How to Structure Python Projects Like a Professional Engineer

Messy folders. Circular imports. Files that keep growing. If that sounds familiar, your project structure needs a serious upgrade.

How to Structure Python Projects Like a Professional Engineer

Learn how to organize your Python projects the way professional engineers do — with scalable folder layouts, modular design, and best practices that keep your codebase clean and maintainable.

“Good code is its own best documentation.” — Steve McConnell

When you first start writing Python scripts, it’s tempting to dump everything into a single file. It runs. It works. Life is good.

But as your project grows — features stack up, complexity creeps in, bugs multiply — the “one-file wonder” becomes a nightmare to maintain.

This is where project structure steps in.

In this article, we’ll explore how to structure your Python projects like a professional engineer, using principles that scale — whether you’re building a CLI tool, REST API, ML pipeline, or a full-fledged application.

Why Project Structure Matters

Think of your codebase as a growing city:

Without structure: Roads tangle, houses stack on top of each other, and you can’t find the nearest hospital.

With structure: You have highways, zoning rules, and Google Maps.

A clean structure helps with:

Readability: Your future self and teammates will thank you.

Maintainability: It’s easier to debug and add features.

Testability: Good separation of concerns allows for easier unit testing.

Scalability: Modular code scales better as the project grows.

The Professional Python Project Blueprint

Let’s start with a simple directory layout for a Python package or application:

my_project/ 
├── my_project/ 
│   ├── __init__.py 
│   ├── core/ 
│   │   ├── __init__.py 
│   │   └── logic.py 
│   ├── services/ 
│   │   ├── __init__.py 
│   │   └── external_api.py 
│   ├── models/ 
│   │   └── user.py 
│   └── utils/ 
│       └── helpers.py 
├── tests/ 
│   ├── __init__.py 
│   └── test_logic.py 
├── scripts/ 
│   └── run_pipeline.py 
├── requirements.txt 
├── pyproject.toml or setup.py 
├── .env 
├── .gitignore 
└── README.md

Now, let’s break it down like a pro.

1. The Core Application (my_project/)

This is where your actual application lives — not at the root, but nested inside a folder with the same name.

Why? Because it separates your package code from top-level metadata (like README.md, requirements.txt, etc.). Tools like Pytest and setuptools also play nicer with this layout.

• core/: Contains business logic or the brain of your app.
• services/: External integrations (e.g., APIs, databases).
• models/: Data representations (like Pydantic models or ORM classes).
• utils/: Helper functions and utilities.

2. Testing (tests/)

“If it’s not tested, it’s broken.”

Always have a dedicated tests/ directory, mirroring the structure of your codebase. You can use pytest or unittest.

Example:

# tests/test_logic.py 
from my_project.core.logic import do_something 
 
def test_do_something(): 
    assert do_something() == 'expected'

Keep test dependencies in requirements-dev.txt or include them in pyproject.toml.

3. Scripts (scripts/)

These are entry points, not the main application logic.

Put CLI commands, maintenance scripts, and cron jobs here. Keep them lightweight — their job is to orchestrate, not implement.

Example:

# scripts/run_pipeline.py 
from my_project.core.pipeline import run 
 
if __name__ == '__main__': 
    run()

4. Dependencies

Use a clear, modern dependency management system:

• Use: pyproject.toml with Poetry, Hatch, or PDM.
• Avoid: Keeping everything in requirements.txt (unless you pin versions).

A pyproject.toml file keeps your builds, dependencies, and project metadata cleanly organized.

Example snippet:

[tool.poetry.dependencies] 
python = "^3.11" 
requests = "^2.31.0" 
 
[tool.poetry.dev-dependencies] 
pytest = "^7.4.2" 
black = "^24.3.0"

5. Code Quality & Tooling

Adopt these tools early on:

Black — Auto format your code.

Flake8 / Ruff — Linting.

isort — Sort imports.

Pre-commit — Hook tools into your Git workflow.

mypy — Optional static type checks.

These help you catch issues early and keep code consistent across teams.

6. Environment Management

Use .env files to manage secrets and environment-specific variables (use with python-dotenv or os.environ).

Structure:

.env 
.env.dev 
.env.prod

Make sure to add .env to .gitignore to avoid leaking secrets.

7. Documentation

A good README goes a long way.

Include:

• What the project does
• How to install and run it
• How to run tests
• Example usage

If it’s a serious project, consider using MkDocs or Sphinx for full documentation.

Documenting Python Projects with MkDocs — The Right Way to Showcase Your Code

Learn how to turn your Python project into a beautifully documented site using MkDocs — complete with themes, plugins…

Bonus: Cookiecutter Templates

Don’t start from scratch every time.

Use tools like cookiecutter to scaffold professional project templates. Example:

cookiecutter https://github.com/audreyfeldroy/cookiecutter-pypackage.git

Final Thoughts

Your project structure is not just about folders — it reflects how you think about code, scale, and collaboration.

Even if you’re working solo, building habits like:

Separating concerns

Writing tests

Documenting code

Using tools effectively

…will instantly level up your game and prepare you for real-world engineering environments.

TL;DR — Pro Structure Checklist

• Use a nested module (my_project/my_project/)
• Separate logic, services, models, and utils
• Write tests in a mirrored tests/ folder
• Use pyproject.toml for dependencies
• Include scripts in a scripts/ folder
• Manage secrets with .env files
• Format + lint code with pre-commit hooks
• Write a clear, helpful README

Over to You

Have you been structuring your Python projects like this? If not, start with your next script.

If you found this helpful, follow for more dev best practices and engineering insights. Happy coding! 🐍