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.. _descriptorhowto:
======================
Descriptor HowTo Guide
======================
:Author: Raymond Hettinger
:Contact: <python at rcn dot com>
.. Contents::
:term:`Descriptors <descriptor>` let objects customize attribute lookup,
storage, and deletion.
This guide has four major sections:
1) The "primer" gives a basic overview, moving gently from simple examples,
adding one feature at a time. It is a great place to start.
2) The second section shows a complete, practical descriptor example. If you
already know the basics, start there.
3) 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.
4) 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__`.
Primer
^^^^^^
In this primer, we start with the most basic possible example and then we'll
add new capabilities one by one.
Simple example: A descriptor that returns a constant
----------------------------------------------------
The :class:`Ten` class is a descriptor that always returns the constant ``10``::
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::
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
10
In the ``a.x`` attribute lookup, the dot operator finds the value ``5`` stored
in the class dictionary. In the ``a.y`` descriptor lookup, the dot operator
calls the descriptor's :meth:`__get__()` method. 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.
Dynamic lookups
---------------
Interesting descriptors typically run computations instead of doing lookups::
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::
>>> g = Directory('games')
>>> s = Directory('songs')
>>> g.size # The games directory has three files
3
>>> os.system('touch games/newfile') # Add a fourth file to the directory
0
>>> g.size # Automatically updated
4
>>> s.size # The songs directory has twenty files
20
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 *obj* parameter that
lets the :meth:`__get__` method learn the target directory. The *objtype*
parameter is the class *Directory*.
Managed attributes
------------------
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::
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::
>>> 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
One major issue with this example is 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.
Customized names
----------------
When a class uses descriptors, it can inform each descriptor about what
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*::
import logging
logging.basicConfig(level=logging.INFO)
class LoggedAccess:
def __set_name__(self, owner, name):
self.public_name = name
self.private_name = f'_{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 lookup 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*::
>>> 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
The two *Person* instances contain only the private names::
>>> vars(pete)
{'_name': 'Peter P', '_age': 10}
>>> vars(kate)
{'_name': 'Catherine C', '_age': 20}
Closing thoughts
----------------
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.
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 dotted 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.
Complete Practical Example
^^^^^^^^^^^^^^^^^^^^^^^^^^
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 :class:`Validator` class is both an :term:`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 = f'_{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.
Custom validators
-----------------
Here are three practical data validation utilities:
1) :class:`OneOf` verifies that a value is one of a restricted set of options.
2) :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.
3) :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
<https://en.wikipedia.org/wiki/Predicate_(mathematical_logic)>`_ 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}'
)
Practical use
-------------
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) # Allowed.
Component('Widget', 'metal', 5) # Blocked: 'Widget' is not all uppercase
Component('WIDGET', 'metle', 5) # Blocked: 'metle' is misspelled
Component('WIDGET', 'metal', -5) # Blocked: -5 is negative
Component('WIDGET', 'metal', 'V') # Blocked: 'V' isn't a number
Technical Tutorial
^^^^^^^^^^^^^^^^^^
What follows is a more technical tutorial for the mechanics and details of how
descriptors work.
Abstract
--------
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 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 :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 base classes 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.
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 :meth:`__set__` or :meth:`__delete__`, 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.
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, 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.
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 :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::
def object_getattribute(obj, name):
"Emulate PyObject_GenericGetAttr() in Objects/object.c"
null = object()
objtype = type(obj)
value = getattr(objtype, name, null)
if value is not null and hasattr(value, '__get__'):
if hasattr(value, '__set__') or hasattr(value, '__delete__'):
return value.__get__(obj, objtype) # data descriptor
try:
return vars(obj)[name] # instance variable
except (KeyError, TypeError):
pass
if hasattr(value, '__get__'):
return value.__get__(obj, objtype) # non-data descriptor
if value is not null:
return value # class variable
# Emulate slot_tp_getattr_hook() in Objects/typeobject.c
if hasattr(objtype, '__getattr__'):
return objtype.__getattr__(obj, name) # __getattr__ hook
raise AttributeError(name)
The :exc:`TypeError` exception handler is needed because the instance dictionary
doesn't exist when its class defines :term:`__slots__`.
Invocation from a class
-----------------------
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`.
Invocation from super
---------------------
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
<https://www.python.org/download/releases/2.2.3/descrintro/#cooperation>`_.
Summary of invocation logic
---------------------------
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 ``None`` 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 :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, the *name*
is 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.
ORM example
-----------
The following code is simplified skeleton showing how data descriptors could
be used to implement an `object relational mapping
<https://en.wikipedia.org/wiki/Object%E2%80%93relational_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 :class:`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
An interactive session shows how data is retrieved from the database and how
it can be updated::
>>> import sqlite3
>>> conn = sqlite3.connect('entertainment.db')
>>> 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.
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
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 :func:`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
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:
...
@property
def value(self):
"Recalculate the cell before returning value"
self.recalc()
return self._value
Functions and methods
---------------------
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::
class MethodType:
"Emulate Py_MethodType 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 which 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 :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 0x1012e5ae8>
>>> 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!
Static 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 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 | Called from a |
| | object | 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::
class E:
@staticmethod
def f(x):
print(x)
>>> E.f(3)
3
>>> E().f(3)
3
Using the non-data descriptor protocol, a pure Python version of
:func:`staticmethod` would look like this::
class StaticMethod:
"Emulate PyStaticMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
def __get__(self, obj, objtype=None):
return self.f
Class methods
-------------
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
>>> print(F.f(3))
('F', 3)
>>> print(F().f(3))
('F', 3)
This behavior is useful whenever the method only needs to have a class
reference and does 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::
class 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::
>>> 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:
"Emulate PyClassMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
def __get__(self, obj, cls=None):
if cls is None:
cls = type(obj)
if hasattr(obj, '__get__'):
return self.f.__get__(cls)
return MethodType(self.f, cls)
The code path for ``hasattr(obj, '__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::
class G:
@classmethod
@property
def __doc__(cls):
return f'A doc for {cls.__name__!r}'
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 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') # Create an immutable instance
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 <https://en.wikipedia.org/wiki/Flyweight_pattern>`_ likely only
matters when a large number of instances are going to be created.
4. Blocks tools like :func:`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's 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::
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
return obj._slotvalues[self.offset]
def __set__(self, obj, value):
'Emulate member_set() in Objects/descrobject.c'
obj._slotvalues[self.offset] = value
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. The :meth:`object.__new__` method takes care of creating instances
that have slots instead of a instance dictionary. Here is a rough equivalent
in pure Python::
class Type(type):
'Simulate how the type metaclass adds member objects for slots'
def __new__(mcls, clsname, bases, mapping):
'Emuluate 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)
class Object:
'Simulate how object.__new__() allocates memory for __slots__'
def __new__(cls, *args):
'Emulate object_new() in Objects/typeobject.c'
inst = super().__new__(cls)
if hasattr(cls, 'slot_names'):
inst._slotvalues = [None] * len(cls.slot_names)
return inst
To use the simulation in a real class, just inherit from :class:`Object` and
set the :term:`metaclass` to :class:`Type`::
class H(Object, metaclass=Type):
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*::
>>> import pprint
>>> pprint.pp(dict(vars(H)))
{'__module__': '__main__',
'slot_names': ['x', 'y'],
'__init__': <function H.__init__ at 0x7fb5d302f9d0>,
'x': <Member 'x' of 'H'>,
'y': <Member 'y' of 'H'>,
'__doc__': None}
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]}
Unlike the real ``__slots__``, this simulation does have an instance
dictionary just to hold the ``_slotvalues`` array. So, unlike the real code,
this simulation doesn't block assignments to misspelled attributes::
>>> h.xz = 30 # For actual __slots__ this would raise an AttributeError
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