__slots__ does not (significantly) speed up attribute access:

>>> class Foo(object):
...     __slots__ = ('spam',)
...     def __init__(self):
...         self.spam = 'eggs'
... 
>>> class Bar(object):
...     def __init__(self):
...         self.spam = 'eggs'
... 
>>> import timeit
>>> timeit.timeit('t.spam', 'from __main__ import Foo; t=Foo()')
0.07030296325683594
>>> timeit.timeit('t.spam', 'from __main__ import Bar; t=Bar()')
0.07646608352661133

The goal of using __slots__ is to save memory; instead of using a .__dict__ mapping on the instance, the class has descriptors objects for each and every attribute named in __slots__ and instances have the attribute assigned wether or not they have an actual value:

>>> class Foo(object):
...     __slots__ = ('spam',)
... 
>>> dir(Foo())
['__class__', '__delattr__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slots__', '__str__', '__subclasshook__', 'spam']
>>> Foo().spam
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: spam
>>> Foo.spam
<member 'spam' of 'Foo' objects>
>>> type(Foo.spam)
<type 'member_descriptor'>

So python still has to look at the class for each attribute access on an instance of Foo (to find the descriptor). Any unknown attribute (say, Foo.ham) will still result in Python looking through the class MRO to search for that attribute, and that includes dictionary searches. And you can still assign additional attributes to the class:

>>> Foo.ham = 'eggs'
>>> dir(Foo)
['__class__', '__delattr__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slots__', '__str__', '__subclasshook__', 'ham', 'spam']
>>> Foo().ham
'eggs'

The slot descriptors are created when the class is created, and access memory assigned to each instance to store and retrieve a reference to the associated value (the same chunk of memory that tracks instance reference counts and a reference back to the class object). Without slots, a descriptor for __dict__ is used accessing a reference to a dict object in the same manner.

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__. This has the effect of denying the creation of non-slotted attributes on its instances, including within the class body (such as in methods like __init__).

Quick Caveats

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

Copyclass 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), and problems might not otherwise manifest, but your objects will take up more space than they should. Python 3.8:

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

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

Copy>>> 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: create abstractions with empty __slots__ for every parent class (or for every parent class but one), then inherit from these abstractions instead of their concrete versions in your new concrete class. (The original parent classes should also inherit from their respective abstractions, of course.)

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's trivial to demonstrate measurably significant speedup:

Copyimport 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

Copy>>> 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.

Copy>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342

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

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):

Copy       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__ scales 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".

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

Demonstration

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:

Copyclass Base(object): 
    __slots__ = ()

now:

Copy>>> 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__

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

and now:

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

but:

Copy>>> 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):

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

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

and

Copy>>> 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__:

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

and:

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

However, __slots__ may cause problems for multiple inheritance:

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

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

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

```python
>>> 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:

Copyfrom 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

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

and now:

Good question, slots can definitely help in some situations, but it’s not always worth using. By default, Python gives every object a dynamic dict, which allows you to add attributes at runtime, but that also uses more memory. When you define slots, you tell Python exactly which attributes an object can have, and it skips creating the dict, which can lead to significant memory savings, especially if you’re creating thousands or millions of instances. That said, there are tradeoffs. You lose the ability to add new attributes unless you explicitly include dict in the slots, and using slots can make inheritance a bit trickier, especially when combining it with other classes that also define slots. Also worth noting: if you’re using dataclasses, you’ll need to explicitly enable slots via @DataClass(slots=True) (available in Python 3.10+). In general, slots makes sense in performance-critical systems, like compilers, interpreters, or memory-heavy data processing pipelines, but for most everyday Python code, the benefits are usually too small to matter.