- dunder methods#
“Dunder” is a contraction of “double underscore”.
It’s methods like
__eq__that are sometimes also called magic methods or it’s said that they implement an object protocol.
In spoken form, you’d call
__init__just “dunder init”.
Its first documented use is a mailing list posting by Mark Jackson from 2002.
- dict classes#
A regular class whose attributes are stored in the
object.__dict__attribute of every single instance. This is quite wasteful especially for objects with very few data attributes and the space consumption can become significant when creating large numbers of instances.
This is the type of class you get by default both with and without attrs (except with the next APIs
- slotted classes#
A class whose instances have no
object.__dict__attribute and define their attributes in a
object.__slots__attribute instead. In attrs, they are created by passing
@attr.s(and are on by default in
Their main advantage is that they use less memory on CPython and are slightly faster.
However, they also come with several possibly surprising gotchas:
Slotted classes don’t allow for any other attribute to be set except for those defined in one of the class’ hierarchies
>>> from attr import define >>> @define ... class Coordinates: ... x: int ... y: int ... >>> c = Coordinates(x=1, y=2) >>> c.z = 3 Traceback (most recent call last): ... AttributeError: 'Coordinates' object has no attribute 'z'
Slotted classes can inherit from other classes just like non-slotted classes, but some of the benefits of slotted classes are lost if you do that. If you must inherit from other classes, try to inherit only from other slotted classes.
However, it’s not possible to inherit from more than one class that has attributes in
__slots__(you will get an
TypeError: multiple bases have instance lay-out conflict).
It’s not possible to monkeypatch methods on slotted classes. This can feel limiting in test code, however the need to monkeypatch your own classes is usually a design smell.
If you really need to monkeypatch an instance in your tests, but don’t want to give up on the advantages of slotted classes in production code, you can always subclass a slotted class as a dict class with no further changes and all the limitations go away:
>>> import unittest.mock >>> @define ... class Slotted: ... x: int ... ... def method(self): ... return self.x >>> s = Slotted(42) >>> s.method() 42 >>> with unittest.mock.patch.object(s, "method", return_value=23): ... pass Traceback (most recent call last): ... AttributeError: 'Slotted' object attribute 'method' is read-only >>> @define(slots=False) ... class Dicted(Slotted): ... pass >>> d = Dicted(42) >>> d.method() 42 >>> with unittest.mock.patch.object(d, "method", return_value=23): ... assert 23 == d.method()
Slotted classes must implement
__setstate__to be serializable with
pickleprotocol 0 and 1. Therefore, attrs creates these methods automatically for slotted classes.
When decorating with
@attr.s(slots=True)and the class already implements the
__setstate__methods, they will be overwritten by attrs autogenerated implementation by default.
This can be avoided by setting
@attr.s(getstate_setstate=False)or by setting
Also, think twice before using
Slotted classes are weak-referenceable by default. This can be disabled in CPython by passing
Since it’s currently impossible to make a class slotted after it’s been created, attrs has to replace your class with a new one. While it tries to do that as graciously as possible, certain metaclass features like
object.__init_subclass__()do not work with slotted classes.
class.__subclasses__attribute needs a garbage collection run (which can be manually triggered using
gc.collect()), for the original class to be removed. See issue #407 for more details.