Supercharge Your Classes With Python super() (original) (raw)

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Supercharge Your Classes With Python super()

While Python isn’t purely an object-oriented language, it’s flexible enough and powerful enough to allow you to build your applications using the object-oriented paradigm. One of the ways in which Python achieves this is by supporting inheritance, which it does with super().

In this tutorial, you’ll learn about the following:

• The concept of inheritance in Python
• Multiple inheritance in Python
• How the super() function works
• How the super() function in single inheritance works
• How the super() function in multiple inheritance works

Take the Quiz: Test your knowledge with our interactive “Supercharge Your Classes With Python super()” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Supercharge Your Classes With Python super()

In this quiz, you'll test your understanding of inheritance and the super() function in Python. By working through this quiz, you'll revisit the concept of inheritance, multiple inheritance, and how the super() function works in both single and multiple inheritance scenarios.

An Overview of Python’s super() Function

If you have experience with object-oriented languages, you may already be familiar with the functionality of super().

If not, don’t fear! While the official documentation is fairly technical, at a high level super() gives you access to methods in a superclass from the subclass that inherits from it.

super() alone returns a temporary object of the superclass that then allows you to call that superclass’s methods.

Why would you want to do any of this? While the possibilities are limited by your imagination, a common use case is building classes that extend the functionality of previously built classes.

Calling the previously built methods with super() saves you from needing to rewrite those methods in your subclass, and allows you to swap out superclasses with minimal code changes.

super() in Single Inheritance

If you’re unfamiliar with object-oriented programming concepts, inheritance might be an unfamiliar term. Inheritance is a concept in object-oriented programming in which a class derives (or inherits) attributes and behaviors from another class without needing to implement them again.

For me at least, it’s easier to understand these concepts when looking at code, so let’s write classes describing some shapes:

Here, there are two similar classes: Rectangle and Square.

You can use them as below:

In this example, you have two shapes that are related to each other: a square is a special kind of rectangle. The code, however, doesn’t reflect that relationship and thus has code that is essentially repeated.

By using inheritance, you can reduce the amount of code you write while simultaneously reflecting the real-world relationship between rectangles and squares:

Here, you’ve used super() to call the __init__() of the Rectangle class, allowing you to use it in the Square class without repeating code. Below, the core functionality remains after making changes:

In this example, Rectangle is the superclass, and Square is the subclass.

Because the Square and Rectangle .__init__() methods are so similar, you can simply call the superclass’s .__init__() method (Rectangle.__init__()) from that of Square by using super(). This sets the .length and .width attributes even though you just had to supply a single length parameter to the Square constructor.

When you run this, even though your Square class doesn’t explicitly implement it, the call to .area() will use the .area() method in the superclass and print 16. The Square class inherited .area() from the Rectangle class.

What Can super() Do for You?

So what can super() do for you in single inheritance?

Like in other object-oriented languages, it allows you to call methods of the superclass in your subclass. The primary use case of this is to extend the functionality of the inherited method.

In the example below, you will create a class Cube that inherits from Square and extends the functionality of .area() (inherited from the Rectangle class through Square) to calculate the surface area and volume of a Cube instance:

Now that you’ve built the classes, let’s look at the surface area and volume of a cube with a side length of 3:

Here you have implemented two methods for the Cube class: .surface_area() and .volume(). Both of these calculations rely on calculating the area of a single face, so rather than reimplementing the area calculation, you use super() to extend the area calculation.

Also notice that the Cube class definition does not have an .__init__(). Because Cube inherits from Square and .__init__() doesn’t really do anything differently for Cube than it already does for Square, you can skip defining it, and the .__init__() of the superclass (Square) will be called automatically.

super() returns a delegate object to a parent class, so you call the method you want directly on it: super().area().

Not only does this save us from having to rewrite the area calculations, but it also allows us to change the internal .area() logic in a single location. This is especially in handy when you have a number of subclasses inheriting from one superclass.

A super() Deep Dive

Before heading into multiple inheritance, let’s take a quick detour into the mechanics of super().

While the examples above (and below) call super() without any parameters, super() can also take two parameters: the first is the subclass, and the second parameter is an object that is an instance of that subclass.

First, let’s see two examples showing what manipulating the first variable can do, using the classes already shown:

In Python 3, the super(Square, self) call is equivalent to the parameterless super() call. The first parameter refers to the subclass Square, while the second parameter refers to a Square object which, in this case, is self. You can call super() with other classes as well:

In this example, you are setting Square as the subclass argument to super(), instead of Cube. This causes super() to start searching for a matching method (in this case, .area()) at one level above Square in the instance hierarchy, in this case Rectangle.

In this specific example, the behavior doesn’t change. But imagine that Square also implemented an .area() function that you wanted to make sure Cube did not use. Calling super() in this way allows you to do that.

What about the second parameter? Remember, this is an object that is an instance of the class used as the first parameter. For an example, isinstance(Cube, Square) must return True.

By including an instantiated object, super() returns a bound method: a method that is bound to the object, which gives the method the object’s context such as any instance attributes. If this parameter is not included, the method returned is just a function, unassociated with an object’s context.

For more information about bound methods, unbound methods, and functions, read the Python documentation on its descriptor system.

super() in Multiple Inheritance

Now that you’ve worked through an overview and some examples of super() and single inheritance, you will be introduced to an overview and some examples that will demonstrate how multiple inheritance works and how super() enables that functionality.

Multiple Inheritance Overview

There is another use case in which super() really shines, and this one isn’t as common as the single inheritance scenario. In addition to single inheritance, Python supports multiple inheritance, in which a subclass can inherit from multiple superclasses that don’t necessarily inherit from each other (also known as sibling classes).

I’m a very visual person, and I find diagrams are incredibly helpful to understand concepts like this. The image below shows a very simple multiple inheritance scenario, where one class inherits from two unrelated (sibling) superclasses:

A diagrammed example of multiple inheritance (Image: Kyle Stratis)

To better illustrate multiple inheritance in action, here is some code for you to try out, showing how you can build a right pyramid (a pyramid with a square base) out of a Triangle and a Square:

This example declares a Triangle class and a RightPyramid class that inherits from both Square and Triangle.

You’ll see another .area() method that uses super() just like in single inheritance, with the aim of it reaching the .perimeter() and .area() methods defined all the way up in the Rectangle class.

The problem, though, is that both superclasses (Triangle and Square) define a .area(). Take a second and think about what might happen when you call .area() on RightPyramid, and then try calling it like below:

Did you guess that Python will try to call Triangle.area()? This is because of something called the method resolution order.

Method Resolution Order

The method resolution order (or MRO) tells Python how to search for inherited methods. This comes in handy when you’re using super() because the MRO tells you exactly where Python will look for a method you’re calling with super() and in what order.

Every class has an .__mro__ attribute that allows us to inspect the order, so let’s do that:

This tells us that methods will be searched first in Rightpyramid, then in Triangle, then in Square, then Rectangle, and then, if nothing is found, in object, from which all classes originate.

The problem here is that the interpreter is searching for .area() in Triangle before Square and Rectangle, and upon finding .area() in Triangle, Python calls it instead of the one you want. Because Triangle.area() expects there to be a .height and a .base attribute, Python throws an AttributeError.

Luckily, you have some control over how the MRO is constructed. Just by changing the signature of the RightPyramid class, you can search in the order you want, and the methods will resolve correctly:

Notice that RightPyramid initializes partially with the .__init__() from the Square class. This allows .area() to use the .length on the object, as is designed.

Now, you can build a pyramid, inspect the MRO, and calculate the surface area:

You see that the MRO is now what you’d expect, and you can inspect the area of the pyramid as well, thanks to .area() and .perimeter().

There’s still a problem here, though. For the sake of simplicity, I did a few things wrong in this example: the first, and arguably most importantly, was that I had two separate classes with the same method name and signature.

This causes issues with method resolution, because the first instance of .area() that is encountered in the MRO list will be called.

When you’re using super() with multiple inheritance, it’s imperative to design your classes to cooperate. Part of this is ensuring that your methods are unique so that they get resolved in the MRO, by making sure method signatures are unique—whether by using method names or method parameters.

In this case, to avoid a complete overhaul of your code, you can rename the Triangle class’s .area() method to .tri_area(). This way, the area methods can continue using class properties rather than taking external parameters:

Let’s also go ahead and use this in the RightPyramid class:

The next issue here is that the code doesn’t have a delegated Triangle object like it does for a Square object, so calling .area_2() will give us an AttributeError since .base and .height don’t have any values.

You need to do two things to fix this:

1. All methods that are called with super() need to have a call to their superclass’s version of that method. This means that you will need to add super().__init__() to the .__init__() methods of Triangle and Rectangle.
2. Redesign all the .__init__() calls to take a keyword dictionary. See the complete code below.

There are a number of important differences in this code:

• **kwargs is modified in some places (such as RightPyramid.__init__()):** This will allow users of these objects to instantiate them only with the arguments that make sense for that particular object.
• Setting up named arguments before kwargs: You can see this in RightPyramid.__init__(). This has the neat effect of popping that key right out of the **kwargs dictionary, so that by the time that it ends up at the end of the MRO in the object class, **kwargs is empty.

Now, when you use these updated classes, you have this:

It works! You’ve used super() to successfully navigate a complicated class hierarchy while using both inheritance and composition to create new classes with minimal reimplementation.

Multiple Inheritance Alternatives

As you can see, multiple inheritance can be useful but also lead to very complicated situations and code that is hard to read. It’s also rare to have objects that neatly inherit everything from more than multiple other objects.

If you see yourself beginning to use multiple inheritance and a complicated class hierarchy, it’s worth asking yourself if you can achieve code that is cleaner and easier to understand by using composition instead of inheritance. Since this article is focused on inheritance, I won’t go into too much detail on composition and how to wield it in Python. Luckily, Real Python has published a deep-dive guide to both inheritance and composition in Python that will make you an OOP pro in no time.

There’s another technique that can help you get around the complexity of multiple inheritance while still providing many of the benefits. This technique is in the form of a specialized, simple class called a mixin.

A mixin works as a kind of inheritance, but instead of defining an “is-a” relationship it may be more accurate to say that it defines an “includes-a” relationship. With a mix-in you can write a behavior that can be directly included in any number of other classes.

Below, you will see a short example using VolumeMixin to give specific functionality to our 3D objects—in this case, a volume calculation:

In this example, the code was reworked to include a mixin called VolumeMixin. The mixin is then used by Cube and gives Cube the ability to calculate its volume, which is shown below:

This mixin can be used the same way in any other class that has an area defined for it and for which the formula area * height returns the correct volume.

A super() Recap

In this tutorial, you learned how to supercharge your classes with super(). Your journey started with a review of single inheritance and then showed how to call superclass methods easily with super().

You then learned how multiple inheritance works in Python, and techniques to combine super() with multiple inheritance. You also learned about how Python resolves method calls using the method resolution order (MRO), as well as how to inspect and modify the MRO to ensure appropriate methods are called at appropriate times.

For more information about object-oriented programming in Python and using super(), check out these resources:

• Official super() documentation
• Python’s super() Considered Super by Raymond Hettinger
• Object-Oriented Programming in Python

Take the Quiz: Test your knowledge with our interactive “Supercharge Your Classes With Python super()” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Supercharge Your Classes With Python super()

In this quiz, you'll test your understanding of inheritance and the super() function in Python. By working through this quiz, you'll revisit the concept of inheritance, multiple inheritance, and how the super() function works in both single and multiple inheritance scenarios.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Supercharge Your Classes With Python super()