I Was Wrong About Python’s super() — Here’s What It Actually Does

I used super() thinking it just called the parent method—but I had no idea what was really going on under the hood.

I Was Wrong About Python’s super() — Here’s What It Actually Does

Here’s the truth about how super() works in Python, why it's more powerful (and tricky) than you think, and how to use it the right way in real-world code.

If you’ve worked with Python classes long enough, chances are you’ve come across the super() function.

Maybe you even used it without thinking too much about it.

I know I did.

I used to believe super() was just a cleaner way to call a parent class method — a kind of syntactic sugar for ParentClass.method(self).

But after diving deeper into Python’s object model, I realized I was only scratching the surface.

Turns out, super() is way smarter than I thought.
Here’s what it actually does — and why you should care.

What I Thought super() Did

Like many developers, I picked up the habit of using super() to avoid hardcoding parent class names. I believed:

class Dog(Animal): 
    def speak(self): 
        super().speak()

was just a neater way of saying:

class Dog(Animal): 
    def speak(self): 
        Animal.speak(self)

It was only about avoiding repetition, right?
Wrong.

That’s only part of the picture.

What super() Actually Does

The super() function doesn’t just blindly call the immediate parent method.

It walks the Method Resolution Order (MRO) — a linear path Python calculates for method lookup in class hierarchies, especially when multiple inheritance is involved.

This means super() allows a method to delegate part of its behavior up the inheritance chain without needing to know which class comes next.

Let me show you why that matters.

A Real Example: The Power of MRO

Here’s a classic multiple inheritance example:

class A: 
    def process(self): 
        print("A.process") 
 
class B(A): 
    def process(self): 
        print("B.process") 
        super().process() 
 
class C(A): 
    def process(self): 
        print("C.process") 
        super().process() 
 
class D(B, C): 
    def process(self): 
        print("D.process") 
        super().process() 
 
d = D() 
d.process()

What do you expect the output to be?

The Output Might Surprise You

D.process   
B.process   
C.process   
A.process

Why? Because Python calculates the MRO for D as:

D -> B -> C -> A

When super() is used, it follows this chain regardless of which class you're currently in. So when D.process() calls super().process(), it goes to B, then C, then A — each class gets a turn.

This is cooperative multiple inheritance in action. Without super(), each class would need to manually call the next one — and good luck figuring out what “next” means when the hierarchy gets deep.

Why super() is Essential in Modern Python

Python’s super() isn’t just a utility; it’s a design pattern.

It encourages loose coupling between classes.

Each class in the hierarchy trusts that super() will handle forwarding correctly, even if the hierarchy changes.

In fact, when you don’t use super() in a cooperative inheritance setting, your code becomes brittle and error-prone.

Imagine if B.process() directly called A.process() — C.process() would get skipped entirely.

When Not to Use super()

Of course, super() isn’t always the answer.

If your class hierarchy is simple and doesn’t use multiple inheritance, calling the parent method directly might be fine.

Also, super() only works with new-style classes, which means your classes must inherit from object (not a problem in Python 3, where all classes do by default).

But if you’re building reusable components or working with mixins and multiple inheritance, super() is your best friend.

Final Thoughts

I was wrong about super() for a long time. I thought it was a small helper function with limited value.

But once I understood its true role in Python’s object-oriented model, it changed how I design class hierarchies.

If you’ve been underestimating super() like I was — it’s time to give it another look.
Not just for the sake of writing cleaner code, but for writing correct and scalable code.

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