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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 (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
.)
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 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 nameEllipsis
. 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
andTrue
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.
-
Booleans (
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 attributesz.real
andz.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 bya[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 wherex = 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 achar
type; instead, every code point in the string is represented as a string object with length1
. The built-in functionord()
converts a code point from its string form to an integer in the range0 - 10FFFF
;chr()
converts an integer in the range0 - 10FFFF
to the corresponding length1
string object.str.encode()
can be used to convert astr
tobytes
using the given text encoding, andbytes.decode()
can be used to achieve the opposite. - Tuples
-
The items of a tuple are arbitrary Python objects. Tuples of two 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-inbytes()
constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via thedecode()
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 immutablebytes
objects.
The extension module
array
provides an additional example of a mutable sequence type, as does thecollections
module.
- Set types
-
These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, 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
and1.0
), 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 asadd()
. - 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 byk
from the mappinga
; this can be used in expressions and as the target of assignments ordel
statements. The built-in functionlen()
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
and1.0
) then they can be used interchangeably to index the same dictionary entry.Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.
Dictionaries are mutable; they can be created by the
{...}
notation (see section Dictionary displays).The extension modules
dbm.ndbm
anddbm.gnu
provide additional examples of mapping types, as does thecollections
module.Changed in version 3.7: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.
- 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; not inherited by subclasses.Writable
The function’s name.
Writable
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
The namespace supporting arbitrary function attributes.
Writable
__closure__
None
or a tuple of cells that contain bindings for the function’s free variables. See below for information on thecell_contents
attribute.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.
A cell object has the attribute
cell_contents
. This can be used to get the value of the cell, as well as set the value.Additional information about a function’s definition can be retrieved from its code object; see the description of internal types below. The
cell
type can be accessed in thetypes
module. - 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, orNone
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 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, whenC
is a class which contains a definition for a functionf()
, andx
is an instance ofC
, callingx.f(1)
is equivalent to callingC.f(x, 1)
.When an instance method object is derived from a class method object, the “class instance” stored in
__self__
will actually be the class itself, so that calling eitherx.f(1)
orC.f(1)
is equivalent to callingf(C,1)
wheref
is the underlying function.Note that the transformation from function object to 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’siterator.__next__()
method will cause the function to execute until it provides a value using theyield
statement. When the function executes areturn
statement or falls off the end, aStopIteration
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 containawait
expressions, as well asasync with
andasync for
statements. See also the Coroutine Objects section. - Asynchronous generator functions
-
A function or method which is defined using
async def
and which uses theyield
statement is called a asynchronous generator function. Such a function, when called, returns an asynchronous iterator object which can be used in anasync 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 theyield
expression. When the function executes an emptyreturn
statement or falls off the end, aStopAsyncIteration
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()
andmath.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, orNone
if unavailable;__name__
is the function’s name;__self__
is set toNone
(but see the next item);__module__
is the name of the module the function was defined in orNone
if unavailable. - Built-in methods
-
This is really a different disguise of a built-in function, this time 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, or by calling functions such asimportlib.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 tom.__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 tom.__dict__["x"] = 1
.Predefined (writable) attributes:
__name__
is the module’s name;__doc__
is the module’s documentation string, orNone
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 toC.__dict__["x"]
(although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found 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__
attribute isC
. 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, orNone
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 alsoos.popen()
,os.fdopen()
, and themakefile()
method of socket objects (and perhaps by other functions or methods provided by extension modules).The objects
sys.stdin
,sys.stdout
andsys.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 theio.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 total number of positional arguments (including positional-only arguments and arguments with default values);co_posonlyargcount
is the number of positional-only arguments (including arguments with default values);co_kwonlyargcount
is the number of keyword-only 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;co_flags
is an integer encoding a number of flags for the interpreter.The following flag bits are defined for
co_flags
: bit0x04
is set if the function uses the*arguments
syntax to accept an arbitrary number of positional arguments; bit0x08
is set if the function uses the**keywords
syntax to accept arbitrary keyword arguments; bit0x20
is set if the function is a generator.Future feature declarations (
from __future__ import division
) also use bits inco_flags
to indicate whether a code object was compiled with a particular feature enabled: bit0x2000
is set if the function was compiled with future division enabled; bits0x10
and0x1000
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, orNone
if undefined.
- Frame objects
-
Frame objects represent execution frames. They may occur in traceback objects (see below), and are also passed to registered trace functions.
Special read-only attributes:
f_back
is to the previous stack frame (towards the caller), orNone
if this is the bottom stack frame;f_code
is the code object being executed in this frame;f_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 notNone
, is a function called for various events during code execution (this is used by the debugger). Normally an event is triggered for each new source line - this can be disabled by settingf_trace_lines
toFalse
.Implementations may allow per-opcode events to be requested by setting
f_trace_opcodes
toTrue
. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.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 implicitly created when an exception occurs, and may also be explicitly created by calling
types.TracebackType
.For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section The try statement.) It is accessible as the third item of the tuple returned by
sys.exc_info()
, and as the__traceback__
attribute of the caught exception.When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as
sys.last_traceback
.For explicitly created tracebacks, it is up to the creator of the traceback to determine how the
tb_next
attributes should be linked to form a full stack trace.Special read-only attributes:
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 atry
statement with no matching except clause or with a finally clause.Special writable attribute:
tb_next
is the next level in the stack trace (towards the frame where the exception occurred), orNone
if there is no next level.Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code, and the
tb_next
attribute of existing instances can be updated. - Slice objects
-
Slice objects are used to represent slices for
__getitem__()
methods. They are also created by the built-inslice()
function.Special read-only attributes:
start
is the lower bound;stop
is the upper bound;step
is the step value; each isNone
if omitted. These attributes can have any type.Slice objects support one method:
slice.
indices
( self, length )-
This method takes a single integer 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
).
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.)
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 usingsuper().__new__(cls[, ...])
with appropriate arguments and then modifying the newly-created instance as necessary before returning it.If
__new__()
is invoked during object construction and it returns an instance or subclass 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 the object constructor.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 aTypeError
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 callx.__del__()
— the former decrements the reference count forx
by one, and the latter is only called whenx
’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 tosys.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 toNone
. Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the__del__()
method is called.
object.
__repr__
( self )-
Called by 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 functionsformat()
andprint()
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
callsobject.__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 thestr.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 aTypeError
if passed any non-empty string.Changed in version 3.7:
object.__format__(x, '')
is now equivalent tostr(x)
rather thanformat(str(self), '')
.
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
callsx.__lt__(y)
,x<=y
callsx.__le__(y)
,x==y
callsx.__eq__(y)
,x!=y
callsx.__ne__(y)
,x>y
callsx.__gt__(y)
, andx>=y
callsx.__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
andTrue
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 anif
statement), Python will callbool()
on the value to determine if the result is true or false.By default,
__ne__()
delegates to__eq__()
and inverts the result unless it isNotImplemented
. There are no other implied relationships among the comparison operators, for example, the truth of(x<y or x==y)
does not implyx<=y
. To automatically generate ordering operations from a single root operation, seefunctools.total_ordering()
.See the paragraph on
__hash__()
for some important notes on 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 includingset
,frozenset
, anddict
.__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 aPy_ssize_t
. This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s__hash__()
must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is withpython -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) andx.__hash__()
returns an appropriate value such thatx == y
implies both thatx is y
andhash(x) == hash(y)
.A class that overrides
__eq__()
and does not define__hash__()
will have its__hash__()
implicitly set toNone
. When the__hash__()
method of a class isNone
, instances of the class will raise an appropriateTypeError
when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checkingisinstance(obj, collections.abc.Hashable)
.If a class that overrides
__eq__()
needs to retain the implementation of__hash__()
from a parent class, the interpreter must be told this explicitly by setting__hash__ = <ParentClass>.__hash__
.If a class that does not override
__eq__()
wishes to suppress hash support, it should include__hash__ = None
in the class definition. A class which defines its own__hash__()
that explicitly raises aTypeError
would be incorrectly identified as hashable by anisinstance(obj, collections.abc.Hashable)
call.Note
By default, the
__hash__()
values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.This is intended to provide protection against a denial-of-service caused by carefully-chosen inputs that exploit the worst case performance of a dict insertion, O(n^2) complexity. See http://www.ocert.org/advisories/ocert-2011-003.html for details.
Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).
See 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 returnFalse
orTrue
. 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 the default attribute access fails with an
AttributeError
(either__getattribute__()
raises anAttributeError
because name is not an instance attribute or an attribute in the class tree forself
; or__get__()
of a name property raisesAttributeError
). This method should either return the (computed) attribute value or raise anAttributeError
exception.Note that if the attribute is found through the normal mechanism,
__getattr__()
is not called. (This is an intentional asymmetry between__getattr__()
and__setattr__()
.) This is done both for 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 anAttributeError
. This method should return the (computed) attribute value or raise anAttributeError
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 ifdel 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. Customizing module attribute access
Special names __getattr__
and __dir__
can be also used to customize access to module attributes. The __getattr__
function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an AttributeError
. If an attribute is not found on a module object through the normal lookup, i.e. object.__getattribute__()
, then __getattr__
is searched in the module __dict__
before raising an AttributeError
. If found, it is called with the attribute name and the result is returned.
The __dir__
function should accept no arguments, and return a sequence of strings that represents the names accessible on module. If present, this function overrides the standard dir()
search on a module.
For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the __class__
attribute of a module object to a subclass 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}...')
super().__setattr__(attr, value)
sys.modules[__name__].__class__ = VerboseModule
Note
Defining module __getattr__
and setting module __class__
only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected.
Changed in version 3.5: __class__
module attribute is now writable.
New in version 3.7: __getattr__
and __dir__
module attributes.
See also
- PEP 562 - Module __getattr__ and __dir__
-
Describes the
__getattr__
and__dir__
functions on modules.
3.3.2.2. Implementing Descriptors
The following methods only apply when an instance of the class containing the method (a so-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=None )-
Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional owner argument is 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.PEP 252 specifies that
__get__()
is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own__getattribute__()
implementation always passes in both arguments whether they are required or not.
object.
__set__
( self, instance, value )-
Called to set the attribute on an instance instance of the owner class to a new value, value.
Note, adding
__set__()
or__delete__()
changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details.
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.
Note
__set_name__()
is only called implicitly as part of thetype
constructor, so it will need to be called explicitly with the appropriate parameters when a descriptor is added to a class after initial creation:class A: pass descr = custom_descriptor() A.attr = descr descr.__set_name__(A, 'attr')
See Creating the class object for more details.
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).
3.3.2.3. Invoking Descriptors
In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: __get__()
, __set__()
, and __delete__()
. If any of those methods are defined for an object, it is said to be a descriptor.
The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, a.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 ofsuper
, then the bindingsuper(B, obj).m()
searchesobj.__class__.__mro__
for the base classA
immediately precedingB
and then invokes the descriptor with the call:A.__dict__['m'].__get__(obj, obj.__class__)
.
For instance bindings, the precedence of descriptor invocation depends on 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.
3.3.2.4. __slots__
__slots__ allow us to explicitly declare data members (like properties) and deny the creation of __dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)
The space saved over using __dict__ can be significant. Attribute lookup speed can be significantly improved as well.
object.
__slots__
-
This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance.
3.3.2.4.1. 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
andtuple
.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
.If an iterator is used for __slots__ then a descriptor is created for each of the iterator’s values. However, the __slots__ attribute will be an empty iterator.
3.3.3. Customizing class creation
Whenever a class inherits from another class, __init_subclass__ is called on 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 astype(cls)
.New in version 3.6.
3.3.3.1. 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:
MRO entries are resolved;
the appropriate metaclass is determined;
the class namespace is prepared;
the class body is executed;
the class object is created.
3.3.3.2. Resolving MRO entries
If a base that appears in class definition is not an instance of type
, then an __mro_entries__
method is searched on it. If found, it is called with the original bases tuple. This method must return a tuple of classes that will be used instead of this base. The tuple may be empty, in such case the original base is ignored.
See also
PEP 560 - Core support for typing module and generic types
3.3.3.3. Determining the appropriate metaclass
The appropriate metaclass for a class definition is determined as follows:
if no bases and no explicit metaclass are given, 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
.
3.3.3.4. Preparing the class namespace
Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a __prepare__
attribute, it is called as namespace = metaclass.__prepare__(name, bases, **kwds)
(where the additional keyword arguments, if any, come from the class definition). The __prepare__
method should be implemented as a classmethod()
. The namespace returned by __prepare__
is passed in to __new__
, but when the final class object is created the namespace is copied into a new dict
.
If the metaclass has no __prepare__
attribute, then the class namespace is initialised as an empty ordered mapping.
See also
- PEP 3115 - Metaclasses in Python 3000
-
Introduced the
__prepare__
namespace hook
3.3.3.5. Executing the class body
The class body is executed (approximately) as exec(body, globals(), namespace)
. The key difference from a normal call 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.
3.3.3.6. Creating the class object
Once the class namespace has been populated by executing the class body, the class object is created by calling metaclass(name, bases, namespace, **kwds)
(the additional keywords passed here are the same as those passed to __prepare__
).
This class object is the one that will be referenced by the zero-argument form 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 RuntimeError
in Python 3.8.
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;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
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()
andissubclass()
behavior through__instancecheck__()
and__subclasscheck__()
, with motivation for this functionality in the context of adding Abstract Base Classes (see theabc
module) to the language.
3.3.5. Emulating generic types
One can implement the generic class syntax as specified by PEP 484 (for example List[int]
) by defining a special method:
- classmethod
object.
__class_getitem__
( cls, key ) -
Return an object representing the specialization of a generic class by type arguments found in key.
This method is looked up on the class object itself, and when defined in the class body, this method is implicitly a class method. Note, this mechanism is primarily reserved for use with static type hints, other usage is discouraged.
See also
PEP 560 - Core support for typing module and generic types
3.3.7. 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.abc
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__()
,