Mastering Python Project Structure
A Comprehensive Guide
TL;DR; The article “Mastering Python Project Structure: A Comprehensive Guide” provides an in-depth guide on structuring Python projects. It covers everything from setting up a sample project structure (tutorial_ml) with clearly defined components like schemas and wrangler modules, to explaining advanced topics such as setup.cfg, pyproject.toml, and Pydantic. Additionally, it discusses Python project setup, installation processes, manual and automated testing with pytest, and quality assurance with pre-commit hooks. The guide also integrates GitHub Actions for CI/CD, and explores containerizing with Docker for consistent development environments.
In this article, we dive into the essentials of structuring a Python project. We’ll cover the basics of package creation and delve into advanced topics such as setup.cfg, pyproject.toml Pydantic and others. Our goal is to establish a solid foundation for structuring your Python projects effectively.
This comprehensive guide is designed for Python developers looking to master the art of project structuring. It walks you through the essential components and best practices for organizing a Python project, ensuring readability, maintainability, and ease of use. Starting with an example project called tutorial_ml, the guide explains its structure, including the src directory, schemas, and wrangler modules. Key configuration files like setup.cfg and pyproject.toml are demystified, and the role of Pydantic in data validation is highlighted. The guide also delves into practical aspects such as setting up the development environment, deploying the package, and ensuring code quality through manual and automated testing, including pytest integration. It emphasizes the importance of pre-commit hooks for maintaining code standards and explores the integration of GitHub Actions to automate workflows. Finally, it discusses the benefits of containerizing the Python project using Docker, creating a consistent and isolated development environment, and integrating tools like Jupyter Lab for interactive testing. This guide is an essential resource for Python developers aiming to elevate their project structure and management skills.
Summary
- Section 1. Project Structure and Functionalities
- Section 2. Python project setup, and installation
- Section 3. Testing and Experimenting with your package
- Section 4. Leveraging pre-commit for Code Quality Assurance
- Section 5: Integrating GitHub Actions with Pre-Commit and Pytest
- Section 6: Containerizing with Docker for an Integrated Development Environment
Explore how to structure your Python project for straightforward deployment of machine learning models by clicking the links above. If you’re new to this, I recommend progressing through each section for a clearer understanding. For those with advanced skills, feel free to jump to any section that interests you
Code base
Access the entire code on github.com/rfelixmg/tutorial_ml
Remember to fork and star if you like it!
