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Data model
**********

Objects, values and types
=========================

*Objects* are Python’s abstraction for data. All data in a Python
program is represented by objects or by relations between objects. (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 (so
you should always close files explicitly).

Note that the use of the implementation’s tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with 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".)

The standard type hierarchy
===========================

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

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

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

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

See Implementing the arithmetic operations for more details.

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

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

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 are
dwarfed by the overhead of using objects in Python, so there is
no reason to complicate the language with two kinds of floating
point numbers.

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

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

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 code
points. All the code points in the range "U+0000 - U+10FFFF"
can be represented in a string. Python doesn’t have a "char"
type; instead, every code point in the string is represented
as a string object with length "1". The built-in function
"ord()" converts a code point from its string form to an
integer in the range "0 - 10FFFF"; "chr()" converts an
integer in the range "0 - 10FFFF" to the corresponding length
"1" string object. "str.encode()" can be used to convert a
"str" to "bytes" using the given text encoding, and
"bytes.decode()" can be used to achieve the opposite.

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

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

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.

Coroutine functions
A function or method which is defined using "async def" is
called a *coroutine function*. Such a function, when called,
returns a *coroutine* object. It may contain "await"
expressions, as well as "async with" and "async for" statements.
See also the Coroutine Objects section.

Asynchronous generator functions
A function or method which is defined using "async def" and
which uses the "yield" statement is called a *asynchronous
generator function*. Such a function, when called, returns an
asynchronous iterator object which can be used in an "async for"
statement to execute the body of the function.

Calling the asynchronous iterator’s "aiterator.__anext__()"
method will return an *awaitable* which when awaited will
execute until it provides a value using the "yield" expression.
When the function executes an empty "return" statement or falls
off the end, a "StopAsyncIteration" exception is raised and the
asynchronous iterator will have reached the end of the set of
values to be yielded.

Built-in functions
A built-in function object is a wrapper around a C function.
Examples of built-in functions are "len()" and "math.sin()"
("math" is a standard built-in module). The number and type of
the arguments are determined by the C function. Special read-
only attributes: "__doc__" is the function’s documentation
string, or "None" if unavailable; "__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".

Predefined (writable) attributes: "__name__" is the module’s name;
"__doc__" is the module’s documentation string, or "None" if
unavailable; "__annotations__" (optional) is a dictionary
containing *variable annotations* collected during module body
execution; "__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.

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.

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
https://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
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; "__annotations__" (optional) is a
dictionary containing *variable annotations* collected during class
body execution.

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.

Frame objects support one method:

frame.clear()

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

"RuntimeError" is raised if the frame is currently executing.

New in version 3.4.

Traceback objects
Traceback objects represent a stack trace of an exception. A
traceback 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.

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").

Setting a special method to "None" indicates that the corresponding
operation is not available. For example, if a class sets "__iter__()"
to "None", the class is not iterable, so calling "iter()" on its
instances will raise a "TypeError" (without falling back to
"__getitem__()"). [2]

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

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().__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:
"super().__init__([args...])".

Because "__new__()" and "__init__()" work together in constructing
objects ("__new__()" to create it, and "__init__()" to customize
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 finalizer or (improperly) a destructor. If a base class
has a "__del__()" method, the derived class’s "__del__()" method,
if any, must explicitly call it to ensure proper deletion of the
base class part of the instance.

It is possible (though not recommended!) for the "__del__()" method
to postpone destruction of the instance by creating a new reference
to it. This is called object *resurrection*. It is
implementation-dependent whether "__del__()" is called a second
time when a resurrected object is about to be destroyed; the
current *CPython* implementation only calls it once.

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.

**CPython implementation detail:** It is possible for a reference
cycle to prevent the reference count of an object from going to
zero. In this case, the cycle will be later detected and deleted
by the *cyclic garbage collector*. A common cause of reference
cycles is when an exception has been caught in a local variable.
The frame’s locals then reference the exception, which references
its own traceback, which references the locals of all frames caught
in the traceback.

See also: Documentation for the "gc" module.

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. In particular:

* "__del__()" can be invoked when arbitrary code is being
executed, including from any arbitrary thread. If "__del__()"
needs to take a lock or invoke any other blocking resource, it
may deadlock as the resource may already be taken by the code
that gets interrupted to execute "__del__()".

* "__del__()" can be executed during interpreter shutdown. As
a consequence, the global variables it needs to access
(including other modules) may already have been deleted or set
to "None". 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,
evaluation of formatted string literals and the "str.format()"
method, 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.

Changed in version 3.4: The __format__ method of "object" itself
raises a "TypeError" if passed any non-empty string.

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.

By default, "__ne__()" delegates to "__eq__()" and inverts the
result unless it is "NotImplemented". There are no other implied
relationships among the comparison operators, for example, the
truth of "(x<y or x==y)" does not imply "x<=y". To automatically
generate ordering operations from a single root operation, see
"functools.total_ordering()".

See the paragraph on "__hash__()" for some important notes on
creating *hashable* objects which support custom comparison
operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used
when the left argument does not support the operation but the right
argument does); rather, "__lt__()" and "__gt__()" are each other’s
reflection, "__le__()" and "__ge__()" are each other’s reflection,
and "__eq__()" and "__ne__()" are their own reflection. If the
operands are of different types, and right operand’s type is a
direct or indirect subclass of the left operand’s type, the
reflected method of the right operand has priority, otherwise the
left operand’s method has priority. Virtual subclassing is not
considered.

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 mix together the hash values of the components of the
object that also play a part in comparison of objects by packing
them into a tuple and hashing the tuple. Example:

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

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.

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 the default attribute access fails with an
"AttributeError" (either "__getattribute__()" raises an
"AttributeError" because *name* is not an instance attribute or an
attribute in the class tree for "self"; or "__get__()" of a *name*
property raises "AttributeError"). This method should either
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.

Customizing module attribute access
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For a more fine grained customization of the module behavior (setting
attributes, properties, etc.), one can set the "__class__" attribute
of a module object to a subclass of "types.ModuleType". For example:

import sys
from types import ModuleType

class VerboseModule(ModuleType):
def __repr__(self):
return f'Verbose {self.__name__}'

def __setattr__(self, attr, value):
print(f'Setting {attr}...')
setattr(self, attr, value)

sys.modules[__name__].__class__ = VerboseModule

Note: Setting module "__class__" only affects lookups made using the
attribute access syntax – directly accessing the module globals
(whether by code within the module, or via a reference to the
module’s globals dictionary) is unaffected.

Changed in version 3.5: "__class__" module attribute is now writable.

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.

object.__set_name__(self, owner, name)

Called at the time the owning class *owner* is created. The
descriptor has been assigned to *name*.

New in version 3.6.

The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).

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.

__slots__
~~~~~~~~~

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

The space saved over using *__dict__* can be significant.

object.__slots__

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

Notes on using *__slots__*
""""""""""""""""""""""""""

* When inheriting from a class without *__slots__*, the *__dict__*
and *__weakref__* attribute of the instances will always be
accessible.

* Without a *__dict__* variable, instances cannot be assigned new
variables not listed in the *__slots__* definition. Attempts to
assign to an unlisted variable name 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 not limited to the
class where it is defined. *__slots__* declared in parents are
available in child classes. However, child subclasses will get a
*__dict__* and *__weakref__* unless they also define *__slots__*
(which should only contain names of 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__*.

* Multiple inheritance with multiple slotted parent classes can be
used, but only one parent is allowed to have attributes created by
slots (the other bases must have empty slot layouts) - violations
raise "TypeError".

Customizing class creation
--------------------------

Whenever a class inherits from another class, *__init_subclass__* is
called on that class. This way, it is possible to write classes which
change the behavior of subclasses. This is closely related to class
decorators, but where class decorators only affect the specific class
they’re applied to, "__init_subclass__" solely applies to future
subclasses of the class defining the method.

classmethod object.__init_subclass__(cls)

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

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

class Philosopher:
def __init_subclass__(cls, default_name, **kwargs):
super().__init_subclass__(**kwargs)
cls.default_name = default_name

class AustralianPhilosopher(Philosopher, default_name="Bruce"):
pass

The default implementation "object.__init_subclass__" does nothing,
but raises an error if it is called with any arguments.

Note: The metaclass hint "metaclass" is consumed by the rest of
the type machinery, and is never passed to "__init_subclass__"
implementations. The actual metaclass (rather than the explicit
hint) can be accessed as "type(cls)".

New in version 3.6.

Metaclasses
~~~~~~~~~~~

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

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

class Meta(type):
pass

class MyClass(metaclass=Meta):
pass

class MySubclass(MyClass):
pass

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

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

* the appropriate metaclass is determined

* the class namespace is prepared

* the class body is executed

* the class object is created

Determining the appropriate metaclass
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

* if no bases and no explicit metaclass are given, then "type()" is
used

* if an explicit metaclass is given and it is *not* an instance of
"type()", then it is used directly as the metaclass

* if an instance of "type()" is given as the explicit metaclass, or
bases are defined, then the most derived metaclass is used

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

Preparing the class namespace
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

If the metaclass has no "__prepare__" attribute, then the class
namespace is initialised as an empty ordered mapping.

See also:

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

Executing the class body
~~~~~~~~~~~~~~~~~~~~~~~~

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

However, even when the class definition occurs inside the function,
methods defined inside the class still cannot see names defined at the
class scope. Class variables must be accessed through the first
parameter of instance or class methods, or through the implicit
lexically scoped "__class__" reference described in the next section.

Creating the class object
~~~~~~~~~~~~~~~~~~~~~~~~~

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

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

**CPython implementation detail:** In CPython 3.6 and later, the
"__class__" cell is passed to the metaclass as a "__classcell__" entry
in the class namespace. If present, this must be propagated up to the
"type.__new__" call in order for the class to be initialised
correctly. Failing to do so will result in a "DeprecationWarning" in
Python 3.6, and a "RuntimeWarning" in the future.

When using the default metaclass "type", or any metaclass that
ultimately calls "type.__new__", the following additional
customisation steps are invoked after creating the class object:

* first, "type.__new__" collects all of the descriptors in the class
namespace that define a "__set_name__()" method;

* second, all of these "__set_name__" methods are called with the
class being defined and the assigned name of that particular
descriptor; and

* finally, the "__init_subclass__()" hook is called on the immediate
parent of the new class in its method resolution order.

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

When a new class is created by "type.__new__", the object provided as
the namespace parameter is copied to a new ordered mapping and the
original object is discarded. The new copy is wrapped in a read-only
proxy, which becomes the "__dict__" attribute of the class object.

See also:

**PEP 3135** - New super
Describes the implicit "__class__" closure reference

Metaclass example
~~~~~~~~~~~~~~~~~

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

Here is an example of a metaclass that uses an
"collections.OrderedDict" to remember the order that class variables
are 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".

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.

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, ...)".

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.

**CPython implementation detail:** In CPython, the length is
required to be at most "sys.maxsize". If the length is larger than
"sys.maxsize" some features (such as "len()") may raise
"OverflowError". To prevent raising "OverflowError" by truth value
testing, an object must define a "__bool__()" method.

object.__length_hint__(self)

Called to implement "operator.length_hint()". Should return an
estimated length for the object (which may be greater or less than
the actual length). The length must be an integer ">=" 0. This
method is purely an optimization and is never required for
correctness.

New in version 3.4.

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

a[1:2] = b

is translated to

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

and so forth. Missing slice items are always filled in 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.__missing__(self, key)

Called by "dict"."__getitem__()" to implement "self[key]" for dict
subclasses when key is not in the dictionary.

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.

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.

Emulating numeric types
-----------------------

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

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

These methods are called to implement the binary arithmetic
operations ("+", "-", "*", "@", "/", "//", "%", "divmod()",
"pow()", "**", "<<", ">>", "&", "^", "|"). For instance, to
evaluate the 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.__rmatmul__(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 [3] and the operands are of
different types. [4] 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.__imatmul__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)

These methods are called to implement the augmented arithmetic
assignments ("+=", "-=", "*=", "@=", "/=", "//=", "%=", "**=",
"<<=", ">>=", "&=", "^=", "|="). These methods should attempt to
do the operation in-place (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)

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

object.__index__(self)

Called to implement "operator.index()", and whenever Python needs
to losslessly convert the numeric object to an integer object (such
as in slicing, or in the built-in "bin()", "hex()" and "oct()"
functions). Presence of this method indicates that the numeric
object is an integer type. Must return an integer.

Note: In order to have a coherent integer type class, when
"__index__()" is defined "__int__()" should also be defined, and
both should return the same value.

object.__round__(self[, ndigits])
object.__trunc__(self)
object.__floor__(self)
object.__ceil__(self)

Called to implement the built-in function "round()" and "math"
functions "trunc()", "floor()" and "ceil()". Unless *ndigits* is
passed to "__round__()" all these methods should return the value
of the object truncated to an "Integral" (typically an "int").

If "__int__()" is not defined then the built-in function "int()"
falls back to "__trunc__()".

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 343** - The “with” statement
The specification, background, and examples for the Python "with"
statement.

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).

Coroutines
==========

Awaitable Objects
-----------------

An *awaitable* object generally implements an "__await__()" method.
*Coroutine* objects returned from "async def" functions are awaitable.

Note: The *generator iterator* objects returned from generators
decorated with "types.coroutine()" or "asyncio.coroutine()" are also
awaitable, but they do not implement "__await__()".

object.__await__(self)

Must return an *iterator*. Should be used to implement *awaitable*
objects. For instance, "asyncio.Future" implements this method to
be compatible with the "await" expression.

New in version 3.5.

See also: **PEP 492** for additional information about awaitable
objects.

Coroutine Objects
-----------------

*Coroutine* objects are *awaitable* objects. A coroutine’s execution
can be controlled by calling "__await__()" and iterating over the
result. When the coroutine has finished executing and returns, the
iterator raises "StopIteration", and the exception’s "value" attribute
holds the return value. If the coroutine raises an exception, it is
propagated by the iterator. Coroutines should not directly raise
unhandled "StopIteration" exceptions.

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

Changed in version 3.5.2: It is a "RuntimeError" to await on a
coroutine more than once.

coroutine.send(value)

Starts or resumes execution of the coroutine. If *value* is
"None", this is equivalent to advancing the iterator returned by
"__await__()". If *value* is not "None", this method delegates to
the "send()" method of the iterator that caused the coroutine to
suspend. The result (return value, "StopIteration", or other
exception) is the same as when iterating over the "__await__()"
return value, described above.

coroutine.throw(type[, value[, traceback]])

Raises the specified exception in the coroutine. This method
delegates to the "throw()" method of the iterator that caused the
coroutine to suspend, if it has such a method. Otherwise, the
exception is raised at the suspension point. The result (return
value, "StopIteration", or other exception) is the same as when
iterating over the "__await__()" return value, described above. If
the exception is not caught in the coroutine, it propagates back to
the caller.

coroutine.close()

Causes the coroutine to clean itself up and exit. If the coroutine
is suspended, this method first delegates to the "close()" method
of the iterator that caused the coroutine to suspend, if it has
such a method. Then it raises "GeneratorExit" at the suspension
point, causing the coroutine to immediately clean itself up.
Finally, the coroutine is marked as having finished executing, even
if it was never started.

Coroutine objects are automatically closed using the above process
when they are about to be destroyed.

Asynchronous Iterators
----------------------

An *asynchronous iterable* is able to call asynchronous code in its
"__aiter__" implementation, and an *asynchronous iterator* can call
asynchronous code in its "__anext__" method.

Asynchronous iterators can be used in an "async for" statement.

object.__aiter__(self)

Must return an *asynchronous iterator* object.

object.__anext__(self)

Must return an *awaitable* resulting in a next value of the
iterator. Should raise a "StopAsyncIteration" error when the
iteration is over.

An example of an asynchronous iterable object:

class Reader:
async def readline(self):
...

def __aiter__(self):
return self

async def __anext__(self):
val = await self.readline()
if val == b'':
raise StopAsyncIteration
return val

New in version 3.5.

Note: Changed in version 3.5.2: Starting with CPython 3.5.2,
"__aiter__" can directly return *asynchronous iterators*. Returning
an *awaitable* object will result in a
"PendingDeprecationWarning".The recommended way of writing backwards
compatible code in CPython 3.5.x is to continue returning awaitables
from "__aiter__". If you want to avoid the
PendingDeprecationWarning and keep the code backwards compatible,
the following decorator can be used:

import functools
import sys

if sys.version_info < (3, 5, 2):
def aiter_compat(func):
@functools.wraps(func)
async def wrapper(self):
return func(self)
return wrapper
else:
def aiter_compat(func):
return func

Example:

class AsyncIterator:

@aiter_compat
def __aiter__(self):
return self

async def __anext__(self):
...

Starting with CPython 3.6, the "PendingDeprecationWarning" will be
replaced with the "DeprecationWarning". In CPython 3.7, returning an
awaitable from "__aiter__" will result in a "RuntimeError".

Asynchronous Context Managers
-----------------------------

An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its "__aenter__" and "__aexit__" methods.

Asynchronous context managers can be used in an "async with"
statement.

object.__aenter__(self)

This method is semantically similar to the "__enter__()", with only
difference that it must return an *awaitable*.

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

This method is semantically similar to the "__exit__()", with only
difference that it must return an *awaitable*.

An example of an asynchronous context manager class:

class AsyncContextManager:
async def __aenter__(self):
await log('entering context')

async def __aexit__(self, exc_type, exc, tb):
await log('exiting context')

New in version 3.5.

-[ Footnotes ]-

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

[2] The "__hash__()", "__iter__()", "__reversed__()", and
"__contains__()" methods have special handling for this; others
will still raise a "TypeError", but may do so by relying on the
behavior that "None" is not callable.

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

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

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