access for new-style classes¶

3.4.2.2.Implementing Descriptors¶

3.4.2.3.Invoking Descriptors¶

3.4.2.4.slots¶

3.4.3.Customizing class creation¶

3.4.4.Customizing instance and subclass checks¶

3.4.5.Emulating callable objects¶

3.4.6.Emulating container types¶

3.4.7.Additional methods for emulation of sequence types¶

3.4.8.Emulating numeric types¶

3.4.9.Coercion rules¶

3.4.10.With Statement Context Managers¶

3.4.11.Special method lookup for old-style classes¶

3.4.12.Special method lookup for new-style classes¶

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Movatterモバイル変換

3.Data model¶

3.1.Objects, values and types¶

3.2.The standard type hierarchy¶

3.3.New-style and classic classes¶

3.4.Special method names¶

3.4.1.Basic customization¶

3.4.2.Customizing attribute access¶

3.4.2.1.More attribute access for new-style classes¶

3.4.2.2.Implementing Descriptors¶

3.4.2.3.Invoking Descriptors¶

3.4.2.4.__slots__¶

3.4.3.Customizing class creation¶

3.4.4.Customizing instance and subclass checks¶

3.4.5.Emulating callable objects¶

3.4.6.Emulating container types¶

3.4.7.Additional methods for emulation of sequence types¶

3.4.8.Emulating numeric types¶

3.4.9.Coercion rules¶

3.4.10.With Statement Context Managers¶

3.4.11.Special method lookup for old-style classes¶

3.4.12.Special method lookup for new-style classes¶

3.4.2.4.slots¶