Advanced Python: 9 Best Practices You Should Follow When Defining Classes

Write classes like a pro — avoid the traps, embrace the tools.

Advanced Python: 9 Best Practices You Should Follow When Defining Classes

Learn how to write cleaner, faster, and more maintainable Python classes using these 9 advanced-level tips that every serious developer should apply today.

Introduction: Don’t Just Write Classes — Craft Them

Python makes defining classes easy. Maybe too easy. Most developers can whip up a class without much thought — toss in a few attributes, maybe an __init__, and call it a day.

But here’s the problem: what starts as a simple class can quickly become a tangled mess — hard to maintain, hard to test, and worst of all, hard to reuse. Whether you’re working on a large codebase or just want to up your Python game, applying smart practices to class design can be a game-changer.

Let’s explore 9 advanced class-definition techniques in Python that will make your code more Pythonic, scalable, and bulletproof.

1. Prefer @dataclass for Simple Data Containers

If your class is just a data holder with a few attributes, avoid manually writing boilerplate. Use the @dataclass decorator.

from dataclasses import dataclass 
 
@dataclass 
class Book: 
    title: str 
    author: str 
    pages: int

Auto-generates __init__, __repr__, __eq__, and more.

Cleaner and more readable.

Reduces boilerplate.

Use frozen=True if you want your instances to be immutable.

2. Use __slots__ to Save Memory

If you’re creating a large number of instances, consider using __slots__ to restrict dynamic attribute creation.

class Point: 
    __slots__ = ['x', 'y'] 
    def __init__(self, x, y): 
        self.x = x 
        self.y = y

Saves memory by preventing the creation of __dict__ per instance.

Can prevent accidental addition of unwanted attributes.

Use only when you’re sure the attributes won’t change dynamically.

3. Favor Composition Over Inheritance

Avoid deep inheritance chains. Use composition to assemble behavior instead of extending it.

Instead of:

class ElectricCar(Car): 
    def charge(self): 
        ...

Consider:

class Battery: 
    def charge(self): 
        ... 
 
class ElectricCar: 
    def __init__(self): 
        self.battery = Battery()

Easier to test individual components.

Promotes modular design.

Reduces coupling between classes.

4. Use classmethod as Alternative Constructors

Need multiple ways to initialize your object? Use class methods.

from datetime import datetime 
 
class Event: 
    def __init__(self, name, timestamp): 
        self.name = name 
        self.timestamp = timestamp 
 
    @classmethod 
    def now(cls, name): 
        return cls(name, datetime.now())

Makes code more expressive.

Adds flexibility to object creation.

5. Don’t Abuse __init__

Keep __init__ lean and focused. Avoid doing too much inside it, especially side-effects like network calls or file reads.

Instead:

• Accept config/data, store it.
• Do the heavy lifting in a separate method like .initialize() or a factory function.

This keeps object creation fast and testable.

6. Implement __repr__ for Better Debugging

A well-crafted __repr__ makes your logs and debugging output far more informative.

class User: 
    def __init__(self, username, active=True): 
        self.username = username 
        self.active = active 
 
    def __repr__(self): 
        return f"User(username={self.username!r}, active={self.active})"

Helps in debugging and logging.

Makes unit test outputs readable.

It’s a small investment with big returns.

7. Encapsulate with Properties — the Pythonic Way

Use the @property decorator to control access to your attributes without changing how they’re accessed.

class Temperature: 
    def __init__(self, celsius): 
        self._celsius = celsius 
 
    @property 
    def fahrenheit(self): 
        return self._celsius * 9 / 5 + 32

Preserves interface consistency.

Adds logic to attribute access without breaking your API.

Clean and Pythonic.

8. Define Meaningful __eq__ and __hash__ for Identity

For objects that will be compared or used in sets/dictionaries, define __eq__ and __hash__ explicitly (or use @dataclass(eq=True, frozen=True)).

class Coordinate: 
    def __init__(self, x, y): 
        self.x = x 
        self.y = y 
 
    def __eq__(self, other): 
        return isinstance(other, Coordinate) and self.x == other.x and self.y == other.y 
 
    def __hash__(self): 
        return hash((self.x, self.y))

Your class will work as expected in sets or as dict keys.

Prevents subtle bugs during comparisons.

9. Know When to Use __str__, __repr__, __format__, and __bytes__

Each of these has a distinct role:

• __str__: Human-readable
• __repr__: Debug-friendly, often unambiguous
• __format__: Custom formatting with format()
• __bytes__: Byte representation, for binary protocols

Even just adding __str__ and __repr__ makes a huge difference.

class Money: 
    def __init__(self, amount): 
        self.amount = amount 
 
    def __str__(self): 
        return f"${self.amount:.2f}" 
 
    def __repr__(self): 
        return f"Money(amount={self.amount})"

Conclusion: Don’t Just Code — Architect

Writing good classes isn’t just about knowing the syntax — it’s about making intentional design choices. The 9 practices above are more than tricks; they’re habits that will help you build code that lasts longer, performs better, and reads like a story rather than a puzzle.

The more thoughtfully you define your classes, the less debugging you’ll do later.

Final Thought

Great Python developers don’t just use classes — they design them. Think of each class as a contract, a building block, and a future collaborator’s best friend.

Write classes today that your future self will thank you for.