Guia de descritores¶
- Autor:
Raymond Hettinger
- Contato:
<python at rcn dot com>
Descritores permitem que os objetos personalizem a consulta, o armazenamento e a exclusão de atributos.
Este guia tem quatro seções principais:
O “primer” oferece uma visão geral básica, movendo-se suavemente a partir de exemplos simples, adicionando um recurso de cada vez. Comece aqui se você for novo em descritores.
A segunda seção mostra um exemplo de descritor prático completo. Se você já conhece o básico, comece por aí.
A terceira seção fornece um tutorial mais técnico que aborda a mecânica detalhada de como os descritores funcionam. A maioria das pessoas não precisa desse nível de detalhe.
A última seção tem equivalentes puros de Python para descritores embutidos que são escritos em C. Leia isto se estiver curioso sobre como as funções se transformam em métodos vinculados ou sobre a implementação de ferramentas comuns como
classmethod()
,staticmethod()
,property()
e __slots__.
Primer¶
Neste primer, começamos com o exemplo mais básico possível e, em seguida, adicionaremos novos recursos um por um.
Exemplo simples: um descritor que retorna uma constante¶
The Ten
class is a descriptor whose __get__()
method always
returns the constant 10
:
class Ten:
def __get__(self, obj, objtype=None):
return 10
Para usar o descritor, ele deve ser armazenado como uma variável de classe em outra classe:
class A:
x = 5 # Regular class attribute
y = Ten() # Descriptor instance
Uma sessão interativa mostra a diferença entre a pesquisa de atributo normal e a pesquisa de descritor:
>>> a = A() # Make an instance of class A
>>> a.x # Normal attribute lookup
5
>>> a.y # Descriptor lookup
10
In 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
.
Observe que o valor 10
não é armazenado no dicionário da classe ou no dicionário da instância. Em vez disso, o valor 10
é calculado sob demanda.
Este exemplo mostra como funciona um descritor simples, mas não é muito útil. Para recuperar constantes, a pesquisa de atributo normal seria melhor.
Na próxima seção, criaremos algo mais útil, uma pesquisa dinâmica.
Pesquisas dinâmicas¶
Descritores interessantes normalmente executam cálculos em vez de retornar constantes:
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
Uma sessão interativa mostra que a pesquisa é dinâmica – calcula respostas diferentes e atualizadas a cada vez:
>>> 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
Além de mostrar como os descritores podem executar cálculos, este exemplo também revela o propósito dos parâmetros para __get__()
. O parâmetro self é size, uma instância de DirectorySize. O parâmetro obj é g ou s, uma instância de Directory. É o parâmetro obj que permite ao método __get__()
aprender o diretório de destino. O parâmetro objtype é a classe Directory.
Atributos gerenciados¶
Um uso popular para descritores é gerenciar o acesso aos dados da instância. O descritor é atribuído a um atributo público no dicionário da classe, enquanto os dados reais são armazenados como um atributo privado no dicionário da instância. Os métodos __get__()
e __set__()
do descritor são disparados quando o atributo público é acessado.
No exemplo a seguir, age é o atributo público e _age é o atributo privado. Quando o atributo público é acessado, o descritor registra a pesquisa ou atualização:
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__()
Uma sessão interativa mostra que todo o acesso ao atributo gerenciado age é registrado, mas que o atributo regular name não é registrado:
>>> 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
40
Um grande problema com este exemplo é que o nome privado _age está conectado na classe LoggedAgeAccess. Isso significa que cada instância pode ter apenas um atributo registrado e que seu nome é imutável. No próximo exemplo, vamos corrigir esse problema.
Nomes personalizados¶
Quando uma classe usa descritores, ela pode informar a cada descritor sobre qual nome de variável foi usado.
Neste exemplo, a classe Person
tem duas instâncias de descritor, name e age. Quando a classe Person
é definida, ela faz uma função de retorno para __set_name__()
em LoggedAccess para que os nomes dos campos possam ser registrados, dando a cada descritor o seu próprio public_name e private_name:
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
Uma sessão interativa mostra que a classe Person
chamou __set_name__()
para que os nomes dos campos fossem registrados. Aqui chamamos vars()
para pesquisar o descritor sem acioná-lo:
>>> vars(vars(Person)['name'])
{'public_name': 'name', 'private_name': '_name'}
>>> vars(vars(Person)['age'])
{'public_name': 'age', 'private_name': '_age'}
A nova classe agora registra acesso a name e age:
>>> 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 20
As duas instâncias Person contêm apenas os nomes privados:
>>> vars(pete)
{'_name': 'Peter P', '_age': 10}
>>> vars(kate)
{'_name': 'Catherine C', '_age': 20}
Pensamentos finais¶
Um descritor é o que chamamos de qualquer objeto que define __get__()
, __set__()
ou __delete__()
.
Optionally, descriptors can have a __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 classmethod()
, staticmethod()
,
property()
, and functools.cached_property()
are all implemented as
descriptors.
Exemplo prático completo¶
In this example, we create a practical and powerful tool for locating notoriously hard to find data corruption bugs.
Validator class¶
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 Validator
class is both an abstract base class and a
managed attribute descriptor:
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 Validator
and must supply a
validate()
method to test various restrictions as needed.
Custom validators¶
Here are three practical data validation utilities:
OneOf
verifies that a value is one of a restricted set of options.Number
verifies that a value is either anint
orfloat
. Optionally, it verifies that a value is between a given minimum or maximum.String
verifies that a value is astr
. Optionally, it validates a given minimum or maximum length. It can validate a user-defined predicate as well.
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}'
)
Aplicação prática¶
Here’s how the data validators can be used in a real class:
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 valid
Technical Tutorial¶
What follows is a more technical tutorial for the mechanics and details of how descriptors work.
Resumo¶
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.
Definição e introdução¶
In general, a descriptor is an attribute value that has one of the methods in
the descriptor protocol. Those methods are __get__()
, __set__()
,
and __delete__()
. If any of those methods are defined for an
attribute, 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 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
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.
Protocolo Descriptor¶
descr.__get__(self, obj, type=None)
descr.__set__(self, obj, value)
descr.__delete__(self, obj)
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 __set__()
or __delete__()
, it is considered
a data descriptor. Descriptors that only define __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 __get__()
and
__set__()
with the __set__()
raising an AttributeError
when
called. Defining the __set__()
method with an exception raising
placeholder is enough to make it a data descriptor.
Overview of descriptor invocation¶
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 __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.
Invocation from an instance¶
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 __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 object.__getattribute__()
. Here is
a pure Python equivalent:
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)
Note, there is no __getattr__()
hook in the __getattribute__()
code. That is why calling __getattribute__()
directly or with
super().__getattribute__
will bypass __getattr__()
entirely.
Instead, it is the dot operator and the getattr()
function that are
responsible for invoking __getattr__()
whenever __getattribute__()
raises an AttributeError
. Their logic is encapsulated in a helper
function:
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__
Invocation from a class¶
The logic for a dotted lookup such as A.x
is in
type.__getattribute__()
. The steps are similar to those for
object.__getattribute__()
but the instance dictionary lookup is replaced
by a search through the class’s method resolution order.
If a descriptor is found, it is invoked with desc.__get__(None, A)
.
The full C implementation can be found in type_getattro()
and
_PyType_Lookup()
in Objects/typeobject.c.
Invocation from super¶
The logic for super’s dotted lookup is in the __getattribute__()
method for
object returned by 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 super_getattro()
in
Objects/typeobject.c. A pure Python equivalent can be found in
Guido’s Tutorial.
Summary of invocation logic¶
The mechanism for descriptors is embedded in the __getattribute__()
methods for object
, type
, and super()
.
The important points to remember are:
Descriptors are invoked by the
__getattribute__()
method.Classes inherit this machinery from
object
,type
, orsuper()
.Overriding
__getattribute__()
prevents automatic descriptor calls because all the descriptor logic is in that method.object.__getattribute__()
andtype.__getattribute__()
make different calls to__get__()
. The first includes the instance and may include the class. The second puts inNone
for the instance and always includes the class.Data descriptors always override instance dictionaries.
Non-data descriptors may be overridden by instance dictionaries.
Automatic name notification¶
Sometimes it is desirable for a descriptor to know what class variable name it
was assigned to. When a new class is created, the type
metaclass
scans the dictionary of the new class. If any of the entries are descriptors
and if they define __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.
Os detalhes de implementações estão em type_new()
e set_names()
em Objects/typeobject.c.
Since the update logic is in type.__new__()
, notifications only take
place at the time of class creation. If descriptors are added to the class
afterwards, __set_name__()
will need to be called manually.
Exemplo de ORM¶
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:
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 Field
class to define models that describe the schema for
each table in a database:
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:
>>> 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'
Pure Python Equivalents¶
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.
Propriedades¶
Calling 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
:
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.")
To see how property()
is implemented in terms of the descriptor protocol,
here is a pure Python equivalent:
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!r} of {type(obj).__name__!r} object has no getter'
)
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError(
f'property {self._name!r} of {type(obj).__name__!r} object has no setter'
)
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError(
f'property {self._name!r} of {type(obj).__name__!r} object 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
The 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:
...
@property
def value(self):
"Recalculate the cell before returning value"
self.recalc()
return self._value
Either the built-in property()
or our Property()
equivalent would
work in this example.
Funções e métodos¶
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 types.MethodType
which is
roughly equivalent to:
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
__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:
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:
class D:
def f(self, x):
return x
The function has a qualified name attribute to support introspection:
>>> D.f.__qualname__
'D.f'
Accessing the function through the class dictionary does not invoke
__get__()
. Instead, it just returns the underlying function object:
>>> D.__dict__['f']
<function D.f at 0x00C45070>
Dotted access from a class calls __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 __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 0x00B18C90>
If you have ever wondered where self comes from in regular methods or where cls comes from in class methods, this is it!
Tipos de métodos¶
Non-data descriptors provide a simple mechanism for variations on the usual patterns of binding functions into methods.
To recap, functions have a __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:
Transformação
Called from an object
Called from a class
função
f(obj, *args)
f(*args)
staticmethod
f(*args)
f(*args)
classmethod
f(type(obj), *args)
f(cls, *args)
Métodos estáticos¶
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:
class E:
@staticmethod
def f(x):
return x * 10
>>> E.f(3)
30
>>> E().f(3)
30
Using the non-data descriptor protocol, a pure Python version of
staticmethod()
would look like this:
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 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__
.
Métodos de classe¶
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 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 dict.fromkeys()
creates a new dictionary from a list of
keys. The pure Python equivalent is:
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
classmethod()
would look like this:
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)
The code path for hasattr(type(self.f), '__get__')
was added in
Python 3.9 and makes it possible for classmethod()
to support
chained decorators. For example, a classmethod and property could be
chained together. In Python 3.11, this functionality was deprecated.
class G:
@classmethod
@property
def __doc__(cls):
return f'A doc for {cls.__name__!r}'
>>> G.__doc__
"A doc for 'G'"
The 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__
.
Member objects and __slots__¶
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:
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__
:
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 functools.cached_property()
which require an
instance dictionary to function correctly:
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:
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 type.__new__()
method takes care of adding member objects to class
variables:
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 object.__new__()
method takes care of creating instances that have
slots instead of an instance dictionary. Here is a rough simulation in pure
Python:
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 Object
and
set the metaclass to Type
:
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'>}
When 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'