| Author: | Raymond Hettinger |
|---|---|
| Contact: | <python at rcn dot com> |
:term:`Descriptors <descriptor>` let objects customize attribute lookup, storage, and deletion.
This guide has four major sections:
- The "primer" gives a basic overview, moving gently from simple examples, adding one feature at a time. Start here if you're new to descriptors.
- The second section shows a complete, practical descriptor example. If you already know the basics, start there.
- The third section provides a more technical tutorial that goes into the detailed mechanics of how descriptors work. Most people don't need this level of detail.
- The last section has pure Python equivalents for built-in descriptors that are written in C. Read this if you're curious about how functions turn into bound methods or about the implementation of common tools like :func:`classmethod`, :func:`staticmethod`, :func:`property`, and :term:`__slots__`.
In this primer, we start with the most basic possible example and then we'll add new capabilities one by one.
The :class:`Ten` class is a descriptor whose :meth:`__get__` method always
returns the constant 10:
.. testcode::
class Ten:
def __get__(self, obj, objtype=None):
return 10
To use the descriptor, it must be stored as a class variable in another class:
.. testcode::
class A:
x = 5 # Regular class attribute
y = Ten() # Descriptor instance
An interactive session shows the difference between normal attribute lookup and descriptor lookup:
>>> a = A() # Make an instance of class A
>>> a.x # Normal attribute lookup
5
>>> a.y # Descriptor lookup
10In the a.x attribute lookup, the dot operator finds 'x': 5
in the class dictionary. In the a.y lookup, the dot operator
finds a descriptor instance, recognized by its __get__ method.
Calling that method returns 10.
Note that the value 10 is not stored in either the class dictionary or the
instance dictionary. Instead, the value 10 is computed on demand.
This example shows how a simple descriptor works, but it isn't very useful. For retrieving constants, normal attribute lookup would be better.
In the next section, we'll create something more useful, a dynamic lookup.
Interesting descriptors typically run computations instead of returning constants:
.. testcode::
import os
class DirectorySize:
def __get__(self, obj, objtype=None):
return len(os.listdir(obj.dirname))
class Directory:
size = DirectorySize() # Descriptor instance
def __init__(self, dirname):
self.dirname = dirname # Regular instance attribute
An interactive session shows that the lookup is dynamic — it computes different, updated answers each time:
>>> s = Directory('songs')
>>> g = Directory('games')
>>> s.size # The songs directory has twenty files
20
>>> g.size # The games directory has three files
3
>>> os.remove('games/chess') # Delete a game
>>> g.size # File count is automatically updated
2
Besides showing how descriptors can run computations, this example also reveals the purpose of the parameters to :meth:`__get__`. The self parameter is size, an instance of DirectorySize. The obj parameter is either g or s, an instance of Directory. It is the obj parameter that lets the :meth:`__get__` method learn the target directory. The objtype parameter is the class Directory.
A popular use for descriptors is managing access to instance data. The descriptor is assigned to a public attribute in the class dictionary while the actual data is stored as a private attribute in the instance dictionary. The descriptor's :meth:`__get__` and :meth:`__set__` methods are triggered when the public attribute is accessed.
In the following example, age is the public attribute and _age is the private attribute. When the public attribute is accessed, the descriptor logs the lookup or update:
.. testcode::
import logging
logging.basicConfig(level=logging.INFO)
class LoggedAgeAccess:
def __get__(self, obj, objtype=None):
value = obj._age
logging.info('Accessing %r giving %r', 'age', value)
return value
def __set__(self, obj, value):
logging.info('Updating %r to %r', 'age', value)
obj._age = value
class Person:
age = LoggedAgeAccess() # Descriptor instance
def __init__(self, name, age):
self.name = name # Regular instance attribute
self.age = age # Calls __set__()
def birthday(self):
self.age += 1 # Calls both __get__() and __set__()
An interactive session shows that all access to the managed attribute age is logged, but that the regular attribute name is not logged:
.. testcode::
:hide:
import logging, sys
logging.basicConfig(level=logging.INFO, stream=sys.stdout, force=True)
>>> mary = Person('Mary M', 30) # The initial age update is logged
INFO:root:Updating 'age' to 30
>>> dave = Person('David D', 40)
INFO:root:Updating 'age' to 40
>>> vars(mary) # The actual data is in a private attribute
{'name': 'Mary M', '_age': 30}
>>> vars(dave)
{'name': 'David D', '_age': 40}
>>> mary.age # Access the data and log the lookup
INFO:root:Accessing 'age' giving 30
30
>>> mary.birthday() # Updates are logged as well
INFO:root:Accessing 'age' giving 30
INFO:root:Updating 'age' to 31
>>> dave.name # Regular attribute lookup isn't logged
'David D'
>>> dave.age # Only the managed attribute is logged
INFO:root:Accessing 'age' giving 40
40One major issue with this example is that the private name _age is hardwired in the LoggedAgeAccess class. That means that each instance can only have one logged attribute and that its name is unchangeable. In the next example, we'll fix that problem.
When a class uses descriptors, it can inform each descriptor about which variable name was used.
In this example, the :class:`Person` class has two descriptor instances, name and age. When the :class:`Person` class is defined, it makes a callback to :meth:`__set_name__` in LoggedAccess so that the field names can be recorded, giving each descriptor its own public_name and private_name:
.. testcode::
import logging
logging.basicConfig(level=logging.INFO)
class LoggedAccess:
def __set_name__(self, owner, name):
self.public_name = name
self.private_name = '_' + name
def __get__(self, obj, objtype=None):
value = getattr(obj, self.private_name)
logging.info('Accessing %r giving %r', self.public_name, value)
return value
def __set__(self, obj, value):
logging.info('Updating %r to %r', self.public_name, value)
setattr(obj, self.private_name, value)
class Person:
name = LoggedAccess() # First descriptor instance
age = LoggedAccess() # Second descriptor instance
def __init__(self, name, age):
self.name = name # Calls the first descriptor
self.age = age # Calls the second descriptor
def birthday(self):
self.age += 1
An interactive session shows that the :class:`Person` class has called :meth:`__set_name__` so that the field names would be recorded. Here we call :func:`vars` to look up the descriptor without triggering it:
>>> vars(vars(Person)['name'])
{'public_name': 'name', 'private_name': '_name'}
>>> vars(vars(Person)['age'])
{'public_name': 'age', 'private_name': '_age'}The new class now logs access to both name and age:
.. testcode::
:hide:
import logging, sys
logging.basicConfig(level=logging.INFO, stream=sys.stdout, force=True)
>>> pete = Person('Peter P', 10)
INFO:root:Updating 'name' to 'Peter P'
INFO:root:Updating 'age' to 10
>>> kate = Person('Catherine C', 20)
INFO:root:Updating 'name' to 'Catherine C'
INFO:root:Updating 'age' to 20The two Person instances contain only the private names:
>>> vars(pete)
{'_name': 'Peter P', '_age': 10}
>>> vars(kate)
{'_name': 'Catherine C', '_age': 20}A :term:`descriptor` is what we call any object that defines :meth:`__get__`, :meth:`__set__`, or :meth:`__delete__`.
Optionally, descriptors can have a :meth:`__set_name__` method. This is only used in cases where a descriptor needs to know either the class where it was created or the name of class variable it was assigned to. (This method, if present, is called even if the class is not a descriptor.)
Descriptors get invoked by the dot operator during attribute lookup. If a
descriptor is accessed indirectly with vars(some_class)[descriptor_name],
the descriptor instance is returned without invoking it.
Descriptors only work when used as class variables. When put in instances, they have no effect.
The main motivation for descriptors is to provide a hook allowing objects stored in class variables to control what happens during attribute lookup.
Traditionally, the calling class controls what happens during lookup. Descriptors invert that relationship and allow the data being looked-up to have a say in the matter.
Descriptors are used throughout the language. It is how functions turn into bound methods. Common tools like :func:`classmethod`, :func:`staticmethod`, :func:`property`, and :func:`functools.cached_property` are all implemented as descriptors.
In this example, we create a practical and powerful tool for locating notoriously hard to find data corruption bugs.
A validator is a descriptor for managed attribute access. Prior to storing any data, it verifies that the new value meets various type and range restrictions. If those restrictions aren't met, it raises an exception to prevent data corruption at its source.
This :class:`Validator` class is both an :term:`abstract base class` and a managed attribute descriptor:
.. testcode::
from abc import ABC, abstractmethod
class Validator(ABC):
def __set_name__(self, owner, name):
self.private_name = '_' + name
def __get__(self, obj, objtype=None):
return getattr(obj, self.private_name)
def __set__(self, obj, value):
self.validate(value)
setattr(obj, self.private_name, value)
@abstractmethod
def validate(self, value):
pass
Custom validators need to inherit from :class:`Validator` and must supply a :meth:`validate` method to test various restrictions as needed.
Here are three practical data validation utilities:
- :class:`OneOf` verifies that a value is one of a restricted set of options.
- :class:`Number` verifies that a value is either an :class:`int` or :class:`float`. Optionally, it verifies that a value is between a given minimum or maximum.
- :class:`String` verifies that a value is a :class:`str`. Optionally, it validates a given minimum or maximum length. It can validate a user-defined predicate as well.
.. testcode::
class OneOf(Validator):
def __init__(self, *options):
self.options = set(options)
def validate(self, value):
if value not in self.options:
raise ValueError(f'Expected {value!r} to be one of {self.options!r}')
class Number(Validator):
def __init__(self, minvalue=None, maxvalue=None):
self.minvalue = minvalue
self.maxvalue = maxvalue
def validate(self, value):
if not isinstance(value, (int, float)):
raise TypeError(f'Expected {value!r} to be an int or float')
if self.minvalue is not None and value < self.minvalue:
raise ValueError(
f'Expected {value!r} to be at least {self.minvalue!r}'
)
if self.maxvalue is not None and value > self.maxvalue:
raise ValueError(
f'Expected {value!r} to be no more than {self.maxvalue!r}'
)
class String(Validator):
def __init__(self, minsize=None, maxsize=None, predicate=None):
self.minsize = minsize
self.maxsize = maxsize
self.predicate = predicate
def validate(self, value):
if not isinstance(value, str):
raise TypeError(f'Expected {value!r} to be an str')
if self.minsize is not None and len(value) < self.minsize:
raise ValueError(
f'Expected {value!r} to be no smaller than {self.minsize!r}'
)
if self.maxsize is not None and len(value) > self.maxsize:
raise ValueError(
f'Expected {value!r} to be no bigger than {self.maxsize!r}'
)
if self.predicate is not None and not self.predicate(value):
raise ValueError(
f'Expected {self.predicate} to be true for {value!r}'
)
Here's how the data validators can be used in a real class:
.. testcode::
class Component:
name = String(minsize=3, maxsize=10, predicate=str.isupper)
kind = OneOf('wood', 'metal', 'plastic')
quantity = Number(minvalue=0)
def __init__(self, name, kind, quantity):
self.name = name
self.kind = kind
self.quantity = quantity
The descriptors prevent invalid instances from being created:
>>> Component('Widget', 'metal', 5) # Blocked: 'Widget' is not all uppercase
Traceback (most recent call last):
...
ValueError: Expected <method 'isupper' of 'str' objects> to be true for 'Widget'
>>> Component('WIDGET', 'metle', 5) # Blocked: 'metle' is misspelled
Traceback (most recent call last):
...
ValueError: Expected 'metle' to be one of {'metal', 'plastic', 'wood'}
>>> Component('WIDGET', 'metal', -5) # Blocked: -5 is negative
Traceback (most recent call last):
...
ValueError: Expected -5 to be at least 0
>>> Component('WIDGET', 'metal', 'V') # Blocked: 'V' isn't a number
Traceback (most recent call last):
...
TypeError: Expected 'V' to be an int or float
>>> c = Component('WIDGET', 'metal', 5) # Allowed: The inputs are validWhat follows is a more technical tutorial for the mechanics and details of how descriptors work.
Defines descriptors, summarizes the protocol, and shows how descriptors are called. Provides an example showing how object relational mappings work.
Learning about descriptors not only provides access to a larger toolset, it creates a deeper understanding of how Python works.
In general, a descriptor is an attribute value that has one of the methods in the descriptor protocol. Those methods are :meth:`__get__`, :meth:`__set__`, and :meth:`__delete__`. If any of those methods are defined for an attribute, it is said to be a :term:`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 method resolution order of type(a). 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.
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. Descriptors simplify the underlying C code and offer a flexible set of new tools for everyday Python programs.
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 :meth:`__set__` or :meth:`__delete__`, it is considered a data descriptor. Descriptors that only define :meth:`__get__` are called non-data descriptors (they are often 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.
A descriptor can be called directly with desc.__get__(obj) or
desc.__get__(None, cls).
But it is more common for a descriptor to be invoked automatically from attribute access.
The expression obj.x looks up the attribute x in the chain of
namespaces for obj. If the search finds a descriptor outside of the
instance __dict__, its :meth:`__get__` method is invoked according to the
precedence rules listed below.
The details of invocation depend on whether obj is an object, class, or
instance of super.
Instance lookup scans through a chain of namespaces giving data descriptors the highest priority, followed by instance variables, then non-data descriptors, then class variables, and lastly :meth:`__getattr__` if it is provided.
If a descriptor is found for a.x, then it is invoked with:
desc.__get__(a, type(a)).
The logic for a dotted lookup is in :meth:`object.__getattribute__`. Here is a pure Python equivalent:
.. testcode::
def find_name_in_mro(cls, name, default):
"Emulate _PyType_Lookup() in Objects/typeobject.c"
for base in cls.__mro__:
if name in vars(base):
return vars(base)[name]
return default
def object_getattribute(obj, name):
"Emulate PyObject_GenericGetAttr() in Objects/object.c"
null = object()
objtype = type(obj)
cls_var = find_name_in_mro(objtype, name, null)
descr_get = getattr(type(cls_var), '__get__', null)
if descr_get is not null:
if (hasattr(type(cls_var), '__set__')
or hasattr(type(cls_var), '__delete__')):
return descr_get(cls_var, obj, objtype) # data descriptor
if hasattr(obj, '__dict__') and name in vars(obj):
return vars(obj)[name] # instance variable
if descr_get is not null:
return descr_get(cls_var, obj, objtype) # non-data descriptor
if cls_var is not null:
return cls_var # class variable
raise AttributeError(name)
.. testcode::
:hide:
# Test the fidelity of object_getattribute() by comparing it with the
# normal object.__getattribute__(). The former will be accessed by
# square brackets and the latter by the dot operator.
class Object:
def __getitem__(obj, name):
try:
return object_getattribute(obj, name)
except AttributeError:
if not hasattr(type(obj), '__getattr__'):
raise
return type(obj).__getattr__(obj, name) # __getattr__
class DualOperator(Object):
x = 10
def __init__(self, z):
self.z = z
@property
def p2(self):
return 2 * self.x
@property
def p3(self):
return 3 * self.x
def m5(self, y):
return 5 * y
def m7(self, y):
return 7 * y
def __getattr__(self, name):
return ('getattr_hook', self, name)
class DualOperatorWithSlots:
__getitem__ = Object.__getitem__
__slots__ = ['z']
x = 15
def __init__(self, z):
self.z = z
@property
def p2(self):
return 2 * self.x
def m5(self, y):
return 5 * y
def __getattr__(self, name):
return ('getattr_hook', self, name)
class D1:
def __get__(self, obj, objtype=None):
return type(self), obj, objtype
class U1:
x = D1()
class U2(U1):
pass
>>> a = DualOperator(11)
>>> vars(a).update(p3 = '_p3', m7 = '_m7')
>>> a.x == a['x'] == 10
True
>>> a.z == a['z'] == 11
True
>>> a.p2 == a['p2'] == 20
True
>>> a.p3 == a['p3'] == 30
True
>>> a.m5(100) == a.m5(100) == 500
True
>>> a.m7 == a['m7'] == '_m7'
True
>>> a.g == a['g'] == ('getattr_hook', a, 'g')
True
>>> b = DualOperatorWithSlots(22)
>>> b.x == b['x'] == 15
True
>>> b.z == b['z'] == 22
True
>>> b.p2 == b['p2'] == 30
True
>>> b.m5(200) == b['m5'](200) == 1000
True
>>> b.g == b['g'] == ('getattr_hook', b, 'g')
True
>>> u2 = U2()
>>> object_getattribute(u2, 'x') == u2.x == (D1, u2, U2)
TrueNote, there is no :meth:`__getattr__` hook in the :meth:`__getattribute__`
code. That is why calling :meth:`__getattribute__` directly or with
super().__getattribute__ will bypass :meth:`__getattr__` entirely.
Instead, it is the dot operator and the :func:`getattr` function that are responsible for invoking :meth:`__getattr__` whenever :meth:`__getattribute__` raises an :exc:`AttributeError`. Their logic is encapsulated in a helper function:
.. testcode::
def getattr_hook(obj, name):
"Emulate slot_tp_getattr_hook() in Objects/typeobject.c"
try:
return obj.__getattribute__(name)
except AttributeError:
if not hasattr(type(obj), '__getattr__'):
raise
return type(obj).__getattr__(obj, name) # __getattr__
>>> class ClassWithGetAttr:
... x = 123
... def __getattr__(self, attr):
... return attr.upper()
...
>>> cw = ClassWithGetAttr()
>>> cw.y = 456
>>> getattr_hook(cw, 'x')
123
>>> getattr_hook(cw, 'y')
456
>>> getattr_hook(cw, 'z')
'Z'
>>> class ClassWithoutGetAttr:
... x = 123
...
>>> cwo = ClassWithoutGetAttr()
>>> cwo.y = 456
>>> getattr_hook(cwo, 'x')
123
>>> getattr_hook(cwo, 'y')
456
>>> getattr_hook(cwo, 'z')
Traceback (most recent call last):
...
AttributeError: 'ClassWithoutGetAttr' object has no attribute 'z'The logic for a dotted lookup such as A.x is in
:meth:`type.__getattribute__`. The steps are similar to those for
:meth:`object.__getattribute__` but the instance dictionary lookup is replaced
by a search through the class's :term:`method resolution order`.
If a descriptor is found, it is invoked with desc.__get__(None, A).
The full C implementation can be found in :c:func:`type_getattro()` and :c:func:`_PyType_Lookup()` in :source:`Objects/typeobject.c`.
The logic for super's dotted lookup is in the :meth:`__getattribute__` method for object returned by :class:`super()`.
A dotted lookup such as super(A, obj).m searches obj.__class__.__mro__
for the base class B immediately following A and then returns
B.__dict__['m'].__get__(obj, A). If not a descriptor, m is returned
unchanged.
The full C implementation can be found in :c:func:`super_getattro()` in :source:`Objects/typeobject.c`. A pure Python equivalent can be found in Guido's Tutorial.
The mechanism for descriptors is embedded in the :meth:`__getattribute__()` methods for :class:`object`, :class:`type`, and :func:`super`.
The important points to remember are:
- Descriptors are invoked by the :meth:`__getattribute__` method.
- Classes inherit this machinery from :class:`object`, :class:`type`, or :func:`super`.
- Overriding :meth:`__getattribute__` prevents automatic descriptor calls because all the descriptor logic is in that method.
- :meth:`object.__getattribute__` and :meth:`type.__getattribute__` make
different calls to :meth:`__get__`. The first includes the instance and may
include the class. The second puts in
Nonefor the instance and always includes the class. - Data descriptors always override instance dictionaries.
- Non-data descriptors may be overridden by instance dictionaries.
Sometimes it is desirable for a descriptor to know what class variable name it was assigned to. When a new class is created, the :class:`type` metaclass scans the dictionary of the new class. If any of the entries are descriptors and if they define :meth:`__set_name__`, that method is called with two arguments. The owner is the class where the descriptor is used, and the name is the class variable the descriptor was assigned to.
The implementation details are in :c:func:`type_new()` and :c:func:`set_names()` in :source:`Objects/typeobject.c`.
Since the update logic is in :meth:`type.__new__`, notifications only take place at the time of class creation. If descriptors are added to the class afterwards, :meth:`__set_name__` will need to be called manually.
The following code is a simplified skeleton showing how data descriptors could be used to implement an object relational mapping.
The essential idea is that the data is stored in an external database. The Python instances only hold keys to the database's tables. Descriptors take care of lookups or updates:
.. testcode::
class Field:
def __set_name__(self, owner, name):
self.fetch = f'SELECT {name} FROM {owner.table} WHERE {owner.key}=?;'
self.store = f'UPDATE {owner.table} SET {name}=? WHERE {owner.key}=?;'
def __get__(self, obj, objtype=None):
return conn.execute(self.fetch, [obj.key]).fetchone()[0]
def __set__(self, obj, value):
conn.execute(self.store, [value, obj.key])
conn.commit()
We can use the :class:`Field` class to define models that describe the schema for each table in a database:
.. testcode::
class Movie:
table = 'Movies' # Table name
key = 'title' # Primary key
director = Field()
year = Field()
def __init__(self, key):
self.key = key
class Song:
table = 'Music'
key = 'title'
artist = Field()
year = Field()
genre = Field()
def __init__(self, key):
self.key = key
To use the models, first connect to the database:
>>> import sqlite3
>>> conn = sqlite3.connect('entertainment.db')
An interactive session shows how data is retrieved from the database and how it can be updated:
.. testsetup::
song_data = [
('Country Roads', 'John Denver', 1972),
('Me and Bobby McGee', 'Janice Joplin', 1971),
('Coal Miners Daughter', 'Loretta Lynn', 1970),
]
movie_data = [
('Star Wars', 'George Lucas', 1977),
('Jaws', 'Steven Spielberg', 1975),
('Aliens', 'James Cameron', 1986),
]
import sqlite3
conn = sqlite3.connect(':memory:')
conn.execute('CREATE TABLE Music (title text, artist text, year integer);')
conn.execute('CREATE INDEX MusicNdx ON Music (title);')
conn.executemany('INSERT INTO Music VALUES (?, ?, ?);', song_data)
conn.execute('CREATE TABLE Movies (title text, director text, year integer);')
conn.execute('CREATE INDEX MovieNdx ON Music (title);')
conn.executemany('INSERT INTO Movies VALUES (?, ?, ?);', movie_data)
conn.commit()
>>> Movie('Star Wars').director
'George Lucas'
>>> jaws = Movie('Jaws')
>>> f'Released in {jaws.year} by {jaws.director}'
'Released in 1975 by Steven Spielberg'
>>> Song('Country Roads').artist
'John Denver'
>>> Movie('Star Wars').director = 'J.J. Abrams'
>>> Movie('Star Wars').director
'J.J. Abrams'The descriptor protocol is simple and offers exciting possibilities. Several use cases are so common that they have been prepackaged into built-in tools. Properties, bound methods, static methods, class methods, and __slots__ are all based on the descriptor protocol.
Calling :func:`property` is a succinct way of building a data descriptor that triggers a function call upon access to an attribute. Its signature is:
property(fget=None, fset=None, fdel=None, doc=None) -> property
The documentation shows a typical use to define a managed attribute x:
.. testcode::
class C:
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.")
>>> C.x.__doc__
"I'm the 'x' property."
>>> c.x = 2.71828
>>> c.x
2.71828
>>> del c.x
>>> c.x
Traceback (most recent call last):
...
AttributeError: 'C' object has no attribute '_C__x'To see how :func:`property` is implemented in terms of the descriptor protocol, here is a pure Python equivalent:
.. testcode::
class Property:
"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
self._name = ''
def __set_name__(self, owner, name):
self._name = name
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError(f"property '{self._name}' has no getter")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError(f"property '{self._name}' has no setter")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError(f"property '{self._name}' has no deleter")
self.fdel(obj)
def getter(self, fget):
prop = type(self)(fget, self.fset, self.fdel, self.__doc__)
prop._name = self._name
return prop
def setter(self, fset):
prop = type(self)(self.fget, fset, self.fdel, self.__doc__)
prop._name = self._name
return prop
def deleter(self, fdel):
prop = type(self)(self.fget, self.fset, fdel, self.__doc__)
prop._name = self._name
return prop
.. testcode::
:hide:
# Verify the Property() emulation
class CC:
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.")
# Now do it again but use the decorator style
class CCC:
@Property
def x(self):
return self.__x
@x.setter
def x(self, value):
self.__x = value
@x.deleter
def x(self):
del self.__x
>>> cc = CC()
>>> hasattr(cc, 'x')
False
>>> cc.x = 33
>>> cc.x
33
>>> del cc.x
>>> hasattr(cc, 'x')
False
>>> ccc = CCC()
>>> hasattr(ccc, 'x')
False
>>> ccc.x = 333
>>> ccc.x == 333
True
>>> del ccc.x
>>> hasattr(ccc, 'x')
FalseThe :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:
.. testcode::
class Cell:
...
@property
def value(self):
"Recalculate the cell before returning value"
self.recalc()
return self._value
Either the built-in :func:`property` or our :func:`Property` equivalent would work in this example.
Python's object oriented features are built upon a function based environment. Using non-data descriptors, the two are merged seamlessly.
Functions stored in class dictionaries get turned into methods when invoked. Methods only differ from regular functions in that the object instance is prepended to the other arguments. By convention, the instance is called self but could be called this or any other variable name.
Methods can be created manually with :class:`types.MethodType` which is roughly equivalent to:
.. testcode::
class MethodType:
"Emulate PyMethod_Type in Objects/classobject.c"
def __init__(self, func, obj):
self.__func__ = func
self.__self__ = obj
def __call__(self, *args, **kwargs):
func = self.__func__
obj = self.__self__
return func(obj, *args, **kwargs)
To support automatic creation of methods, functions include the :meth:`__get__` method for binding methods during attribute access. This means that functions are non-data descriptors that return bound methods during dotted lookup from an instance. Here's how it works:
.. testcode::
class Function:
...
def __get__(self, obj, objtype=None):
"Simulate func_descr_get() in Objects/funcobject.c"
if obj is None:
return self
return MethodType(self, obj)
Running the following class in the interpreter shows how the function descriptor works in practice:
.. testcode::
class D:
def f(self, x):
return x
The function has a :term:`qualified name` attribute to support introspection:
>>> D.f.__qualname__
'D.f'Accessing the function through the class dictionary does not invoke :meth:`__get__`. Instead, it just returns the underlying function object:
>>> D.__dict__['f'] <function D.f at 0x00C45070>
Dotted access from a class calls :meth:`__get__` which just returns the underlying function unchanged:
>>> D.f <function D.f at 0x00C45070>
The interesting behavior occurs during dotted access from an instance. The dotted lookup calls :meth:`__get__` which returns a bound method object:
>>> d = D() >>> d.f <bound method D.f of <__main__.D object at 0x00B18C90>>
Internally, the bound method stores the underlying function and the bound instance:
>>> d.f.__func__ <function D.f at 0x00C45070> >>> d.f.__self__ <__main__.D object at 0x1012e1f98>
If you have ever wondered where self comes from in regular methods or where cls comes from in class methods, this is it!
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 an
obj.f(*args) call into f(obj, *args). Calling cls.f(*args)
becomes f(*args).
This chart summarizes the binding and its two most useful variants:
Transformation Called from an object Called from a class function f(obj, *args) f(*args) staticmethod f(*args) f(*args) classmethod f(type(obj), *args) f(cls, *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 static methods return the underlying function with no changes, the example calls are unexciting:
.. testcode::
class E:
@staticmethod
def f(x):
return x * 10
>>> E.f(3)
30
>>> E().f(3)
30Using the non-data descriptor protocol, a pure Python version of :func:`staticmethod` would look like this:
.. testcode::
import functools
class StaticMethod:
"Emulate PyStaticMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
functools.update_wrapper(self, f)
def __get__(self, obj, objtype=None):
return self.f
def __call__(self, *args, **kwds):
return self.f(*args, **kwds)
The :func:`functools.update_wrapper` call adds a __wrapped__ attribute
that refers to the underlying function. Also it carries forward
the attributes necessary to make the wrapper look like the wrapped
function: __name__, __qualname__, __doc__, and __annotations__.
.. testcode::
:hide:
class E_sim:
@StaticMethod
def f(x: int) -> str:
"Simple function example"
return "!" * x
wrapped_ord = StaticMethod(ord)
>>> E_sim.f(3)
'!!!'
>>> E_sim().f(3)
'!!!'
>>> sm = vars(E_sim)['f']
>>> type(sm).__name__
'StaticMethod'
>>> f = E_sim.f
>>> type(f).__name__
'function'
>>> sm.__name__
'f'
>>> f.__name__
'f'
>>> sm.__qualname__
'E_sim.f'
>>> f.__qualname__
'E_sim.f'
>>> sm.__doc__
'Simple function example'
>>> f.__doc__
'Simple function example'
>>> sm.__annotations__
{'x': <class 'int'>, 'return': <class 'str'>}
>>> f.__annotations__
{'x': <class 'int'>, 'return': <class 'str'>}
>>> sm.__module__ == f.__module__
True
>>> sm(3)
'!!!'
>>> f(3)
'!!!'
>>> wrapped_ord('A')
65
>>> wrapped_ord.__module__ == ord.__module__
True
>>> wrapped_ord.__wrapped__ == ord
True
>>> wrapped_ord.__name__ == ord.__name__
True
>>> wrapped_ord.__qualname__ == ord.__qualname__
True
>>> wrapped_ord.__doc__ == ord.__doc__
TrueUnlike 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:
.. testcode::
class F:
@classmethod
def f(cls, x):
return cls.__name__, x
>>> F.f(3)
('F', 3)
>>> F().f(3)
('F', 3)This behavior is useful whenever the method only needs to have a class reference and does not rely on data stored in a specific instance. One use for class methods is to create alternate class constructors. For example, the classmethod :func:`dict.fromkeys` creates a new dictionary from a list of keys. The pure Python equivalent is:
.. testcode::
class Dict(dict):
@classmethod
def fromkeys(cls, iterable, value=None):
"Emulate dict_fromkeys() in Objects/dictobject.c"
d = cls()
for key in iterable:
d[key] = value
return d
Now a new dictionary of unique keys can be constructed like this:
>>> d = Dict.fromkeys('abracadabra')
>>> type(d) is Dict
True
>>> d
{'a': None, 'b': None, 'r': None, 'c': None, 'd': None}Using the non-data descriptor protocol, a pure Python version of :func:`classmethod` would look like this:
.. testcode::
import functools
class ClassMethod:
"Emulate PyClassMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
functools.update_wrapper(self, f)
def __get__(self, obj, cls=None):
if cls is None:
cls = type(obj)
if hasattr(type(self.f), '__get__'):
# This code path was added in Python 3.9
# and was deprecated in Python 3.11.
return self.f.__get__(cls, cls)
return MethodType(self.f, cls)
.. testcode::
:hide:
# Verify the emulation works
class T:
@ClassMethod
def cm(cls, x: int, y: str) -> tuple[str, int, str]:
"Class method that returns a tuple"
return (cls.__name__, x, y)
@ClassMethod
@property
def __doc__(cls):
return f'A doc for {cls.__name__!r}'
>>> T.cm(11, 22)
('T', 11, 22)
# Also call it from an instance
>>> t = T()
>>> t.cm(11, 22)
('T', 11, 22)
# Check the alternate path for chained descriptors
>>> T.__doc__
"A doc for 'T'"
# Verify that T uses our emulation
>>> type(vars(T)['cm']).__name__
'ClassMethod'
# Verify that update_wrapper() correctly copied attributes
>>> T.cm.__name__
'cm'
>>> T.cm.__qualname__
'T.cm'
>>> T.cm.__doc__
'Class method that returns a tuple'
>>> T.cm.__annotations__
{'x': <class 'int'>, 'y': <class 'str'>, 'return': tuple[str, int, str]}
# Verify that __wrapped__ was added and works correctly
>>> f = vars(T)['cm'].__wrapped__
>>> type(f).__name__
'function'
>>> f.__name__
'cm'
>>> f(T, 11, 22)
('T', 11, 22)The code path for hasattr(type(self.f), '__get__') was added in
Python 3.9 and makes it possible for :func:`classmethod` to support
chained decorators. For example, a classmethod and property could be
chained together. In Python 3.11, this functionality was deprecated.
.. testcode::
class G:
@classmethod
@property
def __doc__(cls):
return f'A doc for {cls.__name__!r}'
>>> G.__doc__
"A doc for 'G'"The :func:`functools.update_wrapper` call in ClassMethod adds a
__wrapped__ attribute that refers to the underlying function. Also
it carries forward the attributes necessary to make the wrapper look
like the wrapped function: __name__, __qualname__, __doc__,
and __annotations__.
When a class defines __slots__, it replaces instance dictionaries with a
fixed-length array of slot values. From a user point of view that has
several effects:
1. Provides immediate detection of bugs due to misspelled attribute
assignments. Only attribute names specified in __slots__ are allowed:
.. testcode::
class Vehicle:
__slots__ = ('id_number', 'make', 'model')
>>> auto = Vehicle()
>>> auto.id_nubmer = 'VYE483814LQEX'
Traceback (most recent call last):
...
AttributeError: 'Vehicle' object has no attribute 'id_nubmer'2. Helps create immutable objects where descriptors manage access to private
attributes stored in __slots__:
.. testcode::
class Immutable:
__slots__ = ('_dept', '_name') # Replace the instance dictionary
def __init__(self, dept, name):
self._dept = dept # Store to private attribute
self._name = name # Store to private attribute
@property # Read-only descriptor
def dept(self):
return self._dept
@property
def name(self): # Read-only descriptor
return self._name
>>> mark = Immutable('Botany', 'Mark Watney')
>>> mark.dept
'Botany'
>>> mark.dept = 'Space Pirate'
Traceback (most recent call last):
...
AttributeError: property 'dept' of 'Immutable' object has no setter
>>> mark.location = 'Mars'
Traceback (most recent call last):
...
AttributeError: 'Immutable' object has no attribute 'location'3. Saves memory. On a 64-bit Linux build, an instance with two attributes
takes 48 bytes with __slots__ and 152 bytes without. This flyweight
design pattern likely only
matters when a large number of instances are going to be created.
4. Improves speed. Reading instance variables is 35% faster with
__slots__ (as measured with Python 3.10 on an Apple M1 processor).
5. Blocks tools like :func:`functools.cached_property` which require an instance dictionary to function correctly:
.. testcode::
from functools import cached_property
class CP:
__slots__ = () # Eliminates the instance dict
@cached_property # Requires an instance dict
def pi(self):
return 4 * sum((-1.0)**n / (2.0*n + 1.0)
for n in reversed(range(100_000)))
>>> CP().pi
Traceback (most recent call last):
...
TypeError: No '__dict__' attribute on 'CP' instance to cache 'pi' property.It is not possible to create an exact drop-in pure Python version of
__slots__ because it requires direct access to C structures and control
over object memory allocation. However, we can build a mostly faithful
simulation where the actual C structure for slots is emulated by a private
_slotvalues list. Reads and writes to that private structure are managed
by member descriptors:
.. testcode::
null = object()
class Member:
def __init__(self, name, clsname, offset):
'Emulate PyMemberDef in Include/structmember.h'
# Also see descr_new() in Objects/descrobject.c
self.name = name
self.clsname = clsname
self.offset = offset
def __get__(self, obj, objtype=None):
'Emulate member_get() in Objects/descrobject.c'
# Also see PyMember_GetOne() in Python/structmember.c
if obj is None:
return self
value = obj._slotvalues[self.offset]
if value is null:
raise AttributeError(self.name)
return value
def __set__(self, obj, value):
'Emulate member_set() in Objects/descrobject.c'
obj._slotvalues[self.offset] = value
def __delete__(self, obj):
'Emulate member_delete() in Objects/descrobject.c'
value = obj._slotvalues[self.offset]
if value is null:
raise AttributeError(self.name)
obj._slotvalues[self.offset] = null
def __repr__(self):
'Emulate member_repr() in Objects/descrobject.c'
return f'<Member {self.name!r} of {self.clsname!r}>'
The :meth:`type.__new__` method takes care of adding member objects to class variables:
.. testcode::
class Type(type):
'Simulate how the type metaclass adds member objects for slots'
def __new__(mcls, clsname, bases, mapping, **kwargs):
'Emulate type_new() in Objects/typeobject.c'
# type_new() calls PyTypeReady() which calls add_methods()
slot_names = mapping.get('slot_names', [])
for offset, name in enumerate(slot_names):
mapping[name] = Member(name, clsname, offset)
return type.__new__(mcls, clsname, bases, mapping, **kwargs)
The :meth:`object.__new__` method takes care of creating instances that have slots instead of an instance dictionary. Here is a rough simulation in pure Python:
.. testcode::
class Object:
'Simulate how object.__new__() allocates memory for __slots__'
def __new__(cls, *args, **kwargs):
'Emulate object_new() in Objects/typeobject.c'
inst = super().__new__(cls)
if hasattr(cls, 'slot_names'):
empty_slots = [null] * len(cls.slot_names)
object.__setattr__(inst, '_slotvalues', empty_slots)
return inst
def __setattr__(self, name, value):
'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
cls = type(self)
if hasattr(cls, 'slot_names') and name not in cls.slot_names:
raise AttributeError(
f'{cls.__name__!r} object has no attribute {name!r}'
)
super().__setattr__(name, value)
def __delattr__(self, name):
'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
cls = type(self)
if hasattr(cls, 'slot_names') and name not in cls.slot_names:
raise AttributeError(
f'{cls.__name__!r} object has no attribute {name!r}'
)
super().__delattr__(name)
To use the simulation in a real class, just inherit from :class:`Object` and set the :term:`metaclass` to :class:`Type`:
.. testcode::
class H(Object, metaclass=Type):
'Instance variables stored in slots'
slot_names = ['x', 'y']
def __init__(self, x, y):
self.x = x
self.y = y
At this point, the metaclass has loaded member objects for x and y:
>>> from pprint import pp
>>> pp(dict(vars(H)))
{'__module__': '__main__',
'__doc__': 'Instance variables stored in slots',
'slot_names': ['x', 'y'],
'__init__': <function H.__init__ at 0x7fb5d302f9d0>,
'x': <Member 'x' of 'H'>,
'y': <Member 'y' of 'H'>}
# We test this separately because the preceding section is not
# doctestable due to the hex memory address for the __init__ function
>>> isinstance(vars(H)['x'], Member)
True
>>> isinstance(vars(H)['y'], Member)
TrueWhen instances are created, they have a slot_values list where the
attributes are stored:
>>> h = H(10, 20)
>>> vars(h)
{'_slotvalues': [10, 20]}
>>> h.x = 55
>>> vars(h)
{'_slotvalues': [55, 20]}Misspelled or unassigned attributes will raise an exception:
>>> h.xz
Traceback (most recent call last):
...
AttributeError: 'H' object has no attribute 'xz' # Examples for deleted attributes are not shown because this section
# is already a bit lengthy. We still test that code here.
>>> del h.x
>>> hasattr(h, 'x')
False
# Also test the code for uninitialized slots
>>> class HU(Object, metaclass=Type):
... slot_names = ['x', 'y']
...
>>> hu = HU()
>>> hasattr(hu, 'x')
False
>>> hasattr(hu, 'y')
False