How-To Guide for Descriptors

Author:	Raymond Hettinger
Contact:	<python at rcn dot com>
Copyright:	Copyright (c) 2003, 2004 Python Software Foundation.All rights reserved.

Contents

• Abstract
• Definition and Introduction
• Descriptor Protocol
• Invoking Descriptors
• Descriptor Example
• Properties
• Functions and Methods
• Static Methods and Class Methods

Abstract

Defines descriptors, summarizes the protocol, and shows how descriptors arecalled. Examines a custom descriptor and several built-in python descriptorsincluding functions, properties, static methods, and class methods. Shows howeach works by giving a pure Python equivalent and a sample application.

Learning about descriptors not only provides access to a larger toolset, itcreates a deeper understanding of how Python works and an appreciation for theelegance of its design.

Definition and Introduction

In general, a descriptor is an object attribute with "binding behavior", one whoseattribute access has been overridden by methods in the descriptor protocol. Thosemethods are__get__ , __set__ , and __delete__ . If any of those methods aredefined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete theattribute from an object's dictionary. For instance,a.x has a lookup chainstarting with a.__dict__['x'] , then type(a).__dict__['x'] , and continuingthrough the base classes oftype(a) excluding metaclasses.If the looked-up value is an object defining one of thedescriptor methods, then Python may override the default behavior and invoke thedescriptor method instead. Where this occurs in the precedence chain depends onwhich descriptor methods were defined. Note that descriptors are only invokedfor new style objects or classes (a class is new style if it inherits fromobject ortype ).

Descriptors are a powerful, general purpose protocol. They are the mechanismbehind properties, methods, static methods, class methods, andsuper() .They are used used throughout Python itself to implement the new style classesintroduced in version 2.2. Descriptors simplify the underlying C-code and offera flexible set of new tools for everyday Python programs.

Descriptor Protocol

descr.__get__(self, obj, type=None) --> value

descr.__set__(self, obj, value) --> None

descr.__delete__(self, obj) --> None

That is all there is to it. Define any of these methods and an object isconsidered a descriptor and can override default behavior upon being looked upas an attribute.

If an object defines both __get__ and __set__ , it is considered a datadescriptor. Descriptors that only define __get__ are called non-datadescriptors (they are typically used for methods but other uses are possible).

Data and non-data descriptors differ in how overrides are calculated withrespect to entries in an instance's dictionary. If an instance's dictionaryhas an entry with the same name as a data descriptor, the data descriptortakes precedence. If an instance's dictionary has an entry with the samename as a non-data descriptor, the dictionary entry takes precedence.

To make a read-only data descriptor, define both __get__ and__set__ with the __set__ raising anAttributeError when called. Defining the__set__ method with an exception raising placeholder is enough to makeit a data descriptor.

Invoking Descriptors

A descriptor can be called directly by its method name. For example,d.__get__(obj) .

Alternatively, it is more common for a descriptor to be invoked automaticallyupon attribute access. For example,obj.d looks up d in the dictionaryofobj . If d defines the method __get__ , then d.__get__(obj) is invoked according to the precedence rules listed below.

The details of invocation depend on whether obj is an object or a class. Eitherway, descriptors only work for new style objects and classes. A class is newstyle if it is a subclass ofobject .

For objects, the machinery is in object.__getattribute__ which transformsb.x intotype(b).__dict__['x'].__get__(b, type(b)) . The implementation worksthrough a precedence chain that gives data descriptors priority over instancevariables, instance variables priority over non-data descriptors, and assignslowest priority to __getattr__ if provided. The full C implementation can befound inPyObject_GenericGetAttr() in Objects/object.c .

For classes, the machinery is in type.__getattribute__ which transformsB.x into B.__dict__['x'].__get__(None, B) . In pure Python, it looks like:

def __getattribute__(self, key):
    "Emulate type_getattro() in Objects/typeobject.c"
    v = object.__getattribute__(self, key)
    if hasattr(v, '__get__'):
       return v.__get__(None, self)
    return v

The important points to remember are:

• descriptors are invoked by the __getattribute__ method
• overriding __getattribute__ prevents automatic descriptor calls
• __getattribute__ is only available with new style classes and objects
• object.__getattribute__ and type.__getattribute__ make differentcalls to__get__ .
• data descriptors always override instance dictionaries.
• non-data descriptors may be overridden by instance dictionaries.

The object returned by super() also has a custom __getattribute__ method for invoking descriptors. The call super(B, obj).m() searchesobj.__class__.__mro__ for the base classA immediately following B and then returnsA.__dict__['m'].__get__(obj, A) . If not a descriptor,m is returned unchanged. If not in the dictionary,m reverts to asearch using object.__getattribute__ .

Note, in Python 2.2, super(B, obj).m() would only invoke__get__ ifm was a data descriptor. In Python 2.3, non-data descriptors also getinvoked unless an old-style class is involved. The implementation detailsare insuper_getattro() in Objects/typeobject.c and a pure Pythonequivalent can be found inGuido's Tutorial .

The details above show that the mechanism for descriptors is embedded in the__getattribute__() methods forobject , type , and super . Classesinherit this machinery when they derive from object or if they have ameta-class providing similar functionality. Likewise, classes can turn-offdescriptor invocation by overriding__getattribute__() .

Descriptor Example

The following code creates a class whose objects are data descriptors whichprint a message for each get or set. Overriding__getattribute__ isalternate approach that could do this for every attribute. However, thisdescriptor is useful for monitoring just a few chosen attributes:

class RevealAccess(object):
    """A data descriptor that sets and returns values
       normally and prints a message logging their access.
    """

    def __init__(self, initval=None, name='var'):
        self.val = initval
        self.name = name

    def __get__(self, obj, objtype):
        print 'Retrieving', self.name
        return self.val

    def __set__(self, obj, val):
        print 'Updating' , self.name
        self.val = val

>>> class MyClass(object):
    x = RevealAccess(10, 'var "x"')
    y = 5

>>> m = MyClass()
>>> m.x
Retrieving var "x"
10
>>> m.x = 20
Updating var "x"
>>> m.x
Retrieving var "x"
20
>>> m.y
5

The protocol is simple and offers exciting possibilities. Several use cases areso common that they have been packaged into individual function calls.Properties, bound and unbound methods, static methods, and class methods are allbased on the descriptor protocol.

Properties

Calling property() is a succinct way of building a data descriptor that triggersfunction calls upon access to an attribute. Its signature is:

property(fget=None, fset=None, fdel=None, doc=None) -> property attribute

The documentation shows a typical use to define a managed attribute x :

class C(object):
    def getx(self): return self.__x
    def setx(self, value): self.__x = value
    def delx(self): del self.__x
    x = property(getx, setx, delx, "I'm the 'x' property.")

To see how property() is implemented in terms of the descriptor protocol, hereis a pure Python equivalent:

class Property(object):
    "Emulate PyProperty_Type() in Objects/descrobject.c"

    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
        self.fget = fget
        self.fset = fset
        self.fdel = fdel
        self.__doc__ = doc

    def __get__(self, obj, objtype=None):
        if obj is None:
            return self         
        if self.fget is None:
            raise AttributeError, "unreadable attribute"
        return self.fget(obj)

    def __set__(self, obj, value):
        if self.fset is None:
            raise AttributeError, "can't set attribute"
        self.fset(obj, value)

    def __delete__(self, obj):
        if self.fdel is None:
            raise AttributeError, "can't delete attribute"
        self.fdel(obj)

The property() builtin helps whenever a user interface has granted attributeaccess and then subsequent changes require the intervention of a method.

For instance, a spreadsheet class may grant access to a cell value throughCell('b10').value . Subsequent improvements to the program require the cell to berecalculated on every access; however, the programmer does not want to affectexisting client code accessing the attribute directly. The solution is to wrapaccess to the value attribute in aproperty() data descriptor:

class Cell(object):
    . . .
    def getvalue(self, obj):
        "Recalculate cell before returning value"
        self.recalc()
        return obj._value
    value = property(getvalue)

Functions and Methods

Python's object oriented features are built upon a function based environment.Using non-data descriptors, the two are merged seamlessly.

Class dictionaries store methods as functions. In a class definition, methodsare written usingdef and lambda , the usual tools for creatingfunctions. The only difference from regular functions is that the firstargument is reserved for the object instance. By Python convention, theinstance reference is called self but may be called this or anyother variable name.

To support method calls, functions include the __get__ method for bindingmethods during attribute access. This means that all functions are non-datadescriptors which return bound or unbound methods depending whether they areinvoked from an object or a class. In pure python, it works like this:

class Function(object):
    . . .
    def __get__(self, obj, objtype=None):
        "Simulate func_descr_get() in Objects/funcobject.c"
        return types.MethodType(self, obj, objtype)

Running the interpreter shows how the function descriptor works in practice:

>>> class D(object):
     def f(self, x):
          return x

>>> d = D()
>>> D.__dict__['f'] # Stored internally as a function
<function f at 0x00C45070>
>>> D.f             # Get from a class becomes an unbound method
<unbound method D.f>
>>> d.f             # Get from an instance becomes a bound method
<bound method D.f of <__main__.D object at 0x00B18C90>>

The output suggests that bound and unbound methods are two different types.While they could have been implemented that way, the actual C implemention ofPyMethod_Type inObjects/classobject.c is a single object with two differentrepresentations depending on whether theim_self field is set or is NULL (the Cequivalent ofNone ).

Likewise, the effects of calling a method object depend on the im_self field.If set (meaning bound), the original function (stored in the im_func field) iscalled as expected with the first argument set to the instance. If unbound, allof the arguments are passed unchanged to the original function. The actual Cimplementation ofinstancemethod_call() is only slightly more complex in that itincludes some type checking.

Static Methods and Class Methods

Non-data descriptors provide a simple mechanism for variations on the usualpatterns of binding functions into methods.

To recap, functions have a __get__ method so that they can be converted to amethod when accessed as attributes. The non-data descriptor transforms aobj.f(*args) call intof(obj, *args) . Calling klass.f(*args) becomesf(*args) .

This chart summarizes the binding and its two most useful variants:

Transformation Called from anObject Called from aClass

Descriptor	function	f(obj, *args)	f(*args)
staticmethod	f(*args)	f(*args)
classmethod	f(type(obj), *args)	f(klass, *args)

Static methods return the underlying function without changes. Calling eitherc.f orC.f is the equivalent of a direct lookup intoobject.__getattribute__(c, "f") orobject.__getattribute__(C, "f") .As a result, the function becomes identically accessible from either an object or a class.

Good candidates for static methods are methods that do not reference theself variable.

For instance, a statistics package may include a container class forexperimental data. The class provides normal methods for computing the average,mean, median, and other descriptive statistics that depend on the data.However, there may be useful functions which are conceptually related but do notdepend on the data. For instance, erf(x) is handy conversion routine that comesup in statistical work but does not directly depend on a particular data set.It can be called either from an object or the class:s.erf(1.5) --> .9332 orSample.erf(1.5) --> .9332 .

Since staticmethods return the underlying function with no changes, the examplecalls are unexciting:

>>> class E(object):
     def f(x):
          print x
     f = staticmethod(f)

>>> print E.f(3)
3
>>> print E().f(3)
3

Using the non-data descriptor protocol, a pure Python version of staticmethod() would look like this:

class StaticMethod(object):
 "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

 def __init__(self, f):
      self.f = f

 def __get__(self, obj, objtype=None):
      return self.f

Unlike static methods, class methods prepend the class reference to theargument list before calling the function. This format is the samefor whether the caller is an object or a class:

>>> class E(object):
     def f(klass, x):
          return klass.__name__, x
     f = classmethod(f)

>>> print E.f(3)
('E', 3)
>>> print E().f(3)
('E', 3)

This behavior is useful whenever the function only needs to have a classreference and does not care about any underlying data. One use forclassmethods is to create alternate class constructors. In Python 2.3,the classmethoddict.fromkeys() creates a new dictionary from a listof keys. The pure Python equivalent is:

class Dict:
    . . .
    def fromkeys(klass, iterable, value=None):
        "Emulate dict_fromkeys() in Objects/dictobject.c"
        d = klass()
        for key in iterable:
            d[key] = value
        return d
    fromkeys = classmethod(fromkeys)

Now a new dictionary of unique keys can be constructed like this:

>>> Dict.fromkeys('abracadabra')
{'a': None, 'r': None, 'b': None, 'c': None, 'd': None}

Using the non-data descriptor protocol, a pure Python version ofclassmethod() would look like this:

class ClassMethod(object):
     "Emulate PyClassMethod_Type() in Objects/funcobject.c"

     def __init__(self, f):
          self.f = f

     def __get__(self, obj, klass=None):
          if klass is None:
               klass = type(obj)
          def newfunc(*args):
               return self.f(klass, *args)
          return newfunc

 

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