======================
Descriptor HowTo Guide
======================
:Author: Raymond Hettinger
:Contact: <python at rcn dot com>
.. Contents::
Abstract
--------
Defines descriptors, summarizes the protocol, and shows how descriptors are
called. Examines a custom descriptor and several built-in python descriptors
including functions, properties, static methods, and class methods. Shows how
each works by giving a pure Python equivalent and a sample application.
Learning about descriptors not only provides access to a larger toolset, it
creates a deeper understanding of how Python works and an appreciation for the
elegance of its design.
Definition and Introduction
---------------------------
In general, a descriptor is an object attribute with "binding behavior", one
whose attribute access has been overridden by methods in the descriptor
protocol. Those methods are :meth:`__get__`, :meth:`__set__`, and
:meth:`__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. 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. Note that descriptors are only invoked for new style objects or
classes (a class is new style if it inherits from :class:`object` or
:class:`type`).
Descriptors are a powerful, general purpose protocol. They are the mechanism
behind properties, methods, static methods, class methods, and :func:`super()`.
They are used throughout Python itself to implement the new style classes
introduced in version 2.2. Descriptors simplify the underlying C-code and offer
a 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 is
considered a descriptor and can override default behavior upon being looked up
as an attribute.
If an object defines both :meth:`__get__` and :meth:`__set__`, it is considered
a data descriptor. Descriptors that only define :meth:`__get__` are called
non-data descriptors (they are typically used for methods but other uses are
possible).
Data and non-data descriptors differ in how overrides are calculated with
respect to entries in an instance's dictionary. If an instance's dictionary
has an entry with the same name as a data descriptor, the data descriptor
takes precedence. If an instance's dictionary has an entry with the same
name as a non-data descriptor, the dictionary entry takes precedence.
To make a read-only data descriptor, define both :meth:`__get__` and
:meth:`__set__` with the :meth:`__set__` raising an :exc:`AttributeError` when
called. Defining the :meth:`__set__` method with an exception raising
placeholder is enough to make it 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 automatically
upon attribute access. For example, ``obj.d`` looks up ``d`` in the dictionary
of ``obj``. If ``d`` defines the method :meth:`__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.
Either way, descriptors only work for new style objects and classes. A class is
new style if it is a subclass of :class:`object`.
For objects, the machinery is in :meth:`object.__getattribute__` which
transforms ``b.x`` into ``type(b).__dict__['x'].__get__(b, type(b))``. The
implementation works through a precedence chain that gives data descriptors
priority over instance variables, instance variables priority over non-data
descriptors, and assigns lowest priority to :meth:`__getattr__` if provided. The
full C implementation can be found in :c:func:`PyObject_GenericGetAttr()` in
`Objects/object.c <http://svn.python.org/view/python/trunk/Objects/object.c?view=markup>`_\.
For classes, the machinery is in :meth:`type.__getattribute__` which transforms
``B.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 :meth:`__getattribute__` method
* overriding :meth:`__getattribute__` prevents automatic descriptor calls
* :meth:`__getattribute__` is only available with new style classes and objects
* :meth:`object.__getattribute__` and :meth:`type.__getattribute__` make
different calls to :meth:`__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 :meth:`__getattribute__`
method for invoking descriptors. The call ``super(B, obj).m()`` searches
``obj.__class__.__mro__`` for the base class ``A`` immediately following ``B``
and then returns ``A.__dict__['m'].__get__(obj, A)``. If not a descriptor,
``m`` is returned unchanged. If not in the dictionary, ``m`` reverts to a
search using :meth:`object.__getattribute__`.
Note, in Python 2.2, ``super(B, obj).m()`` would only invoke :meth:`__get__` if
``m`` was a data descriptor. In Python 2.3, non-data descriptors also get
invoked unless an old-style class is involved. The implementation details are
in :c:func:`super_getattro()` in
`Objects/typeobject.c <http://svn.python.org/view/python/trunk/Objects/typeobject.c?view=markup>`_
and a pure Python equivalent can be found in `Guido's Tutorial`_.
.. _`Guido's Tutorial`: http://www.python.org/2.2.3/descrintro.html#cooperation
The details above show that the mechanism for descriptors is embedded in the
:meth:`__getattribute__()` methods for :class:`object`, :class:`type`, and
:func:`super`. Classes inherit this machinery when they derive from
:class:`object` or if they have a meta-class providing similar functionality.
Likewise, classes can turn-off descriptor invocation by overriding
:meth:`__getattribute__()`.
Descriptor Example
------------------
The following code creates a class whose objects are data descriptors which
print a message for each get or set. Overriding :meth:`__getattribute__` is
alternate approach that could do this for every attribute. However, this
descriptor 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 are
so common that they have been packaged into individual function calls.
Properties, bound and unbound methods, static methods, and class methods are all
based on the descriptor protocol.
Properties
----------
Calling :func:`property` is a succinct way of building a data descriptor that
triggers function 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 :func:`property` is implemented in terms of the descriptor protocol,
here is 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
if doc is None and fget is not None:
doc = fget.__doc__
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)
def getter(self, fget):
return type(self)(fget, self.fset, self.fdel, self.__doc__)
def setter(self, fset):
return type(self)(self.fget, fset, self.fdel, self.__doc__)
def deleter(self, fdel):
return type(self)(self.fget, self.fset, fdel, self.__doc__)
The :func:`property` builtin helps whenever a user interface has granted
attribute access and then subsequent changes require the intervention of a
method.
For instance, a spreadsheet class may grant access to a cell value through
``Cell('b10').value``. Subsequent improvements to the program require the cell
to be recalculated on every access; however, the programmer does not want to
affect existing client code accessing the attribute directly. The solution is
to wrap access to the value attribute in a property 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, methods
are written using :keyword:`def` and :keyword:`lambda`, the usual tools for
creating functions. The only difference from regular functions is that the
first argument is reserved for the object instance. By Python convention, the
instance reference is called *self* but may be called *this* or any other
variable name.
To support method calls, functions include the :meth:`__get__` method for
binding methods during attribute access. This means that all functions are
non-data descriptors which return bound or unbound methods depending whether
they are invoked 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 implementation of
:c:type:`PyMethod_Type` in
`Objects/classobject.c <http://svn.python.org/view/python/trunk/Objects/classobject.c?view=markup>`_
is a single object with two different representations depending on whether the
:attr:`im_self` field is set or is *NULL* (the C equivalent of *None*).
Likewise, the effects of calling a method object depend on the :attr:`im_self`
field. If set (meaning bound), the original function (stored in the
:attr:`im_func` field) is called as expected with the first argument set to the
instance. If unbound, all of the arguments are passed unchanged to the original
function. The actual C implementation of :func:`instancemethod_call()` is only
slightly more complex in that it includes some type checking.
Static Methods and Class Methods
--------------------------------
Non-data descriptors provide a simple mechanism for variations on the usual
patterns of binding functions into methods.
To recap, functions have a :meth:`__get__` method so that they can be converted
to a method when accessed as attributes. The non-data descriptor transforms a
``obj.f(*args)`` call into ``f(obj, *args)``. Calling ``klass.f(*args)``
becomes ``f(*args)``.
This chart summarizes the binding and its two most useful variants:
+-----------------+----------------------+------------------+
| Transformation | Called from an | Called from a |
| | Object | Class |
+=================+======================+==================+
| 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 either
``c.f`` or ``C.f`` is the equivalent of a direct lookup into
``object.__getattribute__(c, "f")`` or ``object.__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 the
``self`` variable.
For instance, a statistics package may include a container class for
experimental 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 not depend
on the data. For instance, ``erf(x)`` is handy conversion routine that comes up
in statistical work but does not directly depend on a particular dataset.
It can be called either from an object or the class: ``s.erf(1.5) --> .9332`` or
``Sample.erf(1.5) --> .9332``.
Since staticmethods return the underlying function with no changes, the example
calls 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
:func:`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 the
argument list before calling the function. This format is the same
for 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 class
reference and does not care about any underlying data. One use for classmethods
is to create alternate class constructors. In Python 2.3, the classmethod
:func:`dict.fromkeys` creates a new dictionary from a list of keys. The pure
Python equivalent is::
class Dict(object):
. . .
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 of
:func:`classmethod` 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
|
