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Python Enhancement Proposals

PEP 252 – Making Types Look More Like Classes

Guido van Rossum <guido at>
Standards Track

Table of Contents


This PEP proposes changes to the introspection API for types that makes them look more like classes, and their instances more like class instances. For example, type(x) will be equivalent to x.__class__ for most built-in types. When C is x.__class__, x.meth(a) will generally be equivalent to C.meth(x, a), and C.__dict__ contains x’s methods and other attributes.

This PEP also introduces a new approach to specifying attributes, using attribute descriptors, or descriptors for short. Descriptors unify and generalize several different common mechanisms used for describing attributes: a descriptor can describe a method, a typed field in the object structure, or a generalized attribute represented by getter and setter functions.

Based on the generalized descriptor API, this PEP also introduces a way to declare class methods and static methods.

[Editor’s note: the ideas described in this PEP have been incorporated into Python. The PEP no longer accurately describes the implementation.]


One of Python’s oldest language warts is the difference between classes and types. For example, you can’t directly subclass the dictionary type, and the introspection interface for finding out what methods and instance variables an object has is different for types and for classes.

Healing the class/type split is a big effort, because it affects many aspects of how Python is implemented. This PEP concerns itself with making the introspection API for types look the same as that for classes. Other PEPs will propose making classes look more like types, and subclassing from built-in types; these topics are not on the table for this PEP.

Introspection APIs

Introspection concerns itself with finding out what attributes an object has. Python’s very general getattr/setattr API makes it impossible to guarantee that there always is a way to get a list of all attributes supported by a specific object, but in practice two conventions have appeared that together work for almost all objects. I’ll call them the class-based introspection API and the type-based introspection API; class API and type API for short.

The class-based introspection API is used primarily for class instances; it is also used by Jim Fulton’s ExtensionClasses. It assumes that all data attributes of an object x are stored in the dictionary x.__dict__, and that all methods and class variables can be found by inspection of x’s class, written as x.__class__. Classes have a __dict__ attribute, which yields a dictionary containing methods and class variables defined by the class itself, and a __bases__ attribute, which is a tuple of base classes that must be inspected recursively. Some assumptions here are:

  • attributes defined in the instance dict override attributes defined by the object’s class;
  • attributes defined in a derived class override attributes defined in a base class;
  • attributes in an earlier base class (meaning occurring earlier in __bases__) override attributes in a later base class.

(The last two rules together are often summarized as the left-to-right, depth-first rule for attribute search. This is the classic Python attribute lookup rule. Note that PEP 253 will propose to change the attribute lookup order, and if accepted, this PEP will follow suit.)

The type-based introspection API is supported in one form or another by most built-in objects. It uses two special attributes, __members__ and __methods__. The __methods__ attribute, if present, is a list of method names supported by the object. The __members__ attribute, if present, is a list of data attribute names supported by the object.

The type API is sometimes combined with a __dict__ that works the same as for instances (for example for function objects in Python 2.1, f.__dict__ contains f’s dynamic attributes, while f.__members__ lists the names of f’s statically defined attributes).

Some caution must be exercised: some objects don’t list their “intrinsic” attributes (like __dict__ and __doc__) in __members__, while others do; sometimes attribute names occur both in __members__ or __methods__ and as keys in __dict__, in which case it’s anybody’s guess whether the value found in __dict__ is used or not.

The type API has never been carefully specified. It is part of Python folklore, and most third party extensions support it because they follow examples that support it. Also, any type that uses Py_FindMethod() and/or PyMember_Get() in its tp_getattr handler supports it, because these two functions special-case the attribute names __methods__ and __members__, respectively.

Jim Fulton’s ExtensionClasses ignore the type API, and instead emulate the class API, which is more powerful. In this PEP, I propose to phase out the type API in favor of supporting the class API for all types.

One argument in favor of the class API is that it doesn’t require you to create an instance in order to find out which attributes a type supports; this in turn is useful for documentation processors. For example, the socket module exports the SocketType object, but this currently doesn’t tell us what methods are defined on socket objects. Using the class API, SocketType would show exactly what the methods for socket objects are, and we can even extract their docstrings, without creating a socket. (Since this is a C extension module, the source-scanning approach to docstring extraction isn’t feasible in this case.)

Specification of the class-based introspection API

Objects may have two kinds of attributes: static and dynamic. The names and sometimes other properties of static attributes are knowable by inspection of the object’s type or class, which is accessible through obj.__class__ or type(obj). (I’m using type and class interchangeably; a clumsy but descriptive term that fits both is “meta-object”.)

(XXX static and dynamic are not great terms to use here, because “static” attributes may actually behave quite dynamically, and because they have nothing to do with static class members in C++ or Java. Barry suggests to use immutable and mutable instead, but those words already have precise and different meanings in slightly different contexts, so I think that would still be confusing.)

Examples of dynamic attributes are instance variables of class instances, module attributes, etc. Examples of static attributes are the methods of built-in objects like lists and dictionaries, and the attributes of frame and code objects (f.f_code, c.co_filename, etc.). When an object with dynamic attributes exposes these through its __dict__ attribute, __dict__ is a static attribute.

The names and values of dynamic properties are typically stored in a dictionary, and this dictionary is typically accessible as obj.__dict__. The rest of this specification is more concerned with discovering the names and properties of static attributes than with dynamic attributes; the latter are easily discovered by inspection of obj.__dict__.

In the discussion below, I distinguish two kinds of objects: regular objects (like lists, ints, functions) and meta-objects. Types and classes are meta-objects. Meta-objects are also regular objects, but we’re mostly interested in them because they are referenced by the __class__ attribute of regular objects (or by the __bases__ attribute of other meta-objects).

The class introspection API consists of the following elements:

  • the __class__ and __dict__ attributes on regular objects;
  • the __bases__ and __dict__ attributes on meta-objects;
  • precedence rules;
  • attribute descriptors.

Together, these not only tell us about all attributes defined by a meta-object, but they also help us calculate the value of a specific attribute of a given object.

  1. The __dict__ attribute on regular objects

    A regular object may have a __dict__ attribute. If it does, this should be a mapping (not necessarily a dictionary) supporting at least __getitem__(), keys(), and has_key(). This gives the dynamic attributes of the object. The keys in the mapping give attribute names, and the corresponding values give their values.

    Typically, the value of an attribute with a given name is the same object as the value corresponding to that name as a key in the __dict__. In other words, obj.__dict__['spam'] is obj.spam. (But see the precedence rules below; a static attribute with the same name may override the dictionary item.)

  2. The __class__ attribute on regular objects

    A regular object usually has a __class__ attribute. If it does, this references a meta-object. A meta-object can define static attributes for the regular object whose __class__ it is. This is normally done through the following mechanism:

  3. The __dict__ attribute on meta-objects

    A meta-object may have a __dict__ attribute, of the same form as the __dict__ attribute for regular objects (a mapping but not necessarily a dictionary). If it does, the keys of the meta-object’s __dict__ are names of static attributes for the corresponding regular object. The values are attribute descriptors; we’ll explain these later. An unbound method is a special case of an attribute descriptor.

    Because a meta-object is also a regular object, the items in a meta-object’s __dict__ correspond to attributes of the meta-object; however, some transformation may be applied, and bases (see below) may define additional dynamic attributes. In other words, mobj.spam is not always mobj.__dict__['spam']. (This rule contains a loophole because for classes, if C.__dict__['spam'] is a function, C.spam is an unbound method object.)

  4. The __bases__ attribute on meta-objects

    A meta-object may have a __bases__ attribute. If it does, this should be a sequence (not necessarily a tuple) of other meta-objects, the bases. An absent __bases__ is equivalent to an empty sequence of bases. There must never be a cycle in the relationship between meta-objects defined by __bases__ attributes; in other words, the __bases__ attributes define a directed acyclic graph, with arcs pointing from derived meta-objects to their base meta-objects. (It is not necessarily a tree, since multiple classes can have the same base class.) The __dict__ attributes of a meta-object in the inheritance graph supply attribute descriptors for the regular object whose __class__ attribute points to the root of the inheritance tree (which is not the same as the root of the inheritance hierarchy – rather more the opposite, at the bottom given how inheritance trees are typically drawn). Descriptors are first searched in the dictionary of the root meta-object, then in its bases, according to a precedence rule (see the next paragraph).

  5. Precedence rules

    When two meta-objects in the inheritance graph for a given regular object both define an attribute descriptor with the same name, the search order is up to the meta-object. This allows different meta-objects to define different search orders. In particular, classic classes use the old left-to-right depth-first rule, while new-style classes use a more advanced rule (see the section on method resolution order in PEP 253).

    When a dynamic attribute (one defined in a regular object’s __dict__) has the same name as a static attribute (one defined by a meta-object in the inheritance graph rooted at the regular object’s __class__), the static attribute has precedence if it is a descriptor that defines a __set__ method (see below); otherwise (if there is no __set__ method) the dynamic attribute has precedence. In other words, for data attributes (those with a __set__ method), the static definition overrides the dynamic definition, but for other attributes, dynamic overrides static.

    Rationale: we can’t have a simple rule like “static overrides dynamic” or “dynamic overrides static”, because some static attributes indeed override dynamic attributes; for example, a key ‘__class__’ in an instance’s __dict__ is ignored in favor of the statically defined __class__ pointer, but on the other hand most keys in inst.__dict__ override attributes defined in inst.__class__. Presence of a __set__ method on a descriptor indicates that this is a data descriptor. (Even read-only data descriptors have a __set__ method: it always raises an exception.) Absence of a __set__ method on a descriptor indicates that the descriptor isn’t interested in intercepting assignment, and then the classic rule applies: an instance variable with the same name as a method hides the method until it is deleted.

  6. Attribute descriptors

    This is where it gets interesting – and messy. Attribute descriptors (descriptors for short) are stored in the meta-object’s __dict__ (or in the __dict__ of one of its ancestors), and have two uses: a descriptor can be used to get or set the corresponding attribute value on the (regular, non-meta) object, and it has an additional interface that describes the attribute for documentation and introspection purposes.

    There is little prior art in Python for designing the descriptor’s interface, neither for getting/setting the value nor for describing the attribute otherwise, except some trivial properties (it’s reasonable to assume that __name__ and __doc__ should be the attribute’s name and docstring). I will propose such an API below.

    If an object found in the meta-object’s __dict__ is not an attribute descriptor, backward compatibility dictates certain minimal semantics. This basically means that if it is a Python function or an unbound method, the attribute is a method; otherwise, it is the default value for a dynamic data attribute. Backwards compatibility also dictates that (in the absence of a __setattr__ method) it is legal to assign to an attribute corresponding to a method, and that this creates a data attribute shadowing the method for this particular instance. However, these semantics are only required for backwards compatibility with regular classes.

The introspection API is a read-only API. We don’t define the effect of assignment to any of the special attributes (__dict__, __class__ and __bases__), nor the effect of assignment to the items of a __dict__. Generally, such assignments should be considered off-limits. A future PEP may define some semantics for some such assignments. (Especially because currently instances support assignment to __class__ and __dict__, and classes support assignment to __bases__ and __dict__.)

Specification of the attribute descriptor API

Attribute descriptors may have the following attributes. In the examples, x is an object, C is x.__class__, x.meth() is a method, and x.ivar is a data attribute or instance variable. All attributes are optional – a specific attribute may or may not be present on a given descriptor. An absent attribute means that the corresponding information is not available or the corresponding functionality is not implemented.

  • __name__: the attribute name. Because of aliasing and renaming, the attribute may (additionally or exclusively) be known under a different name, but this is the name under which it was born. Example: C.meth.__name__ == 'meth'.
  • __doc__: the attribute’s documentation string. This may be None.
  • __objclass__: the class that declared this attribute. The descriptor only applies to objects that are instances of this class (this includes instances of its subclasses). Example: C.meth.__objclass__ is C.
  • __get__(): a function callable with one or two arguments that retrieves the attribute value from an object. This is also referred to as a “binding” operation, because it may return a “bound method” object in the case of method descriptors. The first argument, X, is the object from which the attribute must be retrieved or to which it must be bound. When X is None, the optional second argument, T, should be meta-object and the binding operation may return an unbound method restricted to instances of T. When both X and T are specified, X should be an instance of T. Exactly what is returned by the binding operation depends on the semantics of the descriptor; for example, static methods and class methods (see below) ignore the instance and bind to the type instead.
  • __set__(): a function of two arguments that sets the attribute value on the object. If the attribute is read-only, this method may raise a TypeError or AttributeError exception (both are allowed, because both are historically found for undefined or unsettable attributes). Example: C.ivar.set(x, y) ~~ x.ivar = y.

Static methods and class methods

The descriptor API makes it possible to add static methods and class methods. Static methods are easy to describe: they behave pretty much like static methods in C++ or Java. Here’s an example:

class C:

    def foo(x, y):
        print "staticmethod", x, y
    foo = staticmethod(foo), 2)
c = C(), 2)

Both the call, 2) and the call, 2) call foo() with two arguments, and print “staticmethod 1 2”. No “self” is declared in the definition of foo(), and no instance is required in the call.

The line “foo = staticmethod(foo)” in the class statement is the crucial element: this makes foo() a static method. The built-in staticmethod() wraps its function argument in a special kind of descriptor whose __get__() method returns the original function unchanged. Without this, the __get__() method of standard function objects would have created a bound method object for ‘’ and an unbound method object for ‘’.

(XXX Barry suggests to use “sharedmethod” instead of “staticmethod”, because the word static is being overloaded in so many ways already. But I’m not sure if shared conveys the right meaning.)

Class methods use a similar pattern to declare methods that receive an implicit first argument that is the class for which they are invoked. This has no C++ or Java equivalent, and is not quite the same as what class methods are in Smalltalk, but may serve a similar purpose. According to Armin Rigo, they are similar to “virtual class methods” in Borland Pascal dialect Delphi. (Python also has real metaclasses, and perhaps methods defined in a metaclass have more right to the name “class method”; but I expect that most programmers won’t be using metaclasses.) Here’s an example:

class C:

    def foo(cls, y):
        print "classmethod", cls, y
    foo = classmethod(foo)
c = C()

Both the call and the call end up calling foo() with two arguments, and print “classmethod __main__.C 1”. The first argument of foo() is implied, and it is the class, even if the method was invoked via an instance. Now let’s continue the example:

class D(C):
d = D()

This prints “classmethod __main__.D 1” both times; in other words, the class passed as the first argument of foo() is the class involved in the call, not the class involved in the definition of foo().

But notice this:

class E(C):
    def foo(cls, y): # override
        print " called"
    foo = classmethod(foo)
e = E()

In this example, the call to from will see class C as its first argument, not class E. This is to be expected, since the call specifies the class C. But it stresses the difference between these class methods and methods defined in metaclasses, where an upcall to a metamethod would pass the target class as an explicit first argument. (If you don’t understand this, don’t worry, you’re not alone.) Note that calling would be a mistake – it would cause infinite recursion. Also note that you can’t specify an explicit ‘cls’ argument to a class method. If you want this (e.g. the __new__ method in PEP 253 requires this), use a static method with a class as its explicit first argument instead.


XXX The following is VERY rough text that I wrote with a different audience in mind; I’ll have to go through this to edit it more. XXX It also doesn’t go into enough detail for the C API.

A built-in type can declare special data attributes in two ways: using a struct memberlist (defined in structmember.h) or a struct getsetlist (defined in descrobject.h). The struct memberlist is an old mechanism put to new use: each attribute has a descriptor record including its name, an enum giving its type (various C types are supported as well as PyObject *), an offset from the start of the instance, and a read-only flag.

The struct getsetlist mechanism is new, and intended for cases that don’t fit in that mold, because they either require additional checking, or are plain calculated attributes. Each attribute here has a name, a getter C function pointer, a setter C function pointer, and a context pointer. The function pointers are optional, so that for example setting the setter function pointer to NULL makes a read-only attribute. The context pointer is intended to pass auxiliary information to generic getter/setter functions, but I haven’t found a need for this yet.

Note that there is also a similar mechanism to declare built-in methods: these are PyMethodDef structures, which contain a name and a C function pointer (and some flags for the calling convention).

Traditionally, built-in types have had to define their own tp_getattro and tp_setattro slot functions to make these attribute definitions work (PyMethodDef and struct memberlist are quite old). There are convenience functions that take an array of PyMethodDef or memberlist structures, an object, and an attribute name, and return or set the attribute if found in the list, or raise an exception if not found. But these convenience functions had to be explicitly called by the tp_getattro or tp_setattro method of the specific type, and they did a linear search of the array using strcmp() to find the array element describing the requested attribute.

I now have a brand spanking new generic mechanism that improves this situation substantially.

  • Pointers to arrays of PyMethodDef, memberlist, getsetlist structures are part of the new type object (tp_methods, tp_members, tp_getset).
  • At type initialization time (in PyType_InitDict()), for each entry in those three arrays, a descriptor object is created and placed in a dictionary that belongs to the type (tp_dict).
  • Descriptors are very lean objects that mostly point to the corresponding structure. An implementation detail is that all descriptors share the same object type, and a discriminator field tells what kind of descriptor it is (method, member, or getset).
  • As explained in PEP 252, descriptors have a get() method that takes an object argument and returns that object’s attribute; descriptors for writable attributes also have a set() method that takes an object and a value and set that object’s attribute. Note that the get() object also serves as a bind() operation for methods, binding the unbound method implementation to the object.
  • Instead of providing their own tp_getattro and tp_setattro implementation, almost all built-in objects now place PyObject_GenericGetAttr and (if they have any writable attributes) PyObject_GenericSetAttr in their tp_getattro and tp_setattro slots. (Or, they can leave these NULL, and inherit them from the default base object, if they arrange for an explicit call to PyType_InitDict() for the type before the first instance is created.)
  • In the simplest case, PyObject_GenericGetAttr() does exactly one dictionary lookup: it looks up the attribute name in the type’s dictionary (obj->ob_type->tp_dict). Upon success, there are two possibilities: the descriptor has a get method, or it doesn’t. For speed, the get and set methods are type slots: tp_descr_get and tp_descr_set. If the tp_descr_get slot is non-NULL, it is called, passing the object as its only argument, and the return value from this call is the result of the getattr operation. If the tp_descr_get slot is NULL, as a fallback the descriptor itself is returned (compare class attributes that are not methods but simple values).
  • PyObject_GenericSetAttr() works very similar but uses the tp_descr_set slot and calls it with the object and the new attribute value; if the tp_descr_set slot is NULL, an AttributeError is raised.
  • But now for a more complicated case. The approach described above is suitable for most built-in objects such as lists, strings, numbers. However, some object types have a dictionary in each instance that can store arbitrary attributes. In fact, when you use a class statement to subtype an existing built-in type, you automatically get such a dictionary (unless you explicitly turn it off, using another advanced feature, __slots__). Let’s call this the instance dict, to distinguish it from the type dict.
  • In the more complicated case, there’s a conflict between names stored in the instance dict and names stored in the type dict. If both dicts have an entry with the same key, which one should we return? Looking at classic Python for guidance, I find conflicting rules: for class instances, the instance dict overrides the class dict, except for the special attributes (like __dict__ and __class__), which have priority over the instance dict.
  • I resolved this with the following set of rules, implemented in PyObject_GenericGetAttr():
    1. Look in the type dict. If you find a data descriptor, use its get() method to produce the result. This takes care of special attributes like __dict__ and __class__.
    2. Look in the instance dict. If you find anything, that’s it. (This takes care of the requirement that normally the instance dict overrides the class dict.)
    3. Look in the type dict again (in reality this uses the saved result from step 1, of course). If you find a descriptor, use its get() method; if you find something else, that’s it; if it’s not there, raise AttributeError.

    This requires a classification of descriptors as data and nondata descriptors. The current implementation quite sensibly classifies member and getset descriptors as data (even if they are read-only!) and method descriptors as nondata. Non-descriptors (like function pointers or plain values) are also classified as non-data (!).

  • This scheme has one drawback: in what I assume to be the most common case, referencing an instance variable stored in the instance dict, it does two dictionary lookups, whereas the classic scheme did a quick test for attributes starting with two underscores plus a single dictionary lookup. (Although the implementation is sadly structured as instance_getattr() calling instance_getattr1() calling instance_getattr2() which finally calls PyDict_GetItem(), and the underscore test calls PyString_AsString() rather than inlining this. I wonder if optimizing the snot out of this might not be a good idea to speed up Python 2.2, if we weren’t going to rip it all out. :-)
  • A benchmark verifies that in fact this is as fast as classic instance variable lookup, so I’m no longer worried.
  • Modification for dynamic types: step 1 and 3 look in the dictionary of the type and all its base classes (in MRO sequence, or course).




Let’s look at lists. In classic Python, the method names of lists were available as the __methods__ attribute of list objects:

>>> [].__methods__
['append', 'count', 'extend', 'index', 'insert', 'pop',
'remove', 'reverse', 'sort']

Under the new proposal, the __methods__ attribute no longer exists:

>>> [].__methods__
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
AttributeError: 'list' object has no attribute '__methods__'

Instead, you can get the same information from the list type:

>>> T = [].__class__
>>> T
<type 'list'>
>>> dir(T)                # like T.__dict__.keys(), but sorted
['__add__', '__class__', '__contains__', '__eq__', '__ge__',
'__getattr__', '__getitem__', '__getslice__', '__gt__',
'__iadd__', '__imul__', '__init__', '__le__', '__len__',
'__lt__', '__mul__', '__ne__', '__new__', '__radd__',
'__repr__', '__rmul__', '__setitem__', '__setslice__', 'append',
'count', 'extend', 'index', 'insert', 'pop', 'remove',
'reverse', 'sort']

The new introspection API gives more information than the old one: in addition to the regular methods, it also shows the methods that are normally invoked through special notations, e.g. __iadd__ (+=), __len__ (len), __ne__ (!=). You can invoke any method from this list directly:

>>> a = ['tic', 'tac']
>>> T.__len__(a)          # same as len(a)
>>> T.append(a, 'toe')    # same as a.append('toe')
>>> a
['tic', 'tac', 'toe']

This is just like it is for user-defined classes.

Notice a familiar yet surprising name in the list: __init__. This is the domain of PEP 253.

Backwards compatibility


Warnings and Errors



A partial implementation of this PEP is available from CVS as a branch named “descr-branch”. To experiment with this implementation, proceed to check out Python from CVS according to the instructions at but add the arguments “-r descr-branch” to the cvs checkout command. (You can also start with an existing checkout and do “cvs update -r descr-branch”.) For some examples of the features described here, see the file Lib/test/

Note: the code in this branch goes way beyond this PEP; it is also the experimentation area for PEP 253 (Subtyping Built-in Types).




Last modified: 2023-09-09 17:39:29 GMT