Stop Using Python Classes Like This — It’s Slowing You Down

If you’re using classes in Python just because “that’s how real developers do it,” you might be writing more code than you need — and slowing yourself down in the process.

Stop Using Python Classes Like This — It’s Slowing You Down

If you’ve been writing Python for a while, chances are you’ve leaned heavily on classes — sometimes too heavily.

While object-oriented programming (OOP) is powerful, many developers misuse classes in ways that make their code more complex, slower, and harder to maintain.

In this article, I’ll walk you through the most common class misuses I’ve seen (and done myself), why they’re problematic, and how to fix them — so you can write faster, cleaner Python.

The Problem: Using Classes as Fancy Containers

A common beginner (and even intermediate) mistake in Python is creating a class just to hold some data:

class User: 
    def __init__(self, name, email): 
        self.name = name 
        self.email = email

If you’re not adding any behavior to this class — just using it as a data container — you’re better off with something simpler like namedtuple or dataclass.

Why it’s bad:

Verbose syntax

Manual implementation of __init__, __repr__, __eq__, etc.

Slower performance and higher memory usage compared to lighter alternatives

Better: Use @dataclass

from dataclasses import dataclass 
 
@dataclass 
class User: 
    name: str 
    email: str

Just one decorator and Python does all the heavy lifting for you — less boilerplate, and your intent is clearer.

The Problem: Over-Abstracting Early

Another classic misuse: prematurely turning everything into a class when a simple function would do.

Example:

class Calculator: 
    def add(self, a, b): 
        return a + b 
 
calc = Calculator() 
print(calc.add(2, 3))

Why? There’s no state here, no inheritance, no need for instantiation.

Better: Use a Function

def add(a, b): 
    return a + b 
 
print(add(2, 3))

Unless you’re maintaining state or extending behavior, a function is almost always simpler and faster.

The Problem: Misusing Inheritance

Another drag on performance and readability: deep and unnecessary inheritance trees.

class Animal: 
    def speak(self): 
        pass 
 
class Dog(Animal): 
    def speak(self): 
        return "Woof" 
 
class Cat(Animal): 
    def speak(self): 
        return "Meow"

While this looks fine at first glance, it introduces complexity — especially as hierarchies grow or behavior starts overlapping.

Better: Use Composition

class Speaker: 
    def __init__(self, sound): 
        self.sound = sound 
 
    def speak(self): 
        return self.sound 
 
dog = Speaker("Woof") 
cat = Speaker("Meow")

Composition gives you more flexibility, less coupling, and often better performance in Python.

The Problem: Treating Python Like Java

Python isn’t Java. It’s dynamic, expressive, and built to embrace simplicity over ceremony.

You don’t need:

Getters and setters (use @property if needed)

Factory classes for everything

Private fields with underscores everywhere

Embrace Pythonic Principles

class Product: 
    def __init__(self, price): 
        self._price = price 
 
    @property 
    def price(self): 
        return self._price 
 
    @price.setter 
    def price(self, value): 
        if value < 0: 
            raise ValueError("Price cannot be negative") 
        self._price = value

Use tools Python gives you — decorators, duck typing, first-class functions — to write less and do more.

So, When Should You Use a Class?

Use classes when:

You need to maintain state across method calls

You’re modeling complex entities with behaviors

You’re leveraging inheritance or polymorphism responsibly

You want to create reusable blueprints for objects

Avoid classes when:

You just need to group data (use dataclass)

You only have one method (use a function)

You’re tempted to over-engineer upfront

Final Thoughts: Less Class, More Clarity

Python gives you many paradigms: functional, procedural, and object-oriented. But many developers fall into the trap of over-OOPing everything, often mimicking patterns from other languages.

Every class you write comes with cost: mental overhead, boilerplate, testing surface, and potential for bugs.

If you’re reaching for a class by default — stop and ask:

Can this be a function? A dataclass? Something simpler?

Chances are, the simpler version will be more Pythonic — and your future self (and your teammates) will thank you.

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