This project serves as a definitive 2026 Reference Implementation for modern Python development. It moves beyond the legacy stack (pip, pandas, GIL-limited execution) to embrace the performance revolution defined by Rust-based tooling and Python 3.14's free-threading capabilities.
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🔭 Project Goal: Demonstrate a production-ready, high-speed Python environment.
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🌱 Tech Stack:
- Runtime: Python 3.14 (Free-threaded / NoGIL supported)
- Dependency Management:
uv(100x faster than pip) - Linting & Formatting:
Ruff(Instantaneous analysis) - API Framework:
FastAPI+Pydantic v2 - Data Processing:
Polars(Rust-backed, multi-core dataframes)
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📫 Contact: dwmom@hotmail.com
Gone are the days of sluggish pip installs and slow linting using flake8.
uv: We use Astral'suvfor package management. It resolves dependencies in milliseconds.ruff: A single tool replacing Black, Isort, and Flake8. Pre-configured inpyproject.toml.
- FastAPI: Asynchronous by default.
- Polars: Used instead of Pandas for data processing. It utilizes all CPU cores effectively, matching the 2026 parallel computing paradigm.
A multi-stage Dockerfile ensures the application is lightweight and secure, with a production-ready gunicorn + uvicorn setup hidden behind a clean interface.
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Clone the repository:
git clone https://github.com/RamonSouzaDev/python-best-practices.git cd python-best-practices -
Build and Run:
docker compose up --build
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Access the Application: Open http://localhost:8000 to see the Live Documentation & Demo.
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Install uv (if not installed):
pip install uv
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Sync Dependencies:
uv sync
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Run the App:
uv run uvicorn src.main:app --reload
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Run Tests:
uv run pytest
To run the full test suite and linting checks:
# Run Linter
uv run ruff check .
# Run Unit Tests
uv run pytestBuilt with ❤️ by Ramon Mendes for the IBM Interview Studies