Python Slots Property

1. Class Property Python
2. Python Slot Machine Code
3. Monty Python Slot Machine App
4. Python Slots Property Management

This can be used in exactly the same way as the DataClassCard and NamedTupleCard examples earlier. The attrs project is great and does support some features that data classes do not, including converters and validators. Furthermore, attrs has been around for a while and is supported in Python 2.7 as well as Python 3.4 and up. However, as attrs is not a part of the standard library, it does add. In earlier versions of Python (property decorators (which we will learn in a bit) were not introduced and property function was used. The property function is used to provide methods to control the access of attributes. Using property function we can bind the getter, setter and deleter function altogether or individually with an attribute name. 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 type(Foo.foo) getsizeof(Foo.foo) 72.

In this article, you will learn about Python @property. You will also learn about getters and setters and also learn about the Pythonic way of using them.

Okay, before learning about Python @property, you must be familiar of some basic yet must know concepts of attributes in Python.

What is a Python attribute?

Python attributes are simply instance variables.

If we dig deeper into it, then you will know that you can actually control attributes. Attributes are simply the names used after objects as a reference to a function or a variable.

For example, object.foo. Here foo is the attribute.

Let’s take an example to understand.

Here length is the attribute.

So what’s actually happening here is that when we access an object like obj.length, we are getting back the value stored in a dict on the object.

Whenever we access an attribute Python first search that in objects __dict__()dictionary. When we say obj.length, it’s default behavior is effectively obj.__dict__('length').

Now that we know about attributes, let’s jump on to the main topic: Python properties.

Let’s start with the need of Python property in real time problems.

Why do we need Python property?

Let’s explain the exact need of Python property with an example to convert currency (currency_y = currency_x * 28).

Now we can make objects of this class to manipulate the attribute currency_x.

So far everything works great.

Now let’s say people like this converter and start using our currency converter with their programs and software by inheriting this class or using it however to develop further modules.

At some point in time, let’s say one of the users comes and suggests that this program should not allow converting the negative value of the currency.

Sounds pretty much fair as the value of the currency cannot be less than 0 and why should we even allow this conversion?

One might argue about encapsulation of data as we are giving direct access to the class variables. We should have used private variables but note that there are no private variables in Python technically.

Yes, we can make them private explicitly by using a leading underscore(_) with variables like self._currency_x but that doesn’t even prevent a programmer from accessing it beyond the class and manipulating it.

The bigger concern to address at this point of time is to restrict users from converting negative values of currencies. This problem can be addressed by using getters and setters interface in our program.

Using getters and setters in Python

Here is the way to implement getters and setters in Python.

Now we can test our program using the interpreter.

That worked perfectly fine and with that update, we successfully imposed the restriction on converting the negative value of the currency.

Now comes the bigger problem.

This update may is easy to fix but we will have to replace c.currency_x = 2 with c.set_currency_x (2). This seems so easy for a small program but when we have hundreds of lines of code using this class, changing code becomes so hectic.

This isn’t the optimal solution for such problems as the program by no means supports the backward compatibility. This is where Python property works like charm.

Now let’s implement Pythonic way to address such problems using Python property.

Python Property() Function

In earlier versions of Python (<2.6), property decorators (which we will learn in a bit) were not introduced and property() function was used.

The property() function is used to provide methods to control the access of attributes. Using property() function we can bind the getter, setter and deleter function altogether or individually with an attribute name.

To create a property, we define the instance variable and one or more method functions.

Syntax

Where,

• fget is a getter function and must be defined if we need to read the attribute else AttributeError is raised.
• fset is a setter function and must be defined if we want to set or write that attribute else AttributeError is raised.
• fdel is a deleter method to delete the attribute.
• doc is the documentation string that describes the attribute.

Here is the simple implementation of property function to address the problem we faced earlier.

That’s the implementation of property function in the problem we faced previously. Here the property attaches the defined getter and setter methods to the variable currency_x. Now, whenever a value is assigned to currency_x, set_currency_x() method will be automatically invoked and we won’t need to change any remaining code.

We have attached print statements inside getter and setter methods to make sure that the program flow is reaching to those functions. Here is the demonstration in Python shell.

Note: The value of the currency is stored in private variable _currrency_x and attribute currency_x is the property object providing the interface to the private variable.

Python @property decorator

Starting with the Python version later than 2.6, this property function can be implemented as Python decorator. The decorator method is used as getter method.

Here is how above program can be implemented using Python @property decorator.

The Question :

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

The Answer 1

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:

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:

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:

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.

• 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:

and

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

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.

Class Property Python

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):

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”.

Demonstration of __slots__:

To deny the creation of a __dict__, you must subclass object:

now:

Or subclass another class that defines __slots__

and now:

but:

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):

and

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__:

And:

However, __slots__ may cause problems for multiple inheritance:

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

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:

Add '__dict__' to __slots__ to get dynamic assignment:

and now:

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:

usage:

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

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:

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__:

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

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

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:

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):

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

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:

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.

in Python 2.7:

in Python 3.6

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__.

Python Slot Machine Code

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.

Create some normal objects and slotted objects:

Instantiate a million of them:

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

Access the regular objects and their __dict__ and inspect again:

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.

The 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).

The 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.

The 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.

The Answer 5

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

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.

The 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.

The Answer 7

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

The Answer 8

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.

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.

The Answer 9

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.

The Answer 10

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.

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.

Monty Python Slot Machine App

The Answer 11

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):

Test classes (ident, appart from slots):

Python Slots Property Management

testcode, verbose mode: