Best Practices for Structuring a Python Project Like a Pro!

BUILD PYTHON PROJECTS THE RIGHT WAY!

Best Practices for Structuring a Python Project Like a Pro!

When you first start writing Python code, it’s tempting to throw everything into a single file and call it a day. And hey, for quick scripts or small utilities, that’s perfectly fine.

But once your project begins to grow — maybe it needs testing, documentation, configuration files, or package distribution — you’ll quickly realize the importance of proper structure. A well-structured Python project is easier to maintain, collaborate on, test, and scale.

In this post, we’ll walk through best practices for structuring a Python project like a seasoned developer — so whether you’re building a small library or the next big SaaS, you’ll have a solid foundation.

Why Structure Matters

Before diving into folder structures and config files, let’s address the “why.”

• Readability: Others (and future you) can understand the project at a glance.
• Scalability: You can easily add new features without a tangled mess.
• Reusability: Components become modular and easier to use across projects.
• Testability: A clean layout simplifies writing and running tests.

The Standard Layout

Here’s a commonly used structure followed by many open-source and professional Python projects:

my_project/ 
├── my_project/            # Main source code package 
│   ├── __init__.py 
│   ├── module1.py 
│   ├── module2.py 
│   └── ... 
├── tests/                 # Test suite 
│   ├── __init__.py 
│   ├── test_module1.py 
│   └── ... 
├── scripts/               # Standalone scripts or CLI tools 
├── docs/                  # Documentation 
├── .gitignore 
├── pyproject.toml         # Modern project config 
├── requirements.txt       # Dependency list (optional if using Poetry/pip-tools) 
├── README.md              # Project overview 
└── setup.cfg/setup.py     # Packaging metadata (for legacy setups)

1. Use a Top-Level Package

Name your project and your main code package the same:

my_project/ 
└── my_project/

This avoids namespace collisions and ensures your package imports cleanly. Inside, you can further split your code into submodules if it starts to grow.

2. Isolate Tests

Keep all tests in their own directory. Use the same module structure as your main code so it’s easy to map tests to implementation:

tests/ 
└── test_module1.py  →  tests my_project/module1.py

Use pytest or unittest, and keep your test dependencies in requirements-dev.txt or in the pyproject.toml extras section.

3. Include a README

Your README.md should explain:

• What the project does
• How to install it
• How to use it
• How to contribute (optional)

A good README is often the difference between someone using your library or clicking away.

4. Choose a Build System (Poetry or Setuptools)

For modern Python development, Poetry is the recommended tool. It handles dependencies, packaging, and virtual environments seamlessly.

With Poetry, your project will include:

pyproject.toml   # Central configuration file 
poetry.lock      # Lock file for reproducibility

No more juggling multiple config files or remembering pip install commands!

5. Keep Configuration Out of Code

Use .env files or external config files for environment-specific settings. Libraries like python-dotenv, dynaconf, or pydantic-settings can help manage this gracefully.

Avoid hardcoding anything that might differ between dev/staging/production.

6. Follow PEP8 and Use Linters

Adhere to PEP8 coding standards. Use tools like:

• Black — auto formats your code
• Flake8 — finds style issues
• isort — sorts your imports
• mypy — type checking (if using type hints)

Automate these checks via a pre-commit hook or CI workflow.

7. Write Tests and Use CI/CD

Testing is not optional for professional projects. Write unit tests, use mocks when needed, and aim for high code coverage.

Set up GitHub Actions, Travis CI, or GitLab CI to run tests automatically on every push.

8. Version Your Code Properly

Use Semantic Versioning (SemVer) for your package:

MAJOR.MINOR.PATCH

Tag your releases with git:

git tag v1.0.0 
git push origin v1.0.0

9. Add Type Hints and Docstrings

Python 3 supports type hints, and they make your code easier to understand and safer to refactor.

def greet(name: str, age: int) -> str: 
    return f"Hello, {name}! You are {age} years old." 
 
# Calling the function 
message = greet("Aashish", 25) 
print(message)

Use docstrings ("""...""") to explain what functions do, especially for public methods or libraries others will use.

def add(a: int, b: int) -> int: 
    """ 
    Add two integers and return the result. 
 
    Parameters: 
    a (int): The first integer. 
    b (int): The second integer. 
 
    Returns: 
    int: The sum of the two integers. 
    """ 
    return a + b 
 
# Usage 
result = add(5, 3) 
print(result)  # Output: 8

10. Document Everything

Great code is readable code, but great projects have documentation.

Use tools like Sphinx or MkDocs to generate documentation from your code and docstrings.

Conclusion

There’s no one perfect way to structure every Python project. But following these best practices will make your codebase clean, manageable, and ready for real-world use.

Whether you’re hacking on a side project or maintaining enterprise code, thoughtful structure pays off in the long run.

TL;DR Checklist

• Use a top-level package
• Keep tests in a separate tests/ directory
• Use pyproject.toml with Poetry
• Avoid hardcoded configs
• Lint and format your code
• Write tests and set up CI
• Version your code properly
• Add type hints and docstrings
• Document your project

Liked this article? Follow me for more Python tips, real-world dev guides, and behind-the-scenes content on writing clean, scalable code.

Have a cool project or tip? Drop it in the comments — I’d love to hear how you structure your Python projects!