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Running on Zero

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 classmethod{.interpreted-text role="func"}, staticmethod{.interpreted-text role="func"}, property{.interpreted-text role="func"}, and __slots__{.interpreted-text role="term"}.

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 !Ten{.interpreted-text role="class"} class is a descriptor whose ~object.__get__{.interpreted-text role="meth"} 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:

::: doctest >>> 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.

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 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 ~object.__get__{.interpreted-text role="meth"}. 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 ~object.__get__{.interpreted-text role="meth"} 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 ~object.__get__{.interpreted-text role="meth"} and ~object.__set__{.interpreted-text role="meth"} 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) :::

::: doctest >>> 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 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.

Customized names

When a class uses descriptors, it can inform each descriptor about which variable name was used.

In this example, the !Person{.interpreted-text role="class"} class has two descriptor instances, name and age. When the !Person{.interpreted-text role="class"} class is defined, it makes a callback to ~object.__set_name__{.interpreted-text role="meth"} 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 !Person{.interpreted-text role="class"} class has called ~object.__set_name__{.interpreted-text role="meth"} so that the field names would be recorded. Here we call vars{.interpreted-text role="func"} to look up the descriptor without triggering it:

::: doctest >>> 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) :::

::: doctest >>> 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:

::: doctest >>> vars(pete) {'_name': 'Peter P', '_age': 10} >>> vars(kate) {'_name': 'Catherine C', '_age': 20} :::

Closing thoughts

A descriptor{.interpreted-text role="term"} is what we call any object that defines ~object.__get__{.interpreted-text role="meth"}, ~object.__set__{.interpreted-text role="meth"}, or ~object.__delete__{.interpreted-text role="meth"}.

Optionally, descriptors can have a ~object.__set_name__{.interpreted-text role="meth"} 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{.interpreted-text role="func"}, staticmethod{.interpreted-text role="func"}, property{.interpreted-text role="func"}, and functools.cached_property{.interpreted-text role="func"} 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 !Validator{.interpreted-text role="class"} class is both an abstract base class{.interpreted-text role="term"} 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 !Validator{.interpreted-text role="class"} and must supply a !validate{.interpreted-text role="meth"} method to test various restrictions as needed.

Custom validators

Here are three practical data validation utilities:

!OneOf{.interpreted-text role="class"} verifies that a value is one of a restricted set of options.
!Number{.interpreted-text role="class"} verifies that a value is either an int{.interpreted-text role="class"} or float{.interpreted-text role="class"}. Optionally, it verifies that a value is between a given minimum or maximum.
!String{.interpreted-text role="class"} verifies that a value is a str{.interpreted-text role="class"}. 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 a 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 application

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:

::: doctest >>> 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.

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.

Definition and introduction

In general, a descriptor is an attribute value that has one of the methods in the descriptor protocol. Those methods are ~object.__get__{.interpreted-text role="meth"}, ~object.__set__{.interpreted-text role="meth"}, and ~object.__delete__{.interpreted-text role="meth"}. If any of those methods are defined for an attribute, it is said to be a descriptor{.interpreted-text role="term"}.

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{.interpreted-text role="func"}. 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)

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 ~object.__set__{.interpreted-text role="meth"} or ~object.__delete__{.interpreted-text role="meth"}, it is considered a data descriptor. Descriptors that only define ~object.__get__{.interpreted-text role="meth"} 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 ~object.__get__{.interpreted-text role="meth"} and ~object.__set__{.interpreted-text role="meth"} with the ~object.__set__{.interpreted-text role="meth"} raising an AttributeError{.interpreted-text role="exc"} when called. Defining the ~object.__set__{.interpreted-text role="meth"} 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 ~object.__dict__{.interpreted-text role="attr"}, its ~object.__get__{.interpreted-text role="meth"} 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 ~object.__getattr__{.interpreted-text role="meth"} 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__{.interpreted-text role="meth"}. Here is a pure Python equivalent:

::: testcode

def find_name_in_mro(cls, name, default):

: "Emulate [PyType_Lookup]{#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 :::

::: {.doctest hide=""} >>> 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) True :::

Note, there is no ~object.__getattr__{.interpreted-text role="meth"} hook in the ~object.__getattribute__{.interpreted-text role="meth"} code. That is why calling ~object.__getattribute__{.interpreted-text role="meth"} directly or with super().__getattribute__ will bypass ~object.__getattr__{.interpreted-text role="meth"} entirely.

Instead, it is the dot operator and the getattr{.interpreted-text role="func"} function that are responsible for invoking ~object.__getattr__{.interpreted-text role="meth"} whenever ~object.__getattribute__{.interpreted-text role="meth"} raises an AttributeError{.interpreted-text role="exc"}. 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__ :::

::: {.doctest hide=""} >>> 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' :::

Invocation from a class

The logic for a dotted lookup such as A.x is in !type.__getattribute__{.interpreted-text role="meth"}. The steps are similar to those for !object.__getattribute__{.interpreted-text role="meth"} but the instance dictionary lookup is replaced by a search through the class's method resolution order{.interpreted-text role="term"}.

If a descriptor is found, it is invoked with desc.__get__(None, A).

The full C implementation can be found in !type_getattro{.interpreted-text role="c:func"} and !_PyType_Lookup{.interpreted-text role="c:func"} in Objects/typeobject.c{.interpreted-text role="source"}.

Invocation from super

The logic for super's dotted lookup is in the ~object.__getattribute__{.interpreted-text role="meth"} method for object returned by super{.interpreted-text role="func"}.

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{.interpreted-text role="c:func"} in Objects/typeobject.c{.interpreted-text role="source"}. A pure Python equivalent can be found in Guido's Tutorial.

Summary of invocation logic

The mechanism for descriptors is embedded in the ~object.__getattribute__{.interpreted-text role="meth"} methods for object{.interpreted-text role="class"}, type{.interpreted-text role="class"}, and super{.interpreted-text role="func"}.

The important points to remember are:

Descriptors are invoked by the ~object.__getattribute__{.interpreted-text role="meth"} method.
Classes inherit this machinery from object{.interpreted-text role="class"}, type{.interpreted-text role="class"}, or super{.interpreted-text role="func"}.
Overriding ~object.__getattribute__{.interpreted-text role="meth"} prevents automatic descriptor calls because all the descriptor logic is in that method.
!object.__getattribute__{.interpreted-text role="meth"} and !type.__getattribute__{.interpreted-text role="meth"} make different calls to ~object.__get__{.interpreted-text role="meth"}. 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 type{.interpreted-text role="class"} metaclass scans the dictionary of the new class. If any of the entries are descriptors and if they define ~object.__set_name__{.interpreted-text role="meth"}, 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 !type_new{.interpreted-text role="c:func"} and !set_names{.interpreted-text role="c:func"} in Objects/typeobject.c{.interpreted-text role="source"}.

Since the update logic is in !type.__new__{.interpreted-text role="meth"}, notifications only take place at the time of class creation. If descriptors are added to the class afterwards, ~object.__set_name__{.interpreted-text role="meth"} will need to be called manually.

ORM example

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 !Field{.interpreted-text role="class"} 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() :::

::: doctest >>> 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' :::

::: testcleanup conn.close() :::

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 property{.interpreted-text role="func"} 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.") :::

::: {.doctest hide=""} >>> 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 property{.interpreted-text role="func"} is implemented in terms of the descriptor protocol, here is a pure Python equivalent that implements most of the core functionality:

::: 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

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

    return self.fget(obj)

def \_\_set\_\_(self, obj, value):

:   

    if self.fset is None:

    :   raise AttributeError

    self.fset(obj, value)

def \_\_delete\_\_(self, obj):

:   

    if self.fdel is None:

    :   raise AttributeError

    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\_\_)

:::

::: {.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.\") no_getter = Property(None, setx, delx, \"I\'m the \'x\' property.\") no_setter = Property(getx, None, delx, \"I\'m the \'x\' property.\") no_deleter = Property(getx, setx, None, \"I\'m the \'x\' property.\") no_doc = Property(getx, setx, delx, None)

# 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 :::

::: {.doctest hide=""} >>> 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') False

>>> cc = CC() >>> cc.x = 33 >>> try: ... cc.no_getter ... except AttributeError as e: ... type(e).__name__ ... 'AttributeError'

>>> try: ... cc.no_setter = 33 ... except AttributeError as e: ... type(e).__name__ ... 'AttributeError'

>>> try: ... del cc.no_deleter ... except AttributeError as e: ... type(e).__name__ ... 'AttributeError'

>>> CC.no_doc.__doc__ is None True :::

The property{.interpreted-text role="func"} 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 property{.interpreted-text role="func"} or our !Property{.interpreted-text role="func"} equivalent would work in this example.

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 types.MethodType{.interpreted-text role="class"} 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)

def \_\_getattribute\_\_(self, name):

:   \"Emulate method_getset() in Objects/classobject.c\" if name == \'\_\_doc\_\_\': return self.\_\_func\_\_.\_\_doc\_\_ return object.\_\_getattribute\_\_(self, name)

def \_\_getattr\_\_(self, name):

:   \"Emulate method_getattro() in Objects/classobject.c\" return getattr(self.\_\_func\_\_, name)

def \_\_get\_\_(self, obj, objtype=None):

:   \"Emulate method_descr_get() in Objects/classobject.c\" return self

:::

To support automatic creation of methods, functions include the ~object.__get__{.interpreted-text role="meth"} 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):

:   return self

class D2:

: pass :::

::: {.doctest hide=""} >>> d = D() >>> d2 = D2() >>> d2.f = d.f.__get__(d2, D2) >>> d2.f() is d True :::

The function has a qualified name{.interpreted-text role="term"} attribute to support introspection:

::: doctest >>> D.f.__qualname__ 'D.f' :::

Accessing the function through the class dictionary does not invoke ~object.__get__{.interpreted-text role="meth"}. Instead, it just returns the underlying function object:

>>> D.__dict__['f']
<function D.f at 0x00C45070>

Dotted access from a class calls ~object.__get__{.interpreted-text role="meth"} 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 ~object.__get__{.interpreted-text role="meth"} 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!

Kinds of methods

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

To recap, functions have a ~object.__get__{.interpreted-text role="meth"} 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

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) --> 0.9332 or Sample.erf(1.5) --> 0.9332.

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 :::

::: doctest >>> E.f(3) 30 >>> E().f(3) 30 :::

Using the non-data descriptor protocol, a pure Python version of staticmethod{.interpreted-text role="func"} 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)

\@property def \_\_annotations\_\_(self): return self.f.\_\_annotations\_\_

:::

The functools.update_wrapper{.interpreted-text role="func"} 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, including ~function.__name__{.interpreted-text role="attr"}, ~function.__qualname__{.interpreted-text role="attr"}, and ~function.__doc__{.interpreted-text role="attr"}.

::: {.testcode hide=""}

class E_sim:

: @StaticMethod def f(x: int) -> str: "Simple function example" return "!" * x

wrapped_ord = StaticMethod(ord) :::

::: {.doctest hide=""} >>> 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__ True :::

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:

::: testcode

class F:

: @classmethod def f(cls, x): return cls.__name__, x :::

::: doctest >>> 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{.interpreted-text role="func"} 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:

::: doctest >>> 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{.interpreted-text role="func"} 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)

    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) :::

::: {.doctest hide=""} >>> T.cm(11, 22) ('T', 11, 22)

# Also call it from an instance >>> t = T() >>> t.cm(11, 22) ('T', 11, 22)

# 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 functools.update_wrapper{.interpreted-text role="func"} 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: ~function.__name__{.interpreted-text role="attr"}, ~function.__qualname__{.interpreted-text role="attr"}, ~function.__doc__{.interpreted-text role="attr"}, and ~function.__annotations__{.interpreted-text role="attr"}.

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:

::: testcode

class Vehicle:

: __slots__ = ('id_number', 'make', 'model') :::

::: doctest >>> 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 :::

::: doctest >>> 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{.interpreted-text role="func"} 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)))

:::

::: doctest >>> 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 !type.__new__{.interpreted-text role="meth"} 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 object.__new__{.interpreted-text role="meth"} 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]{#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]{#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{.interpreted-text role="class"} and set the metaclass{.interpreted-text role="term"} to Type{.interpreted-text role="class"}:

::: 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'>}

::: {.doctest hide=""} # 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) True :::

When instances are created, they have a slot_values list where the attributes are stored:

::: doctest >>> 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:

::: doctest >>> h.xz Traceback (most recent call last): ... AttributeError: 'H' object has no attribute 'xz' :::

::: {.doctest hide=""} # 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 :::

Descriptor Guide {#descriptorhowto}

Primer

Simple example: A descriptor that returns a constant

Dynamic lookups

Managed attributes

Customized names

Closing thoughts

Complete Practical Example

Validator class

Custom validators

Practical application

Technical Tutorial

Abstract

Definition and introduction

Descriptor protocol

Overview of descriptor invocation

Invocation from an instance

Invocation from a class

Invocation from super

Summary of invocation logic

Automatic name notification

ORM example

Pure Python Equivalents

Properties

Functions and methods

Kinds of methods

classmethod f(type(obj), *args) f(cls, *args)

Static methods

Class methods

Member objects and __slots__

classmethod f(type(obj), args) f(cls, args)

Member objects and slots