The Best Python Program Architecture?

Before You Start

No matter what kind of tasks you are working on—whether it's backend web development or building a data analysis system—the principles of a high-quality architecture remain the same across all fields.

Prerequisites

• Knowledge of Python syntax
• Basic understanding of object-oriented programming and programming patterns.

Good or Bad Code?

If you compare the code of an experienced Python developer with that of a junior developer, you will certainly notice significant differences. Often, you might think: "Wow! Their code is much better than mine."

But how can you differentiate good code from bad code?

From my perspective, good code should have the following characteristics:

• It should be written like a story: The code should have a smooth introduction, then reveal its main parts logically. Each section should be composed of simple and understandable blocks, each with a unique and clear purpose (SRP: Single Responsibility Principle).
• Documentation and comments: Only important and necessary comments should be added. The goal is to write code so clear that comments are unnecessary. However, a simple docstring is essential for each function, class, and file.
• Ease of evolution and maintenance: Good code should allow for easy addition of new features, modification of existing ones, and scalability to handle large workloads.

Stay Zen!

Python has a list of 20 design principles written by Tim Peters, an experienced Python developer, to guide us in writing good Python code. This list is called The Zen of Python:

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

Before designing a robust and maintainable architecture, try to apply these principles as much as possible to keep your code clean.

Structuring Your Python Code for Better Maintainability

A well-organized project makes navigation and understanding easier for you and your team. Adopt a standard structure like this one:

my_project/
│
├── main.py
├── requirements.txt
├── .env
├── README.md
├── my_project/
│   ├── __init__.py
│   ├── models/
│   │   ├── __init__.py
│   │   └── user.py
│   ├── services/
│   │   ├── __init__.py
│   │   └── email_service.py
│   ├── utils/
│   │   ├── __init__.py
│   │   └── helpers.py
│   └── tests/
│       ├── __init__.py
│       └── test_user.py

This structure separates responsibilities into clear modules: models, services, utilities, and tests.

Applying the SOLID Principles

The SOLID principles help create maintainable and scalable code:

• Single Responsibility Principle: Each class or function should have only one responsibility.
• Open/Closed Principle: Your code should be open for extension but closed for modification.
• Liskov Substitution Principle: Subclasses should be able to replace their parent classes without breaking the program.
• Interface Segregation Principle: Prefer multiple specific interfaces over a single general one.
• Dependency Inversion Principle: High-level modules should not depend on low-level modules.

Automated Testing

Tests are essential for maintaining reliable and scalable code. Integrate unit tests from the beginning of your project:

def test_greet_user():
    assert greet_user("Alice") == "Hello, Alice!"

Automate your tests with a CI/CD pipeline to quickly detect regressions.

Conclusion

By following these best practices, you can transform your Python code into a powerful, readable, and maintainable tool.

Remember, good code is not just functional—it is designed to be understood and improved by anyone who works on it. 🚀

Written by Gauvain Palanga