Usage of __slots__?

Question

What is the purpose of __slots__ in Python — especially with respect to when I would want to use it, and when not?

Philadelphia

Answer 1

In Python, what is the purpose of __slots__ and what are the cases one should avoid this?

TLDR:

The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

1. faster attribute access.
2. space savings in memory.

The space savings is from

1. Storing value references in slots instead of __dict__.
2. Denying __dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.

Quick Caveats

Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:

class Base:
    __slots__ = 'foo', 'bar'

class Right(Base):
    __slots__ = 'baz', 

class Wrong(Base):
    __slots__ = 'foo', 'bar', 'baz'        # redundant foo and bar

Python doesn't object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:

>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)

This is because the Base's slot descriptor has a slot separate from the Wrong's. This shouldn't usually come up, but it could:

>>> w = Wrong()
>>> w.foo = 'foo'
>>> Base.foo.__get__(w)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
'foo'

The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.

To accommodate this restriction, follow best practices: Factor out all but one or all parents' abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).

See section on multiple inheritance below for an example.

Requirements:

• To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).

• To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a '__dict__' entry.

There are a lot of details if you wish to keep reading.

Why use __slots__: Faster attribute access.

The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.

It is trivial to demonstrate measurably significant faster access:

import timeit

class Foo(object): __slots__ = 'foo',

class Bar(object): pass

slotted = Foo()
not_slotted = Bar()

def get_set_delete_fn(obj):
    def get_set_delete():
        obj.foo = 'foo'
        obj.foo
        del obj.foo
    return get_set_delete

and

>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085

The slotted access is almost 30% faster in Python 3.5 on Ubuntu.

>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342

In Python 2 on Windows I have measured it about 15% faster.

Why use __slots__: Memory Savings

Another purpose of __slots__ is to reduce the space in memory that each object instance takes up.

My own contribution to the documentation clearly states the reasons behind this:

The space saved over using __dict__ can be significant.

SQLAlchemy attributes a lot of memory savings to __slots__.

To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.

In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.

For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):

       Python 2.7             Python 3.6
attrs  __slots__  __dict__*   __slots__  __dict__* | *(no slots defined)
none   16         56 + 272†   16         56 + 112† | †if __dict__ referenced
one    48         56 + 272    48         56 + 112
two    56         56 + 272    56         56 + 112
six    88         56 + 1040   88         56 + 152
11     128        56 + 1040   128        56 + 240
22     216        56 + 3344   216        56 + 408     
43     384        56 + 3344   384        56 + 752

So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.

Just for completeness of my notes, note that there is a one-time cost per slot in the class's namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".

>>> Foo.foo
<member 'foo' of 'Foo' objects>
>>> type(Foo.foo)
<class 'member_descriptor'>
>>> getsizeof(Foo.foo)
72

Demonstration of __slots__:

To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:

class Base(object): 
    __slots__ = ()

now:

>>> b = Base()
>>> b.a = 'a'
Traceback (most recent call last):
  File "<pyshell#38>", line 1, in <module>
    b.a = 'a'
AttributeError: 'Base' object has no attribute 'a'

Or subclass another class that defines __slots__

class Child(Base):
    __slots__ = ('a',)

and now:

c = Child()
c.a = 'a'

but:

>>> c.b = 'b'
Traceback (most recent call last):
  File "<pyshell#42>", line 1, in <module>
    c.b = 'b'
AttributeError: 'Child' object has no attribute 'b'

To allow __dict__ creation while subclassing slotted objects, just add '__dict__' to the __slots__ (note that slots are ordered, and you shouldn't repeat slots that are already in parent classes):

class SlottedWithDict(Child): 
    __slots__ = ('__dict__', 'b')

swd = SlottedWithDict()
swd.a = 'a'
swd.b = 'b'
swd.c = 'c'

and

>>> swd.__dict__
{'c': 'c'}

Or you don't even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:

class NoSlots(Child): pass
ns = NoSlots()
ns.a = 'a'
ns.b = 'b'

And:

>>> ns.__dict__
{'b': 'b'}

However, __slots__ may cause problems for multiple inheritance:

class BaseA(object): 
    __slots__ = ('a',)

class BaseB(object): 
    __slots__ = ('b',)

Because creating a child class from parents with both non-empty slots fails:

>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
  File "<pyshell#68>", line 1, in <module>
    class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:

from abc import ABC

class AbstractA(ABC):
    __slots__ = ()

class BaseA(AbstractA): 
    __slots__ = ('a',)

class AbstractB(ABC):
    __slots__ = ()

class BaseB(AbstractB): 
    __slots__ = ('b',)

class Child(AbstractA, AbstractB): 
    __slots__ = ('a', 'b')

c = Child() # no problem!

Add '__dict__' to __slots__ to get dynamic assignment:

class Foo(object):
    __slots__ = 'bar', 'baz', '__dict__'

and now:

>>> foo = Foo()
>>> foo.boink = 'boink'

So with '__dict__' in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.

When you inherit from an object that isn't slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance's __dict__.

Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.

You can similarly add __weakref__ to __slots__ explicitly if you need that feature.

Set to empty tuple when subclassing a namedtuple:

The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:

from collections import namedtuple
class MyNT(namedtuple('MyNT', 'bar baz')):
    """MyNT is an immutable and lightweight object"""
    __slots__ = ()

usage:

>>> nt = MyNT('bar', 'baz')
>>> nt.bar
'bar'
>>> nt.baz
'baz'

And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:

>>> nt.quux = 'quux'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'MyNT' object has no attribute 'quux'

You can allow __dict__ creation by leaving off __slots__ = (), but you can't use non-empty __slots__ with subtypes of tuple.

Biggest Caveat: Multiple inheritance

Even when non-empty slots are the same for multiple parents, they cannot be used together:

class Foo(object): 
    __slots__ = 'foo', 'bar'
class Bar(object):
    __slots__ = 'foo', 'bar' # alas, would work if empty, i.e. ()

>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
    multiple bases have instance lay-out conflict

Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding '__dict__' to get dynamic assignment, see section above) the creation of a __dict__:

class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ('foo', 'bar')
b = Baz()
b.foo, b.bar = 'foo', 'bar'

You don't have to have slots - so if you add them, and remove them later, it shouldn't cause any problems.

Going out on a limb here: If you're composing mixins or using abstract base classes, which aren't intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.

To demonstrate, first, let's create a class with code we'd like to use under multiple inheritance

class AbstractBase:
    __slots__ = ()
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __repr__(self):
        return f'{type(self).__name__}({repr(self.a)}, {repr(self.b)})'

We could use the above directly by inheriting and declaring the expected slots:

class Foo(AbstractBase):
    __slots__ = 'a', 'b'

But we don't care about that, that's trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:

class AbstractBaseC:
    __slots__ = ()
    @property
    def c(self):
        print('getting c!')
        return self._c
    @c.setter
    def c(self, arg):
        print('setting c!')
        self._c = arg

Now if both bases had nonempty slots, we couldn't do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):

class Concretion(AbstractBase, AbstractBaseC):
    __slots__ = 'a b _c'.split()

And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:

>>> c = Concretion('a', 'b')
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion('a', 'b')
>>> c.d = 'd'
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Concretion' object has no attribute 'd'

Other cases to avoid slots:

• Avoid them when you want to perform __class__ assignment with another class that doesn't have them (and you can't add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)
• Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
• Avoid them if you insist on providing default values via class attributes for instance variables.

You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.

Critiques of other answers

The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.

Do not "only use __slots__ when instantiating lots of objects"

I quote:

"You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."

Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.

Why?

If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.

__slots__ contributes to reusability when creating interfaces or mixins.

It is true that many Python users aren't writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.

__slots__ doesn't break pickling

When pickling a slotted object, you may find it complains with a misleading TypeError:

>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled

This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.

>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>

in Python 2.7:

>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>

in Python 3.6

>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>

So I would keep this in mind, as it is a solved problem.

Critique of the (until Oct 2, 2016) accepted answer

The first paragraph is half short explanation, half predictive. Here's the only part that actually answers the question

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots

The second half is wishful thinking, and off the mark:

While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.

The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):

They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.

It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.

The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn't even author and contributes to ammunition for critics of the site.

Memory usage evidence

Create some normal objects and slotted objects:

>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()

Instantiate a million of them:

>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]

Inspect with guppy.hpy().heap():

>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0 1000000  49 64000000  64  64000000  64 __main__.Foo
     1     169   0 16281480  16  80281480  80 list
     2 1000000  49 16000000  16  96281480  97 __main__.Bar
     3   12284   1   987472   1  97268952  97 str
...

Access the regular objects and their __dict__ and inspect again:

>>> for f in foos:
...     f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
 Index  Count   %      Size    % Cumulative  % Kind (class / dict of class)
     0 1000000  33 280000000  74 280000000  74 dict of __main__.Foo
     1 1000000  33  64000000  17 344000000  91 __main__.Foo
     2     169   0  16281480   4 360281480  95 list
     3 1000000  33  16000000   4 376281480  99 __main__.Bar
     4   12284   0    987472   0 377268952  99 str
...

This is consistent with the history of Python, from Unifying types and classes in Python 2.2

If you subclass a built-in type, extra space is automatically added to the instances to accomodate __dict__ and __weakrefs__. (The __dict__ is not initialized until you use it though, so you shouldn't worry about the space occupied by an empty dictionary for each instance you create.) If you don't need this extra space, you can add the phrase "__slots__ = []" to your class.

Anticipatory

This answer should be part of the official Python documentation about __slots__. Seriously! Thank you! – Ecthyma

@Ecthyma believe it or not, I contributed to the Python docs on __slots__ about a year back: github.com/python/cpython/pull/1819/files – Anticipatory

Fantastically detailed answer. I have one question: should one being using slots as default unless the usage hits one of the caveats, or are slots something to consider if you know you are going to struggle for speed/memory? To put it another way, should you encourage a newbie to learn about them and use them from the start? – Squelch

Great answer, thanks! One sidenote: in the current version of python (3.6 or 3.7) it looks as if subclassing a namedtuple is no longer necessary. It doesn't allow undeclared attribute assignment by default. – Jerri

Could you please rephrase "Factor out all but one or all parents' abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library). It's difficult to parse. Do you mean "Across all parent abstract classes inherited by your concrete class, factor out all or all but one of the __slots__ declarations."? The relation between "their concrete class" and "your new concrete class" is not stated -- simple inherit., or diamond again? – Zoosporangium

Which answer is "the (until Oct 2, 2016) accepted answer"? The history of acceptance isn't easily accessible to normal users; perhaps that heading could be re-worded (and preferably incorporate a link)? Thanks. – Biramous

I'm trying to critique the material while leaving the individuals involved out of it. If you look for the words I quote, it's not hard to find... – Anticipatory

Note that Python 3 classes inherit from object implicitly. See stackoverflow.com/questions/15374857/… – Rozellarozelle

@AaronHall, quick question: If I define some dunder methods like __str__ and __hash__, do I need to list them in __slots__ as well? – Schlesien

@Schlesien no you do not need to list method names in __slots__ - but thanks for the question! A slot declaration creates a descriptor object in the namespace (the __dict__) like a method definition does. – Anticipatory

My own limited testing suggests that the saving on memory usage of instances is not as large as stated in the answer. Is the suggestion in this page, that improvement in python class dictionaries have eroded the advantage of slots in this respect, true? – Hundley

I just get to know __slots__, so @AaronHall thank you for this detailed answer. Is __slots__ only to handle an internal dictionary in the class? What is the difference in terms of performance between using __slots__ and creating dynamic attributes using __setattr__ or setattr()? – Hephzibah

@PedroPabloSeverinHonorato __slots__ doesn't handle an internal dictionary. As some like to put it, it lets you avoid using a dictionary. Regarding performance versus __slots__ and __setattr__ or setattr, I do the analysis for dotted lookups, and conclude "The slotted access is almost 30% faster in Python 3.5 on Ubuntu." - can you clarify your question(s)? – Anticipatory

This answer deserves a standing ovation. – Thebaine

In Python 3 you don't need to explicitly inherit from object in order to use slots. Pls. fix. – Roundsman

@Roundsman thanks for bringing that to my attention, I have updated the text in two locations to make that point explicit. Let me know if that's good or if you think I've missed any other spots or any other issues as well. Much appreciated. – Anticipatory

@AaronHall I profiled the effects of slots for attribute access optimisations, and in one of my tests I included the case of a class using __slots__ with a __dict__ to see if we can get the best of both worlds (faster attribute lookup for some attributes while keeping dynamic assignments for the rest of the object attributes). Well, turns out adding __dict__ makes it as slow as not using __slots__ at all... I did not test for memory usage by I guess if you include a __dict__ that also get thrown out the window. So, I don't see the point of __dict__ in __slots__. Using python 3.8 – Agha

test for memory usage also, then write it up, and if you still think you have something interesting, post it to the python dev mailing list. – Anticipatory

Are attributes set during instance construction (i.e. in __init__) automatically slotted since every instance would have said attributes and it seems like a reasonable optimization? Or do they have to be slotted manually (using __slots__ = ...? – Foch

see the example class Foo(AbstractBase) above - all slots must be declared except for __dict__ and __weakref__ (which get created implicitly if you don't declare slots.) – Anticipatory

Using Python 3.10.2 (through IPython 8.0.1) I actually get no performance increase from using __slots__: %timeit get_set_delete_fn(not_slotted) -> 168 ns ± 0.367 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each), %timeit get_set_delete_fn(slotted) -> 171 ns ± 0.106 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each). The memory advantage of course remains. – Mohan

I can confirm that I don't see a significant performance beneift in Pyhton 3.8 either. – Tonometer

Answer 2

Quoting Jacob Hallen:

The proper use of __slots__ is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. [This use of __slots__ eliminates the overhead of one dict for every object.] While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.

Unfortunately there is a side effect to slots. They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies. This is bad, because the control freaks should be abusing the metaclasses and the static typing weenies should be abusing decorators, since in Python, there should be only one obvious way of doing something.

Making CPython smart enough to handle saving space without __slots__ is a major undertaking, which is probably why it is not on the list of changes for P3k (yet).

Calling

I'd like to see some elaboration on the "static typing"/decorator point, sans pejoratives. Quoting absent third parties is unhelpful. __slots__ doesn't address the same issues as static typing. For example, in C++, it is not the declaration of a member variable is being restricted, it is the assignment of an unintended type (and compiler enforced) to that variable. I'm not condoning the use of __slots__, just interested in the conversation. Thanks! – Bridgetbridgetown

"in Python, there should be only one obvious way of doing something" So what is the one obvious way of preventing global variables (uppercase variables being named consts) using metaclasses? – Hidden

Answer 3

You would want to use __slots__ if you are going to instantiate a lot (hundreds, thousands) of objects of the same class. __slots__ only exists as a memory optimization tool.

It's highly discouraged to use __slots__ for constraining attribute creation.

Pickling objects with __slots__ won't work with the default (oldest) pickle protocol; it's necessary to specify a later version.

Some other introspection features of python may also be adversely affected.

Byram

I demonstrate pickling a slotted object in my answer and also address the first part of your answer. – Anticipatory

I see your point, but slots offer faster attribute access as well (as others have stated). In that case you don't need "to instantiate a lot (hundreds, thousands) of objects of the same class" in order to gain performance. What you need instead are a lot of accesses to the same (slotted) attribute of the same instance. (Please correct me if I'm wrong.) – Elke

why is it "highly discouraged"? I was recently looking for a way to constrain dynamic attribute creation. I found something but there was no mention of slots. Now I read about slots and it seems like exactly what i was looking for before. Whats wrong about using slots to prevent adding attributes at runtime? – Kinesiology

@idclev463035818 I don't think there is any wrong thing about that. – Psychodiagnosis

Answer 4

Each python object has a __dict__ atttribute which is a dictionary containing all other attributes. e.g. when you type self.attr python is actually doing self.__dict__['attr']. As you can imagine using a dictionary to store attribute takes some extra space & time for accessing it.

However, when you use __slots__, any object created for that class won't have a __dict__ attribute. Instead, all attribute access is done directly via pointers.

So if want a C style structure rather than a full fledged class you can use __slots__ for compacting size of the objects & reducing attribute access time. A good example is a Point class containing attributes x & y. If you are going to have a lot of points, you can try using __slots__ in order to conserve some memory.

Ashore

No, an instance of a class with __slots__ defined is not like a C-style structure. There is a class-level dictionary mapping attribute names to indexes, otherwise the following would not be possible: class A(object): __slots__= "value",\n\na=A(); setattr(a, 'value', 1) I really think this answer should be clarified (I can do that if you want). Also, I'm not certain that instance.__hidden_attributes[instance.__class__[attrname]] is faster than instance.__dict__[attrname]. – Underhill

Answer 5

In addition to the other answers, here is an example of using __slots__:

>>> class Test(object):   #Must be new-style class!
...  __slots__ = ['x', 'y']
... 
>>> pt = Test()
>>> dir(pt)
['__class__', '__delattr__', '__doc__', '__getattribute__', '__hash__', 
 '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', 
 '__repr__', '__setattr__', '__slots__', '__str__', 'x', 'y']
>>> pt.x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: x
>>> pt.x = 1
>>> pt.x
1
>>> pt.z = 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute 'z'
>>> pt.__dict__
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Test' object has no attribute '__dict__'
>>> pt.__slots__
['x', 'y']

So, to implement __slots__, it only takes an extra line (and making your class a new-style class if it isn't already). This way you can reduce the memory footprint of those classes 5-fold, at the expense of having to write custom pickle code, if and when that becomes necessary.

Scag

Answer 6

Slots are very useful for library calls to eliminate the "named method dispatch" when making function calls. This is mentioned in the SWIG documentation. For high performance libraries that want to reduce function overhead for commonly called functions using slots is much faster.

Now this may not be directly related to the OPs question. It is related more to building extensions than it does to using the __slots__ syntax on an object. But it does help complete the picture for the usage of slots and some of the reasoning behind them.

Salacious

Answer 7

A very simple example of __slot__ attribute.

Problem: Without __slots__

If I don't have __slot__ attribute in my class, I can add new attributes to my objects.

class Test:
    pass

obj1=Test()
obj2=Test()

print(obj1.__dict__)  #--> {}
obj1.x=12
print(obj1.__dict__)  # --> {'x': 12}
obj1.y=20
print(obj1.__dict__)  # --> {'x': 12, 'y': 20}

obj2.x=99
print(obj2.__dict__)  # --> {'x': 99}

If you look at example above, you can see that obj1 and obj2 have their own x and y attributes and python has also created a dict attribute for each object (obj1 and obj2).

Suppose if my class Test has thousands of such objects? Creating an additional attribute dict for each object will cause lot of overhead (memory, computing power etc.) in my code.

Solution: With __slots__

Now in the following example my class Test contains __slots__ attribute. Now I can't add new attributes to my objects (except attribute x) and python doesn't create a dict attribute anymore. This eliminates overhead for each object, which can become significant if you have many objects.

class Test:
    __slots__=("x")

obj1=Test()
obj2=Test()
obj1.x=12
print(obj1.x)  # --> 12
obj2.x=99
print(obj2.x)  # --> 99

obj1.y=28
print(obj1.y)  # --> AttributeError: 'Test' object has no attribute 'y'

Hawkbill

Hi, you can use class Test: __slots__= ("x", "__dict__") and it will work. – Wilson

Answer 8

An attribute of a class instance has 3 properties: the instance, the name of the attribute, and the value of the attribute.

In regular attribute access, the instance acts as a dictionary and the name of the attribute acts as the key in that dictionary looking up value.

instance(attribute) --> value

In __slots__ access, the name of the attribute acts as the dictionary and the instance acts as the key in the dictionary looking up value.

attribute(instance) --> value

In flyweight pattern, the name of the attribute acts as the dictionary and the value acts as the key in that dictionary looking up the instance.

attribute(value) --> instance

Arbogast

This is a good share, and won't fit well in a comment on one of the answers that also suggest flyweights, but it is not a complete answer to the question itself. In particular (in just context of the question): why Flyweight, and "what are the cases one should avoid ..." __slots__? – Dumortierite

@Merlyn Morgan-Graham, it serves as a hint on which to pick: regular access, __slots__, or flyweight. – Arbogast

Answer 9

In addition to the other answers, __slots__ also adds a little typographical security by limiting attributes to a predefined list. This has long been a problem with JavaScript which also allows you to add new attributes to an existing object, whether you meant to or not.

Here is a normal unslotted object which does nothing, but allows you to add attributes:

class Unslotted:
    pass
test = Unslotted()
test.name = 'Fred'
test.Name = 'Wilma'

Since Python is case sensitive, the two attributes, spelled the same but with different case, are different. If you suspect that one of those is a typing error, then bad luck.

Using slots, you can limit this:

class Slotted:
    __slots__ = ('name')
    pass
test = Slotted()
test.name = 'Fred'      #   OK
test.Name = 'Wilma'     #   Error

This time, the second attribute (Name) is disallowed because it’s not in the __slots__ collection.

I would suggest that it’s probably better to use __slots__ where possible to keep more control over the object.

Photogenic

Answer 10

Beginning in Python 3.9, a dict may be used to add descriptions to attributes via __slots__. None may be used for attributes without descriptions, and private variables will not appear even if a description is given.

class Person:

    __slots__ = {
        "birthday":
            "A datetime.date object representing the person's birthday.",
        "name":
            "The first and last name.",
        "public_variable":
            None,
        "_private_variable":
            "Description",
    }


help(Person)
"""
Help on class Person in module __main__:

class Person(builtins.object)
 |  Data descriptors defined here:
 |
 |  birthday
 |      A datetime.date object representing the person's birthday.
 |
 |  name
 |      The first and last name.
 |
 |  public_variable
"""

Popsicle

Was just looking if someone mentioned it If a dictionary is used to assign slots, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by inspect.getdoc() and displayed in the output of help(). – Tender

Answer 11

Another somewhat obscure use of __slots__ is to add attributes to an object proxy from the ProxyTypes package, formerly part of the PEAK project. Its ObjectWrapper allows you to proxy another object, but intercept all interactions with the proxied object. It is not very commonly used (and no Python 3 support), but we have used it to implement a thread-safe blocking wrapper around an async implementation based on tornado that bounces all access to the proxied object through the ioloop, using thread-safe concurrent.Future objects to synchronise and return results.

By default any attribute access to the proxy object will give you the result from the proxied object. If you need to add an attribute on the proxy object, __slots__ can be used.

from peak.util.proxies import ObjectWrapper

class Original(object):
    def __init__(self):
        self.name = 'The Original'

class ProxyOriginal(ObjectWrapper):

    __slots__ = ['proxy_name']

    def __init__(self, subject, proxy_name):
        # proxy_info attributed added directly to the
        # Original instance, not the ProxyOriginal instance
        self.proxy_info = 'You are proxied by {}'.format(proxy_name)

        # proxy_name added to ProxyOriginal instance, since it is
        # defined in __slots__
        self.proxy_name = proxy_name

        super(ProxyOriginal, self).__init__(subject)

if __name__ == "__main__":
    original = Original()
    proxy = ProxyOriginal(original, 'Proxy Overlord')

    # Both statements print "The Original"
    print "original.name: ", original.name
    print "proxy.name: ", proxy.name

    # Both statements below print 
    # "You are proxied by Proxy Overlord", since the ProxyOriginal
    # __init__ sets it to the original object 
    print "original.proxy_info: ", original.proxy_info
    print "proxy.proxy_info: ", proxy.proxy_info

    # prints "Proxy Overlord"
    print "proxy.proxy_name: ", proxy.proxy_name
    # Raises AttributeError since proxy_name is only set on 
    # the proxy object
    print "original.proxy_name: ", proxy.proxy_name

Womack

Answer 12

In addition to the myriad advantages described in other answers herein – compact instances for the memory-conscious, less error-prone than the more mutable __dict__-bearing instances, et cetera – I find that using __slots__ offers more legible class declarations, as the instance variables of the class are explicitly out in the open.

To contend with inheritance issues with __slots__ declarations I use this metaclass:

import abc

class Slotted(abc.ABCMeta):
    
    """ A metaclass that ensures its classes, and all subclasses,
        will be slotted types.
    """
    
    def __new__(metacls, name, bases, attributes, **kwargs):
        """ Override for `abc.ABCMeta.__new__(…)` setting up a
            derived slotted class.
        """
        if '__slots__' not in attributes:
            attributes['__slots__'] = tuple()
        
        return super(Slotted, metacls).__new__(metacls, name, # type: ignore
                                                        bases,
                                                        attributes,
                                                      **kwargs)

… which, if declared as the metaclass of the base class in an inheritance tower, ensures that everything that derives from that base class will properly inherit __slots__ attributes, even if an intermediate class fails to declare any. Like so:

# note no __slots__ declaration necessary with the metaclass:
class Base(metaclass=Slotted):
    pass

# class is properly slotted, no __dict__:
class Derived(Base):
    __slots__ = 'slot', 'another_slot'

# class is also properly slotted:
class FurtherDerived(Derived):
    pass

Boondoggle

Answer 13

The original question was about general use cases not only about memory. So it should be mentioned here that you also get better performance when instantiating large amounts of objects - interesting e.g. when parsing large documents into objects or from a database.

Here is a comparison of creating object trees with a million entries, using slots and without slots. As a reference also the performance when using plain dicts for the trees (Py2.7.10 on OSX):

********** RUN 1 **********
1.96036410332 <class 'css_tree_select.element.Element'>
3.02922606468 <class 'css_tree_select.element.ElementNoSlots'>
2.90828204155 dict
********** RUN 2 **********
1.77050495148 <class 'css_tree_select.element.Element'>
3.10655999184 <class 'css_tree_select.element.ElementNoSlots'>
2.84120798111 dict
********** RUN 3 **********
1.84069895744 <class 'css_tree_select.element.Element'>
3.21540498734 <class 'css_tree_select.element.ElementNoSlots'>
2.59615707397 dict
********** RUN 4 **********
1.75041103363 <class 'css_tree_select.element.Element'>
3.17366290092 <class 'css_tree_select.element.ElementNoSlots'>
2.70941114426 dict

Test classes (ident, appart from slots):

class Element(object):
    __slots__ = ['_typ', 'id', 'parent', 'childs']
    def __init__(self, typ, id, parent=None):
        self._typ = typ
        self.id = id
        self.childs = []
        if parent:
            self.parent = parent
            parent.childs.append(self)

class ElementNoSlots(object): (same, w/o slots)

testcode, verbose mode:

na, nb, nc = 100, 100, 100
for i in (1, 2, 3, 4):
    print '*' * 10, 'RUN', i, '*' * 10
    # tree with slot and no slot:
    for cls in Element, ElementNoSlots:
        t1 = time.time()
        root = cls('root', 'root')
        for i in xrange(na):
            ela = cls(typ='a', id=i, parent=root)
            for j in xrange(nb):
                elb = cls(typ='b', id=(i, j), parent=ela)
                for k in xrange(nc):
                    elc = cls(typ='c', id=(i, j, k), parent=elb)
        to =  time.time() - t1
        print to, cls
        del root

    # ref: tree with dicts only:
    t1 = time.time()
    droot = {'childs': []}
    for i in xrange(na):
        ela =  {'typ': 'a', id: i, 'childs': []}
        droot['childs'].append(ela)
        for j in xrange(nb):
            elb =  {'typ': 'b', id: (i, j), 'childs': []}
            ela['childs'].append(elb)
            for k in xrange(nc):
                elc =  {'typ': 'c', id: (i, j, k), 'childs': []}
                elb['childs'].append(elc)
    td = time.time() - t1
    print td, 'dict'
    del droot

Rushton

Answer 14

You have — essentially — no use for __slots__.

For the time when you think you might need __slots__, you actually want to use Lightweight or Flyweight design patterns. These are cases when you no longer want to use purely Python objects. Instead, you want a Python object-like wrapper around an array, struct, or numpy array.

class Flyweight(object):

    def get(self, theData, index):
        return theData[index]

    def set(self, theData, index, value):
        theData[index]= value

The class-like wrapper has no attributes — it just provides methods that act on the underlying data. The methods can be reduced to class methods. Indeed, it could be reduced to just functions operating on the underlying array of data.

Marmion

What has Flyweight to do with __slots__? – Krieger

@oefe: I certainly don't get your question. I can quote my answer, if it helps "when you think you might need slots, you actually want to use ... Flyweight design pattern". That's what Flyweight has to do with slots. Do you have a more specific question? – Marmion

@oefe: Flyweight and __slots__ are both optimization techniques to save memory. __slots__ shows benefits when you have many many objects as well as Flyweight design pattern. The both solve the same problem. – Crean

Is there a available comparison between using slots and using Flyweight regarding memory consumption and speed? – Stubbed

... are both optimisation techniques to save memory ... and __slots__ can be implemented as an afterthought. – Scag

Although Flyweight is certainly useful in some contexts, believe it or not, the answer to "how can I reduce memory usage in Python when I create a zillion objects" is not always "don't use Python for your zillion objects." Sometimes __slots__ really is the answer, and as Evgeni points out, it can be added as a simple afterthought (e.g. you can focus on correctness first, and then add performance). – Corves

You can save a lot of memory using Flyweight when you aim to share attributes with identic values between instances, but this may not be the case with __slots__. – K

S.Lott, would you reconsider or rewrite any of this answer? – Anticipatory