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# 3. Data model

## 3.1. Objects, values and types

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

Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘`is`‘ operator compares the identity of two objects; the `id()` function returns an integer representing its identity.

CPython implementation detail: For CPython, `id(x)` is the memory address where `x` is stored.

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

The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.

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

CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the `gc` module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (ex: always close files).

Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a ‘`try`...`except`‘ statement may keep objects alive.

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a `close()` method. Programs are strongly recommended to explicitly close such objects. The ‘`try`...`finally`‘ statement and the ‘`with`‘ statement provide convenient ways to do this.

Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after `a = 1; b = 1`, `a` and `b` may or may not refer to the same object with the value one, depending on the implementation, but after `c = []; d = []`, `c` and `d` are guaranteed to refer to two different, unique, newly created empty lists. (Note that `c = d = []` assigns the same object to both `c` and `d`.)

## 3.2. The standard type hierarchy

Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

Some of the type descriptions below contain a paragraph listing ‘special attributes.’ These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.

None

  This type has a single value. There is a single object with this value. This object is accessed through the built-in name `None`. It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false.

NotImplemented

  This type has a single value. There is a single object with this value. This object is accessed through the built-in name `NotImplemented`. Numeric methods and rich comparison methods may return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) Its truth value is true.

Ellipsis

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

`numbers.Number`

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

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

  `numbers.Integral`

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

    There are two types of integers:

    Integers (`int`)

    > These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2’s complement which gives the illusion of an infinite string of sign bits extending to the left.

    Booleans (`bool`)

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

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

  `numbers.Real` (`float`)

    These represent machine-level double precision floating point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in processor and memory usage that are usually the reason for using these is dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers.

  `numbers.Complex` (`complex`)

    These represent complex numbers as a pair of machine-level double precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary parts of a complex number `z` can be retrieved through the read-only attributes `z.real` and `z.imag`.

Sequences

  These represent finite ordered sets indexed by non-negative numbers. The built-in function `len()` returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, ..., n-1. Item i of sequence a is selected by `a[i]`.

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

  Some sequences also support “extended slicing” with a third “step” parameter: `a[i:j:k]` selects all items of a with index x where `x = i + n*k`, n `>=` `0` and i `<=` x `<` j.

  Sequences are distinguished according to their mutability:

  Immutable sequences

    An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)

    The following types are immutable sequences:

    Strings

      A string is a sequence of values that represent Unicode codepoints. All the codepoints in range `U+0000 - U+10FFFF` can be represented in a string. Python doesn’t have a `chr` type, and every character in the string is represented as a string object with length `1`. The built-in function `ord()` converts a character to its codepoint (as an integer); `chr()` converts an integer in range `0 - 10FFFF` to the corresponding character. `str.encode()` can be used to convert a `str` to `bytes` using the given encoding, and `bytes.decode()` can be used to achieve the opposite.

    Tuples

      The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a ‘singleton’) can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.

    Bytes

      A bytes object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like `b'abc'`) and the built-in function `bytes()` can be used to construct bytes objects. Also, bytes objects can be decoded to strings via the `decode()` method.

  Mutable sequences

    Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and `del` (delete) statements.

    There are currently two intrinsic mutable sequence types:

    Lists

      The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)

    Byte Arrays

      A bytearray object is a mutable array. They are created by the built-in `bytearray()` constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable bytes objects.

    The extension module `array` provides an additional example of a mutable sequence type, as does the `collections` 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, and the built-in function `len()` returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

  For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., `1` and `1.0`), only one of them can be contained in a set.

  There are currently two intrinsic set types:

  Sets

    These represent a mutable set. They are created by the built-in `set()` constructor and can be modified afterwards by several methods, such as `add()`.

  Frozen sets

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

Mappings

  These represent finite sets of objects indexed by arbitrary index sets. The subscript notation `a[k]` selects the item indexed by `k` from the mapping `a`; this can be used in expressions and as the target of assignments or `del` statements. The built-in function `len()` returns the number of items in a mapping.

  There is currently a single intrinsic mapping type:

  Dictionaries

    These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key’s hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., `1` and `1.0`) then they can be used interchangeably to index the same dictionary entry.

    Dictionaries are mutable; they can be created by the `{...}` notation (see section Dictionary displays).

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

Callable types

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

  User-defined functions

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

    Special attributes:

    Attribute | Meaning |
    --- | --- | ---
    `__doc__` | The function’s documentation string, or `None` if unavailable | Writable
    `__name__` | The function’s name | Writable
    `__qualname__` | The function’s qualified name New in version 3.3. | Writable
    `__module__` | The name of the module the function was defined in, or `None` if unavailable. | Writable
    `__defaults__` | A tuple containing default argument values for those arguments that have defaults, or `None` if no arguments have a default value | Writable
    `__code__` | The code object representing the compiled function body. | Writable
    `__globals__` | A reference to the dictionary that holds the function’s global variables — the global namespace of the module in which the function was defined. | Read-only
    `__dict__` | The namespace supporting arbitrary function attributes. | Writable
    `__closure__` | `None` or a tuple of cells that contain bindings for the function’s free variables. | Read-only
    `__annotations__` | A dict containing annotations of parameters. The keys of the dict are the parameter names, and `'return'` for the return annotation, if provided. | Writable
    `__kwdefaults__` | A dict containing defaults for 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, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes. Note that the current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.

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

  Instance methods

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

    Special read-only attributes: `__self__` is the class instance object, `__func__` is the function object; `__doc__` is the method’s documentation (same as `__func__.__doc__`); `__name__` is the method name (same as `__func__.__name__`); `__module__` is the name of the module the method was defined in, or `None` if unavailable.

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

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

    When an instance method object is created by retrieving a user-defined function object from a class via one of its instances, its `__self__` attribute is the instance, and the method object is said to be bound. The new method’s `__func__` attribute is the original function object.

    When a user-defined method object is created by retrieving another method object from a class or instance, the behaviour is the same as for a function object, except that the `__func__` attribute of the new instance is not the original method object but its `__func__` attribute.

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

    When an instance method object is called, the underlying function (`__func__`) is called, inserting the class instance (`__self__`) in front of the argument list. For instance, when `C` is a class which contains a definition for a function `f()`, and `x` is an instance of `C`, calling `x.f(1)` is equivalent to calling `C.f(x, 1)`.

    When an instance method object is derived from a class method object, the “class instance” stored in `__self__` will actually be the class itself, so that calling either `x.f(1)` or `C.f(1)` is equivalent to calling `f(C,1)` where `f` is the underlying function.

    Note that the transformation from function object to instance method object happens each time the attribute is retrieved from the instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non-callable objects) are retrieved without transformation. It is also important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.

  Generator functions

    A function or method which uses the `yield` statement (see section The yield statement) is called a generator function. Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator’s `iterator.__next__()` method will cause the function to execute until it provides a value using the `yield` statement. When the function executes a `return` statement or falls off the end, a `StopIteration` exception is raised and the iterator will have reached the end of the set of values to be returned.

  Built-in functions

    A built-in function object is a wrapper around a C function. Examples of built-in functions are `len()` and `math.sin()` (`math` is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes: `__doc__` is the function’s documentation string, or `None` if unavailable; `__name__` is the function’s name; `__self__` is set to `None` (but see the next item); `__module__` is the name of the module the function was defined in or `None` if unavailable.

  Built-in methods

    This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is `alist.append()`, assuming alist is a list object. In this case, the special read-only attribute `__self__` is set to the object denoted by alist.

  Classes
    Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override `__new__()`. The arguments of the call are passed to `__new__()` and, in the typical case, to `__init__()` to initialize the new instance.
  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 by the import system as invoked either by the `import` statement (see `import`), or by calling functions such as `importlib.import_module()` and built-in `__import__()`. A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the `__globals__` attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., `m.x` is equivalent to `m.__dict__["x"]`. A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).

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

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

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

  Predefined (writable) attributes: `__name__` is the module’s name; `__doc__` is the module’s documentation string, or `None` if unavailable; `__file__` is the pathname of the file from which the module was loaded, if it was loaded from a file. The `__file__` attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter; for extension modules loaded dynamically from a shared library, it is the pathname of the shared library file.

Custom classes

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

  When a class attribute reference (for class `C`, say) would yield a class method object, it is transformed into an instance method object whose `__self__` attributes is `C`. When it would yield a static method object, it is transformed into the object wrapped by the static method object. See section Implementing Descriptors for another way in which attributes retrieved from a class may differ from those actually contained in its `__dict__`.

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

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

  Special attributes: `__name__` is the class name; `__module__` is the module name in which the class was defined; `__dict__` is the dictionary containing the class’s namespace; `__bases__` is a tuple (possibly empty or a singleton) containing the base classes, in the order of their occurrence in the base class list; `__doc__` is the class’s documentation string, or None if undefined.

Class instances

  A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance’s class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose `__self__` attribute is the instance. Static method and class method objects are also transformed; see above under “Classes”. See section Implementing Descriptors for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class’s `__dict__`. If no class attribute is found, and the object’s class has a `__getattr__()` method, that is called to satisfy the lookup.

  Attribute assignments and deletions update the instance’s dictionary, never a class’s dictionary. If the class has a `__setattr__()` or `__delattr__()` method, this is called instead of updating the instance dictionary directly.

  Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names.

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

I/O objects (also known as file objects)

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

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

Internal types

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

  Code objects

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

    Special read-only attributes: `co_name` gives the function name; `co_argcount` is the number of positional arguments (including arguments with default values); `co_nlocals` is the number of local variables used by the function (including arguments); `co_varnames` is a tuple containing the names of the local variables (starting with the argument names); `co_cellvars` is a tuple containing the names of local variables that are referenced by nested functions; `co_freevars` is a tuple containing the names of free variables; `co_code` is a string representing the sequence of bytecode instructions; `co_consts` is a tuple containing the literals used by the bytecode; `co_names` is a tuple containing the names used by the bytecode; `co_filename` is the filename from which the code was compiled; `co_firstlineno` is the first line number of the function; `co_lnotab` is a string encoding the mapping from bytecode offsets to line 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 for `co_flags`: bit `0x04` is set if the function uses the `*arguments` syntax to accept an arbitrary number of positional arguments; bit `0x08` is set if the function uses the `**keywords` syntax to accept arbitrary keyword arguments; bit `0x20` is set if the function is a generator.

    Future feature declarations (`from __future__ import division`) also use bits in `co_flags` to indicate whether a code object was compiled with a particular feature enabled: bit `0x2000` is set if the function was compiled with future division enabled; bits `0x10` and `0x1000` were used in earlier versions of Python.

    Other bits in `co_flags` are reserved for internal use.

    If a code object represents a function, the first item in `co_consts` is the documentation string of the function, or `None` 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), or `None` if this is the bottom stack frame; `f_code` is the code object being executed in this frame; `f_locals` is the dictionary used to look up local variables; `f_globals` is used for global variables; `f_builtins` is used for built-in (intrinsic) names; `f_lasti` gives the precise instruction (this is an index into the bytecode string of the code object).

    Special writable attributes: `f_trace`, if not `None`, is a function called 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 this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to f_lineno.

  Traceback objects

    Traceback objects represent a stack trace of an exception. A traceback object is created when an exception occurs. When the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section The try statement.) It is accessible as the third item of the tuple returned by `sys.exc_info()`. When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as `sys.last_traceback`.

    Special read-only attributes: `tb_next` is the next level in the stack trace (towards the frame where the exception occurred), or `None` if there is no next level; `tb_frame` points to the execution frame of the current level; `tb_lineno` gives the line number where the exception occurred; `tb_lasti` indicates the precise instruction. The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a `try` statement with no matching except clause or with a finally clause.

  Slice objects

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

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

    Slice objects support one method:

    `slice.``indices`(self, length)

      This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.

  Static method objects
    Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are not themselves callable, although the objects they wrap usually are. Static method objects are created by the built-in `staticmethod()` constructor.
  Class method objects
    A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under “User-defined methods”. Class method objects are created by the built-in `classmethod()` constructor.

## 3.3. Special method names

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named `__getitem__()`, and `x` is an instance of this class, then `x[i]` is roughly equivalent to `type(x).__getitem__(x, i)`. Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically `AttributeError` or `TypeError`).

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

### 3.3.1. Basic customization

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

  Called to create a new instance of class cls. `__new__()` is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of `__new__()` should be the new object instance (usually an instance of cls).

  Typical implementations create a new instance of the class by invoking the superclass’s `__new__()` method using `super(currentclass, cls).__new__(cls[, ...])` with appropriate arguments and then modifying the newly-created instance as necessary before returning it.

  If `__new__()` returns an instance of cls, then the new instance’s `__init__()` method will be invoked like `__init__(self[, ...])`, where self is the new instance and the remaining arguments are the same as were passed to `__new__()`.

  If `__new__()` does not return an instance of cls, then the new instance’s `__init__()` method will not be invoked.

  `__new__()` is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.

`object.``__init__`(self[, ...])

  Called after the instance has been created (by `__new__()`), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an `__init__()` method, the derived class’s `__init__()` method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: `BaseClass.__init__(self, [args...])`.

  Because `__new__()` and `__init__()` work together in constructing objects (`__new__()` to create it, and `__init__()` to customise it), no non-`None` value may be returned by `__init__()`; doing so will cause a `TypeError` to be raised at runtime.

`object.``__del__`(self)

  Called when the instance is about to be destroyed. This is also called a destructor. If a base class has a `__del__()` method, the derived class’s `__del__()` method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance. Note that it is possible (though not recommended!) for the `__del__()` method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. It is not guaranteed that `__del__()` methods are called for objects that still exist when the interpreter exits.

  Note

  `del x` doesn’t directly call `x.__del__()` — the former decrements the reference count for `x` by one, and the latter is only called when `x`‘s reference count reaches zero. Some common situations that may prevent the reference count of an object from going to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in `sys.exc_info()[2]` keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in `sys.last_traceback` keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing `None` in `sys.last_traceback`. Circular references which are garbage are detected 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 the `gc` module for more information about how `__del__()` methods are handled by the cycle detector, particularly the description of the `garbage` value.

  Warning

  Due to the precarious circumstances under which `__del__()` methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to `sys.stderr` instead. Also, when `__del__()` is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the `__del__()` method may already have been deleted or in the process of being torn down (e.g. the import machinery shutting down). For this reason, `__del__()` methods should do the absolute minimum needed to maintain external invariants. Starting with version 1.5, Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the `__del__()` method is called.

`object.``__repr__`(self)

  Called by the `repr()` built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form `<...some useful description...>` should be returned. The return value must be a string object. If a class defines `__repr__()` but not `__str__()`, then `__repr__()` is also used when an “informal” string representation of instances of that class is required.

  This is typically used for debugging, so it is important that the representation is information-rich and unambiguous.

`object.``__str__`(self)

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

  This method differs from `object.__repr__()` in that there is no expectation that `__str__()` return a valid Python expression: a more convenient or concise representation can be used.

  The default implementation defined by the built-in type `object` calls `object.__repr__()`.

`object.``__bytes__`(self)

  Called by `bytes()` to compute a byte-string representation of an object. This should return a `bytes` object.

`object.``__format__`(self, format_spec)

  Called by the `format()` built-in function (and by extension, the `str.format()` method of class `str`) to produce a “formatted” string representation of an object. The `format_spec` argument is a string that contains a description of the formatting options desired. The interpretation of the `format_spec` argument is up to the type implementing `__format__()`, however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.

  See Format Specification Mini-Language for a description of the standard formatting syntax.

  The return value must be a string object.

`object.``__lt__`(self, other)
`object.``__le__`(self, other)
`object.``__eq__`(self, other)
`object.``__ne__`(self, other)
`object.``__gt__`(self, other)
`object.``__ge__`(self, other)

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

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

  There are no implied relationships among the comparison operators. The truth of `x==y` does not imply that `x!=y` is false. Accordingly, when defining `__eq__()`, one should also define `__ne__()` so that the operators will behave as expected. See the paragraph on `__hash__()` for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys.

  There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, `__lt__()` and `__gt__()` are each other’s reflection, `__le__()` and `__ge__()` are each other’s reflection, and `__eq__()` and `__ne__()` are their own reflection.

  Arguments to rich comparison methods are never coerced.

  To automatically generate ordering operations from a single root operation, see `functools.total_ordering()`.

`object.``__hash__`(self)

  Called by built-in function `hash()` and for operations on members of hashed collections including `set`, `frozenset`, and `dict`. `__hash__()` should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to somehow mix together (e.g. using exclusive or) the hash values for the components of the object that also play a part in comparison of objects.

  Note

  `hash()` truncates the value returned from an object’s custom `__hash__()` method to the size of a `Py_ssize_t`. This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s `__hash__()` must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with `python -c "import sys; print(sys.hash_info.width)"`

  If a class does not define an `__eq__()` method it should not define a `__hash__()` operation either; if it defines `__eq__()` but not `__hash__()`, its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an `__eq__()` method, it should not implement `__hash__()`, since the implementation of hashable collections requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).

  User-defined classes have `__eq__()` and `__hash__()` methods by default; with them, all objects compare unequal (except with themselves) and `x.__hash__()` returns an appropriate value such that `x == y` implies both that `x is y` and `hash(x) == hash(y)`.

  A class that overrides `__eq__()` and does not define `__hash__()` will have its `__hash__()` implicitly set to `None`. When the `__hash__()` method of a class is `None`, instances of the class will raise an appropriate `TypeError` when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking `isinstance(obj, collections.Hashable`).

  If a class that overrides `__eq__()` needs to retain the implementation of `__hash__()` from a parent class, the interpreter must be told this explicitly by setting `__hash__ = <ParentClass>.__hash__`.

  If a class that does not override `__eq__()` wishes to suppress hash support, it should include `__hash__ = None` in the class definition. A class which defines its own `__hash__()` that explicitly raises a `TypeError` would be incorrectly identified as hashable by an `isinstance(obj, collections.Hashable)` call.

  Note

  By default, the `__hash__()` values of str, bytes and datetime objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

  This is intended to provide protection against a denial-of-service caused by carefully-chosen inputs that exploit the worst case performance of a dict insertion, O(n^2) complexity. See http://www.ocert.org/advisories/ocert-2011-003.html for details.

  Changing hash values affects the iteration order of dicts, sets and other mappings. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).

  See also `PYTHONHASHSEED`.

  Changed in version 3.3: Hash randomization is enabled by default.

`object.``__bool__`(self)

  Called to implement truth value testing and the built-in operation `bool()`; should return `False` or `True`. When this method is not defined, `__len__()` is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither `__len__()` nor `__bool__()`, all its instances are considered true.

### 3.3.2. Customizing attribute access

The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of `x.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 for `self`). `name` is the attribute name. This method should return the (computed) attribute value or raise an `AttributeError` 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 efficiency reasons and because otherwise `__getattr__()` would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the `__getattribute__()` method below for a way to actually get total control over attribute access.

`object.``__getattribute__`(self, name)

  Called unconditionally to implement attribute accesses for instances of the class. If the class also defines `__getattr__()`, the latter will not be called unless `__getattribute__()` either calls it explicitly or raises an `AttributeError`. This method should return the (computed) attribute value or raise an `AttributeError` exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, `object.__getattribute__(self, name)`.

  Note

  This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup.

`object.``__setattr__`(self, name, value)

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

  If `__setattr__()` wants to assign to an instance attribute, it should call the base class method with the same name, for example, `object.__setattr__(self, name, value)`.

`object.``__delattr__`(self, name)

  Like `__setattr__()` but for attribute deletion instead of assignment. This should only be implemented if `del obj.name` is meaningful for the object.

`object.``__dir__`(self)

  Called when `dir()` is called on the object. A sequence must be returned. `dir()` converts the returned sequence to a list and sorts it.

#### 3.3.2.1. Implementing Descriptors

The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ `__dict__`.

`object.``__get__`(self, instance, owner)

  Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). owner is always the owner class, while instance is the instance that the attribute was accessed through, or `None` when the attribute is accessed through the owner. This method should return the (computed) attribute value or raise an `AttributeError` exception.

`object.``__set__`(self, instance, value)

  Called to set the attribute on an instance instance of the owner class to a new value, value.

`object.``__delete__`(self, instance)

  Called to delete the attribute on an instance instance of the owner class.

#### 3.3.2.2. Invoking Descriptors

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: `__get__()`, `__set__()`, and `__delete__()`. If any of those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, `a.x` has a lookup chain starting with `a.__dict__['x']`, then `type(a).__dict__['x']`, and continuing through the base classes of `type(a)` excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called.

The starting point for descriptor invocation is a binding, `a.x`. How the arguments are assembled depends on `a`:

Direct Call
  The simplest and least common call is when user code directly invokes a descriptor method: `x.__get__(a)`.
Instance Binding
  If binding to an object instance, `a.x` is transformed into the call: `type(a).__dict__['x'].__get__(a, type(a))`.
Class Binding
  If binding to a class, `A.x` is transformed into the call: `A.__dict__['x'].__get__(None, A)`.
Super Binding
  If `a` is an instance of `super`, then the binding `super(B, obj).m()` searches `obj.__class__.__mro__` for the base class `A` immediately preceding `B` 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 the which descriptor methods are defined. A descriptor can define any combination of `__get__()`, `__set__()` and `__delete__()`. If it does not define `__get__()`, then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines `__set__()` and/or `__delete__()`, it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both `__get__()` and `__set__()`, while non-data descriptors have just the `__get__()` method. Data descriptors with `__set__()` and `__get__()` defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances.

Python methods (including `staticmethod()` and `classmethod()`) are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class.

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

#### 3.3.2.3. __slots__

By default, instances of classes have a dictionary for attribute storage. This wastes space for objects having very few instance variables. The space consumption can become acute when creating large numbers of instances.

The default can be overridden by defining __slots__ in a class definition. The __slots__ declaration takes a sequence of instance variables and reserves just enough space in each instance to hold a value for each variable. Space is saved because __dict__ is not created for each instance.

`object.``__slots__`

  This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. If defined in a class, __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance.

##### 3.3.2.3.1. Notes on using __slots__

- When inheriting from a class without __slots__, the __dict__ attribute of that class will always be accessible, so a __slots__ definition in the subclass is meaningless.
- Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises `AttributeError`. If dynamic assignment of new variables is desired, then add `'__dict__'` to the sequence of strings in the __slots__ declaration.
- Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add `'__weakref__'` to the sequence of strings in the __slots__ declaration.
- __slots__ are implemented at the class level by creating descriptors (Implementing Descriptors) for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.
- The action of a __slots__ declaration is limited to the class where it is defined. As a result, subclasses will have a __dict__ unless they also define __slots__ (which must only contain names of any additional slots).
- If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.
- Nonempty __slots__ does not work for classes derived from “variable-length” built-in types such as `int`, `bytes` and `tuple`.
- Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key.
- __class__ assignment works only if both classes have the same __slots__.

### 3.3.3. Customizing class creation

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

The class creation process can be customised by passing the `metaclass` keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, both `MyClass` and `MySubclass` are instances of `Meta`:

    class Meta(type):
        pass

    class MyClass(metaclass=Meta):
        pass

    class MySubclass(MyClass):
        pass

Any other keyword arguments that are specified in the class definition are passed through to all metaclass operations described below.

When a class definition is executed, the following steps occur:

- the appropriate metaclass is determined
- the class namespace is prepared
- the class body is executed
- the class object is created

#### 3.3.3.1. Determining the appropriate metaclass

The appropriate metaclass for a class definition is determined as follows:

- if no bases and no explicit metaclass are given, then `type()` is used
- if an explicit metaclass is given and it is not an instance of `type()`, then it is used directly as the metaclass
- if an instance of `type()` is given as the explicit metaclass, or bases are defined, then the most derived metaclass is used

The most derived metaclass is selected from the explicitly specified metaclass (if any) and the metaclasses (i.e. `type(cls)`) of all specified base classes. The most derived metaclass is one which is a subtype of all of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail with `TypeError`.

#### 3.3.3.2. Preparing the class namespace

Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a `__prepare__` attribute, it is called as `namespace = metaclass.__prepare__(name, bases, **kwds)` (where the additional keyword arguments, if any, come from the class definition).

If the metaclass has no `__prepare__` attribute, then the class namespace is initialised as an empty `dict()` instance.

See also

PEP 3115 - Metaclasses in Python 3000
  Introduced the `__prepare__` namespace hook

#### 3.3.3.3. Executing the class body

The class body is executed (approximately) as `exec(body, globals(), namespace)`. The key difference from a normal call to `exec()` is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function.

However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, and cannot be accessed at all from static methods.

#### 3.3.3.4. Creating the class object

Once the class namespace has been populated by executing the class body, the class object is created by calling `metaclass(name, bases, namespace, **kwds)` (the additional keywords passed here are the same as those passed to `__prepare__`).

This class object is the one that will be referenced by the zero-argument form of `super()`. `__class__` is an implicit closure reference created by the compiler if any methods in a class body refer to either `__class__` or `super`. This allows the zero argument form of `super()` to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method.

After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class.

See also

PEP 3135 - New super
  Describes the implicit `__class__` closure reference

#### 3.3.3.5. Metaclass example

The potential uses for metaclasses are boundless. Some ideas that have been explored include logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.

Here is an example of a metaclass that uses an `collections.OrderedDict` to remember the order that class members were defined:

    class OrderedClass(type):

         @classmethod
         def __prepare__(metacls, name, bases, **kwds):
            return collections.OrderedDict()

         def __new__(cls, name, bases, namespace, **kwds):
            result = type.__new__(cls, name, bases, dict(namespace))
            result.members = tuple(namespace)
            return result

    class A(metaclass=OrderedClass):
        def one(self): pass
        def two(self): pass
        def three(self): pass
        def four(self): pass

    >>> A.members
    ('__module__', 'one', 'two', 'three', 'four')

When the class definition for A gets executed, the process begins with calling the metaclass’s `__prepare__()` method which returns an empty `collections.OrderedDict`. That mapping records the methods and attributes of A as they are defined within the body of the class statement. Once those definitions are executed, the ordered dictionary is fully populated and the metaclass’s `__new__()` method gets invoked. That method builds the new type and it saves the ordered dictionary keys in an attribute called `members`.

### 3.3.4. Customizing instance and subclass checks

The following methods are used to override the default behavior of the `isinstance()` and `issubclass()` built-in functions.

In particular, the metaclass `abc.ABCMeta` implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.

`class.``__instancecheck__`(self, instance)

  Return true if instance should be considered a (direct or indirect) instance of class. If defined, called to implement `isinstance(instance, class)`.

`class.``__subclasscheck__`(self, subclass)

  Return true if subclass should be considered a (direct or indirect) subclass of class. If defined, called to implement `issubclass(subclass, class)`.

Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.

See also

PEP 3119 - Introducing Abstract Base Classes
  Includes the specification for customizing `isinstance()` and `issubclass()` behavior through `__instancecheck__()` and `__subclasscheck__()`, with motivation for this functionality in the context of adding Abstract Base Classes (see the `abc` module) to the language.

### 3.3.5. 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 for `x.__call__(arg1, arg2, ...)`.

### 3.3.6. Emulating container types

The following methods can be defined to implement container objects. Containers usually are sequences (such as lists or tuples) or mappings (like dictionaries), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which `0 <= k < N` where N is the length of the sequence, or slice objects, which define a range of items. It is also recommended that mappings provide the methods `keys()`, `values()`, `items()`, `get()`, `clear()`, `setdefault()`, `pop()`, `popitem()`, `copy()`, and `update()` behaving similar to those for Python’s standard dictionary objects. The `collections` module provides a `MutableMapping` abstract base class to help create those methods from a base set of `__getitem__()`, `__setitem__()`, `__delitem__()`, and `keys()`. Mutable sequences should provide methods `append()`, `count()`, `index()`, `extend()`, `insert()`, `pop()`, `remove()`, `reverse()` and `sort()`, like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods `__add__()`, `__radd__()`, `__iadd__()`, `__mul__()`, `__rmul__()` and `__imul__()` described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the `__contains__()` method to allow efficient use of the `in` operator; for mappings, `in` should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the `__iter__()` method to allow efficient iteration through the container; for mappings, `__iter__()` should be the same as `keys()`; for sequences, it should iterate through the values.

`object.``__len__`(self)

  Called to implement the built-in function `len()`. Should return the length of the object, an integer `>=` 0. Also, an object that doesn’t define a `__bool__()` method and whose `__len__()` method returns zero is considered to be false in a Boolean context.

Note

Slicing is done exclusively with the following three methods. A call like

    a[1:2] = b

is translated to

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

and so forth. Missing slice items are always filled in with `None`.

`object.``__getitem__`(self, key)

  Called to implement evaluation of `self[key]`. For sequence types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a sequence type) is up to the `__getitem__()` method. If key is of an inappropriate type, `TypeError` may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), `IndexError` should be raised. For mapping types, if key is missing (not in the container), `KeyError` should be raised.

  Note

  `for` loops expect that an `IndexError` will be raised for illegal indexes to allow proper detection of the end of the sequence.

`object.``__setitem__`(self, key, value)

  Called to implement assignment to `self[key]`. Same note as for `__getitem__()`. This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the `__getitem__()` method.

`object.``__delitem__`(self, key)

  Called to implement deletion of `self[key]`. Same note as for `__getitem__()`. This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the `__getitem__()` method.

`object.``__iter__`(self)

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

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

`object.``__reversed__`(self)

  Called (if present) by the `reversed()` built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order.

  If the `__reversed__()` method is not provided, the `reversed()` built-in will fall back to using the sequence protocol (`__len__()` and `__getitem__()`). Objects that support the sequence protocol should only provide `__reversed__()` if they can provide an implementation that is more efficient than the one provided by `reversed()`.

The membership test operators (`in` and `not in`) are normally implemented as an iteration through a sequence. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be a sequence.

`object.``__contains__`(self, item)

  Called to implement membership test operators. Should return true if item is in self, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs.

  For objects that don’t define `__contains__()`, the membership test first tries iteration via `__iter__()`, then the old sequence iteration protocol via `__getitem__()`, see this section in the language reference.

### 3.3.7. Emulating numeric types

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

`object.``__add__`(self, other)
`object.``__sub__`(self, other)
`object.``__mul__`(self, other)
`object.``__truediv__`(self, other)
`object.``__floordiv__`(self, other)
`object.``__mod__`(self, other)
`object.``__divmod__`(self, other)
`object.``__pow__`(self, other[, modulo])
`object.``__lshift__`(self, other)
`object.``__rshift__`(self, other)
`object.``__and__`(self, other)
`object.``__xor__`(self, other)
`object.``__or__`(self, other)

  These methods are called to implement the binary arithmetic operations (`+`, `-`, `*`, `/`, `//`, `%`, `divmod()`, `pow()`, `**`, `<<`, `>>`, `&`, `^`, `|`). For instance, to evaluate the expression `x + y`, where x is an instance of a class that has an `__add__()` method, `x.__add__(y)` is called. The `__divmod__()` method should be the equivalent to using `__floordiv__()` and `__mod__()`; it should not be related to `__truediv__()`. Note that `__pow__()` should be defined to accept an optional third argument if the ternary version of the built-in `pow()` function is to be supported.

  If one of those methods does not support the operation with the supplied arguments, it should return `NotImplemented`.

`object.``__radd__`(self, other)
`object.``__rsub__`(self, other)
`object.``__rmul__`(self, other)
`object.``__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 are only called if the left operand does not support the corresponding operation and the operands are of different types. [2] For instance, to evaluate the expression `x - y`, where y is an instance of a class that has an `__rsub__()` method, `y.__rsub__(x)` is called if `x.__sub__(y)` returns NotImplemented.

  Note that ternary `pow()` will not try calling `__rpow__()` (the coercion rules would become too complicated).

  Note

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

`object.``__iadd__`(self, other)
`object.``__isub__`(self, other)
`object.``__imul__`(self, other)
`object.``__itruediv__`(self, other)
`object.``__ifloordiv__`(self, other)
`object.``__imod__`(self, other)
`object.``__ipow__`(self, other[, modulo])
`object.``__ilshift__`(self, other)
`object.``__irshift__`(self, other)
`object.``__iand__`(self, other)
`object.``__ixor__`(self, other)
`object.``__ior__`(self, other)

  These methods are called to implement the augmented arithmetic assignments (`+=`, `-=`, `*=`, `/=`, `//=`, `%=`, `**=`, `<<=`, `>>=`, `&=`, `^=`, `|=`). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an `__iadd__()` method, `x += y` is equivalent to `x = x.__iadd__(y)` . Otherwise, `x.__add__(y)` and `y.__radd__(x)` are considered, as with the evaluation of `x + y`. In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += [‘item’] raise an exception when the addition works?), but this behavior is in fact part of the data model.

`object.``__neg__`(self)
`object.``__pos__`(self)
`object.``__abs__`(self)
`object.``__invert__`(self)

  Called to implement the unary arithmetic operations (`-`, `+`, `abs()` and `~`).

`object.``__complex__`(self)
`object.``__int__`(self)
`object.``__float__`(self)
`object.``__round__`(self[, n])

  Called to implement the built-in functions `complex()`, `int()`, `float()` and `round()`. Should return a value of the appropriate type.

`object.``__index__`(self)

  Called to implement `operator.index()`. Also called whenever Python needs an integer object (such as in slicing, or in the built-in `bin()`, `hex()` and `oct()` functions). Must return an integer.

### 3.3.8. With Statement Context Managers

A context manager is an object that defines the runtime context to be established when executing a `with` statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the `with` statement (described in section The with statement), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc.

For more information on context managers, see Context Manager Types.

`object.``__enter__`(self)

  Enter the runtime context related to this object. The `with` statement will bind this method’s return value to the target(s) specified in the `as` clause of the statement, if any.

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

  Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be `None`.

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

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

See also

PEP 0343 - The “with” statement
  The specification, background, and examples for the Python `with` statement.

### 3.3.9. Special method lookup

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

    >>> class C:
    ...     pass
    ...
    >>> c = C()
    >>> c.__len__ = lambda: 5
    >>> len(c)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: object of type 'C' has no len()

The rationale behind this behaviour lies with a number of special methods such as `__hash__()` and `__repr__()` that are implemented by all objects, including type objects. If the implicit lookup of these methods used the conventional lookup process, they would fail when invoked on the type object itself:

    >>> 1 .__hash__() == hash(1)
    True
    >>> int.__hash__() == hash(int)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: descriptor '__hash__' of 'int' object needs an argument

Incorrectly attempting to invoke an unbound method of a class in this way is sometimes referred to as ‘metaclass confusion’, and is avoided by bypassing the instance when looking up special methods:

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

In addition to bypassing any instance attributes in the interest of correctness, implicit special method lookup generally also bypasses the `__getattribute__()` method even of the object’s metaclass:

    >>> class Meta(type):
    ...    def __getattribute__(*args):
    ...       print("Metaclass getattribute invoked")
    ...       return type.__getattribute__(*args)
    ...
    >>> class C(object, metaclass=Meta):
    ...     def __len__(self):
    ...         return 10
    ...     def __getattribute__(*args):
    ...         print("Class getattribute invoked")
    ...         return object.__getattribute__(*args)
    ...
    >>> c = C()
    >>> c.__len__()                 # Explicit lookup via instance
    Class getattribute invoked
    10
    >>> type(c).__len__(c)          # Explicit lookup via type
    Metaclass getattribute invoked
    10
    >>> len(c)                      # Implicit lookup
    10

Bypassing the `__getattribute__()` machinery in this fashion provides significant scope for speed optimisations within the interpreter, at the cost of some flexibility in the handling of special methods (the special method must be set on the class object itself in order to be consistently invoked by the interpreter).

Footnotes

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

[2] | 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 the reflected method is not called.

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