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. For example, 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. This is because int is an immutable type, so the reference to 1 can be reused. This behaviour depends on the implementation used, so should not be relied upon, but is something to be aware of when making use of object identity tests. However, after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that e = f = [] assigns the same object to both e and f.)

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.

3.2.1. 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.

3.2.2. 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.) It should not be evaluated in a boolean context.

See Implementing the arithmetic operations for more details.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning. It will raise a TypeError in a future version of Python.

3.2.3. 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.

3.2.4. `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.

The string representations of the numeric classes, computed by __repr__() and __str__(), have the following properties:

They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.
The representation is in base 10, when possible.
Leading zeros, possibly excepting a single zero before a decimal point, are not shown.
Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.
A sign is shown only when the number is negative.

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

3.2.4.1. `numbers.Integral`¶

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

Note

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

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.

3.2.4.2. `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.

3.2.4.3. `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.

3.2.5. 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]. Some sequences, including built-in sequences, interpret negative subscripts by adding the sequence length. For example, a[-2] equals a[n-2], the second to last item of sequence a with length n.

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. The comment above about negative indexes also applies to negative slice positions.

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:

3.2.5.1. 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.

3.2.5.2. 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.

Note

The collections and array module provide additional examples of mutable sequence types.

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.

3.2.6. 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.

3.2.7. 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:

3.2.7.1. 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 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 and dbm.gnu provide additional examples of mapping types, as does the collections 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.

3.2.8. Callable types¶

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

3.2.8.1. 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.

3.2.8.1.1. Special read-only attributes¶

Attribute	Meaning
function.__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.
function.__closure__¶	`None` or a `tuple` of cells that contain bindings for the names specified in the `co_freevars` attribute of the function’s `code object`. 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.

Attribute

Meaning

function.__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.

function.__closure__¶

None or a tuple of cells that contain bindings for the names specified in the co_freevars attribute of the function’s code object.

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.

3.2.8.1.2. Special writable attributes¶

Most of these attributes check the type of the assigned value:

Attribute	Meaning
function.__doc__¶	The function’s documentation string, or `None` if unavailable.
function.__name__¶	The function’s name. See also: `__name__ attributes`.
function.__qualname__¶	The function’s qualified name. See also: `__qualname__ attributes`. Added in version 3.3.
function.__module__¶	The name of the module the function was defined in, or `None` if unavailable.
function.__defaults__¶	A `tuple` containing default parameter values for those parameters that have defaults, or `None` if no parameters have a default value.
function.__code__¶	The code object representing the compiled function body.
function.__dict__¶	The namespace supporting arbitrary function attributes. See also: `__dict__ attributes`.
function.__annotations__¶	A `dictionary` containing annotations of parameters. The keys of the dictionary are the parameter names, and `'return'` for the return annotation, if provided. See also: Annotations Best Practices.
function.__kwdefaults__¶	A `dictionary` containing defaults for keyword-only parameters.
function.__type_params__¶	A `tuple` containing the type parameters of a generic function. Added in version 3.12.

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.

CPython implementation detail: CPython’s 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 (accessible via the __code__ attribute).

3.2.8.2. 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:

method.__self__¶	Refers to the class instance object to which the method is bound
method.__func__¶	Refers to the original function object
method.__doc__¶	The method’s documentation (same as `method.__func__.__doc__`). A `string` if the original function had a docstring, else `None`.
method.__name__¶	The name of the method (same as `method.__func__.__name__`)
method.__module__¶	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 classmethod 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 classmethod 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 classmethod 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.

It is 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.

3.2.8.3. 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.

3.2.8.4. 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.

3.2.8.5. 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.

3.2.8.6. 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. See function.__doc__.
__name__ is the function’s name. See function.__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. See function.__module__.

3.2.8.7. 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. (The attribute has the same semantics as it does with other instance methods.)

3.2.8.8. 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.

3.2.8.9. Class Instances¶

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

3.2.9. 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 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.

3.2.9.1. Import-related attributes on module objects¶

Module objects have the following attributes that relate to the import system. When a module is created using the machinery associated with the import system, these attributes are filled in based on the module’s spec, before the loader executes and loads the module.

To create a module dynamically rather than using the import system, it’s recommended to use importlib.util.module_from_spec(), which will set the various import-controlled attributes to appropriate values. It’s also possible to use the types.ModuleType constructor to create modules directly, but this technique is more error-prone, as most attributes must be manually set on the module object after it has been created when using this approach.

Caution

With the exception of __name__, it is strongly recommended that you rely on __spec__ and its attributes instead of any of the other individual attributes listed in this subsection. Note that updating an attribute on __spec__ will not update the corresponding attribute on the module itself:

>>> import typing
>>> typing.__name__, typing.__spec__.name
('typing', 'typing')
>>> typing.__spec__.name = 'spelling'
>>> typing.__name__, typing.__spec__.name
('typing', 'spelling')
>>> typing.__name__ = 'keyboard_smashing'
>>> typing.__name__, typing.__spec__.name
('keyboard_smashing', 'spelling')

module.__name__¶

The name used to uniquely identify the module in the import system. For a directly executed module, this will be set to "__main__".

This attribute must be set to the fully qualified name of the module. It is expected to match the value of module.__spec__.name.

module.__spec__¶

A record of the module’s import-system-related state.

Set to the module spec that was used when importing the module. See Module specs for more details.

Added in version 3.4.

module.__package__¶

The package a module belongs to.

If the module is top-level (that is, not a part of any specific package) then the attribute should be set to '' (the empty string). Otherwise, it should be set to the name of the module’s package (which can be equal to module.__name__ if the module itself is a package). See PEP 366 for further details.

This attribute is used instead of __name__ to calculate explicit relative imports for main modules. It defaults to None for modules created dynamically using the types.ModuleType constructor; use importlib.util.module_from_spec() instead to ensure the attribute is set to a str.

It is strongly recommended that you use module.__spec__.parent instead of module.__package__. __package__ is now only used as a fallback if __spec__.parent is not set, and this fallback path is deprecated.

Changed in version 3.4: This attribute now defaults to None for modules created dynamically using the types.ModuleType constructor. Previously the attribute was optional.

Changed in version 3.6: The value of __package__ is expected to be the same as __spec__.parent. __package__ is now only used as a fallback during import resolution if __spec__.parent is not defined.

Changed in version 3.10: ImportWarning is raised if an import resolution falls back to __package__ instead of __spec__.parent.

Changed in version 3.12: Raise DeprecationWarning instead of ImportWarning when falling back to __package__ during import resolution.

Deprecated since version 3.13, will be removed in version 3.15: __package__ will cease to be set or taken into consideration by the import system or standard library.

module.__loader__¶

The loader object that the import machinery used to load the module.

This attribute is mostly useful for introspection, but can be used for additional loader-specific functionality, for example getting data associated with a loader.

__loader__ defaults to None for modules created dynamically using the types.ModuleType constructor; use importlib.util.module_from_spec() instead to ensure the attribute is set to a loader object.

It is strongly recommended that you use module.__spec__.loader instead of module.__loader__.

Changed in version 3.4: This attribute now defaults to None for modules created dynamically using the types.ModuleType constructor. Previously the attribute was optional.

Deprecated since version 3.12, will be removed in version 3.16: Setting __loader__ on a module while failing to set __spec__.loader is deprecated. In Python 3.16, __loader__ will cease to be set or taken into consideration by the import system or the standard library.

module.__path__¶

A (possibly empty) sequence of strings enumerating the locations where the package’s submodules will be found. Non-package modules should not have a __path__ attribute. See __path__ attributes on modules for more details.

It is strongly recommended that you use module.__spec__.submodule_search_locations instead of module.__path__.

module.__file__¶

module.__cached__¶

__file__ and __cached__ are both optional attributes that may or may not be set. Both attributes should be a str when they are available.

__file__ indicates the pathname of the file from which the module was loaded (if loaded from a file), or the pathname of the shared library file for extension modules loaded dynamically from a shared library. It might be missing for certain types of modules, such as C modules that are statically linked into the interpreter, and the import system may opt to leave it unset if it has no semantic meaning (for example, a module loaded from a database).

If __file__ is set then the __cached__ attribute might also be set, which is the path to any compiled version of the code (for example, a byte-compiled file). The file does not need to exist to set this attribute; the path can simply point to where the compiled file would exist (see PEP 3147).

Note that __cached__ may be set even if __file__ is not set. However, that scenario is quite atypical. Ultimately, the loader is what makes use of the module spec provided by the finder (from which __file__ and __cached__ are derived). So if a loader can load from a cached module but otherwise does not load from a file, that atypical scenario may be appropriate.

It is strongly recommended that you use module.__spec__.cached instead of module.__cached__.

Deprecated since version 3.13, will be removed in version 3.15: Setting __cached__ on a module while failing to set __spec__.cached is deprecated. In Python 3.15, __cached__ will cease to be set or taken into consideration by the import system or standard library.

3.2.9.2. Other writable attributes on module objects¶

As well as the import-related attributes listed above, module objects also have the following writable attributes:

module.__doc__¶: The module’s documentation string, or None if unavailable. See also: __doc__ attributes.

module.__annotations__¶: A dictionary containing variable annotations collected during module body execution. For best practices on working with __annotations__, please see Annotations Best Practices.

3.2.9.3. Module dictionaries¶

Module objects also have the following special read-only attribute:

module.__dict__¶: The module’s namespace as a dictionary object. Uniquely among the attributes listed here, __dict__ cannot be accessed as a global variable from within a module; it can only be accessed as an attribute on module objects.

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.

3.2.10. 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 at The Python 2.3 Method Resolution Order.

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 is C. When it would yield a staticmethod 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).

3. Data model¶

3.1. Objects, values and types¶

3.2. The standard type hierarchy¶

3.2.1. None¶

3.2.2. NotImplemented¶

3.2.3. Ellipsis¶

3.2.4. numbers.Number¶

3.2.4.1. numbers.Integral¶

3.2.4.2. numbers.Real (float)¶

3.2.4.3. numbers.Complex (complex)¶

3.2.5. Sequences¶

3.2.5.1. Immutable sequences¶

3.2.5.2. Mutable sequences¶

3.2.6. Set types¶

3.2.7. Mappings¶

3.2.7.1. Dictionaries¶

3.2.8. Callable types¶

3.2.8.1. User-defined functions¶

3.2.8.1.1. Special read-only attributes¶

3.2.8.1.2. Special writable attributes¶

3.2.8.2. Instance methods¶

3.2.8.3. Generator functions¶

3.2.8.4. Coroutine functions¶

3.2.8.5. Asynchronous generator functions¶

3.2.8.6. Built-in functions¶

3.2.8.7. Built-in methods¶

3.2.8.8. Classes¶

3.2.8.9. Class Instances¶

3.2.9. Modules¶

3.2.9.1. Import-related attributes on module objects¶

3.2.9.2. Other writable attributes on module objects¶

3.2.9.3. Module dictionaries¶

3.2.10. Custom classes¶

3.2.10.1. Special attributes

3.2.4. `numbers.Number`¶

3.2.4.1. `numbers.Integral`¶

3.2.4.2. `numbers.Real` (`float`)¶

3.2.4.3. `numbers.Complex` (`complex`)¶