python_doc_md/Beginner/howto/descriptor.md

# Descriptor Guide {#descriptorhowto}

Author
:   Raymond Hettinger

Contact
:   \<python at rcn dot com\>

::: contents
:::

`Descriptors <descriptor>`{.interpreted-text role="term"} 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. Start here if you\'re new to descriptors.
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 `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:

1)  `!OneOf`{.interpreted-text role="class"} verifies that a value is one of a restricted set of options.
2)  `!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.
3)  `!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](https://en.wikipedia.org/wiki/Predicate_(mathematical_logic)) 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](https://www.python.org/download/releases/2.2.3/descrintro/#cooperation).

### 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](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:

::: 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](https://en.wikipedia.org/wiki/Database_model) 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](https://en.wikipedia.org/wiki/Flyweight_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
:::