The SOLID principle is five design principles used in object-oriented programming to improve the readability, flexibility and maintainability of software design, and is also applicable in Python development. 1. The single responsibility principle (SRP) requires a class to do only one thing, avoid putting irrelevant functions in the same class, which can be implemented by splitting logic or using auxiliary modules; 2. The open and closed principle (OCP) emphasizes opening up for extensions, closing down on modifications, and extending functions through inheritance or combination without changing existing code; 3. The Richter replacement principle (LSP) ensures that subclasses can replace parent classes without destroying program behavior, and keep the method contract consistent, avoid introducing exceptions or different return types during rewriting; 4. The interface isolation principle (ISP) advocates defining fine-grained interfaces so that the client only depends on the required functions, Python can be implemented through abstract base classes or Mixins; 5. The dependency inversion principle (DIP) advocates that both high-level and low-level modules rely on abstraction, and decoupling is commonly used in Python to achieve decoupling, which is convenient for testing and replacement implementation. These principles help developers build clearer and easier to maintain system structures.

SOLID principles are a set of five design principles intended to make software designs more understandable, flexible, and maintainable. They were introduced by Robert C. Martin (also known as Uncle Bob) and are especially useful in object-oriented programming. In Python development, applying these principles helps developers write cleaner, scalable, and easier-to-maintain code.

Single Responsibility Principle (SRP)

A class should have only one reason to change — meaning it should do one thing and do it well.

In practice, this means breaking down complex logic into separate components. For example, if you're writing a class that handles both user authentication and logging, it's violating SRP. Instead, split those responsibilities into two classes: one for authentication logic and another for logging behavior.

Why it matters in Python:
Python encourages modular and readable code. Keeping your classes focused make debugging easier and reduces side effects when changes occur.

• Avoid putting unrelated functions inside the same class.
• If a class starts handling multiple tasks, consider splitting it up.
• Use helper modules or utility functions instead of overloading a single class.

Open/Closed Principle (OCP)

Software entities (like classes, modules, functions) should be open for extension but closed for modification.

This means that once a class is working and tested, you shouldn't need to change its source code every time a new feature comes along. Instead, extend it through inheritance or composition.

Example in Python:
Let's say you have a PaymentProcessor class. Instead of modifying it each time you add a new payment method, create an abstract base class or interface like PaymentMethod , then implement subclasses such as CreditCardPayment , PayPalPayment , etc.

 class PaymentProcessor:
    def __init__(self, method: PaymentMethod):
        self.method = method

    def process(self):
        self.method.process()

• Use polymorphism to allow different behaviors without changing existing code.
• Abstract base classes ( abc module) can help enforce this pattern.
• This makes your system more adaptable to future features.

Liskov Substitution Principle (LSP)

Objects of a superclass should be replaced with objects of a subclass without breaking the application.

This principle ensures that a child class doesn't break the expected behavior of the parent class. In Python, since it's dynamically typed, this principle helps avoid confusing bugs caused by unexpected overrides.

What to watch out for: If a subclass throws an exception or returns a completely different type than the parent method, it might violate LSP.

For example, if you have a Rectangle class with a set_width() and set_height() method, and a Square class inherits from it but overrides those methods to keep width and height equal, using Square where Rectangle is expected could lead to unexpected behavior.

• Ensure overridden methods maintain the same contract.
• Don't force subclasses to throw exceptions for methods they don't support.
• Think carefully about how inheritance affects expectations.

Interface Segregation Principle (ISP)

Clients shouldn't be forced to depend on interfaces they don't use.

Instead of having one large interface with many methods, define smaller, more specific ones so that classes only need to implement what they actually use.

How it applies in Python:
Since Python doesn't have interfaces per se (but has abstract base classes), you can still follow ISP by creating small, focused base classes or mixins.

For instance, instead of having a Worker interface with work() , eat() , and rest() , separate them into Workable , Eatable , and Restable . Then, a robot can implement only Workable , while a human implements all three.

• Split large abstract classes into smaller ones.
• Mixins can be used effectively to combine functionality.
• Helps prevent unnecessary implementation and keeps dependencies clean.

Dependency Inversion Principle (DIP)

High-level modules shouldn't depend on low-level modules. Both should depend on abstractions. Also, abstractions shouldn't depend on details; details should depend on abstractions.

This allows for loosely coupled systems. In Python, this often means coding against interfaces or abstract classes rather than concrete implementations.

Practical approach: Use dependency injection to pass required components rather than hardcoding them inside a class.

For example, instead of directly instantiating a database connection inside a service class, inject a database adapter that follows a common interface.

 class UserService:
    def __init__(self, db: Database):
        self.db = db

• Use dependency injection to reduce coupling.
• Define behaviors through abstract classes or protocols.
• Makes testing easier — just swap in a mock version during tests.

Applying SOLID principles in Python isn't about strict rule-following but about making thoughtful design choices. These ideas help structure your code in ways that anticipate change and reduce complexity.

It might feel like extra work at first, especially in smaller projects, but the payoff becomes clear as your codebase grows. And honestly, some of these principles blend naturally into Python's clean syntax and dynamic nature — you might already be doing parts of them without realizing it.

Basically that's it.

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