Python Project Setup 2026: uv + Ruff + Ty + Polars

2025-07-17 · Source: KDnuggets · Field: Technology & Digital — Software Development & Engineering, Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Novice, medium

Summary

The article proposes a streamlined Python project setup for 2026, consolidating multiple tools into a coherent stack for enhanced speed and consistency. This modern default stack comprises `uv` for Python installation, environment management, and dependency handling; `Ruff` for linting and formatting; `Ty` for type checking; and `Polars` for efficient dataframe operations. Three of these tools (`uv`, `Ruff`, `Ty`) are from Astral, ensuring seamless integration and configuration via a single `pyproject.toml` file. This approach replaces older, fragmented setups involving tools like `pyenv`, `pip`, `venv`, `Black`, `isort`, `Flake8`, `mypy`, and `pandas`, reducing overhead and simplifying CI/CD workflows. The setup process is detailed, including installation of `uv`, project scaffolding, dependency management, and configuration for `Ruff`, `Ty`, and `pytest` within `pyproject.toml`.

Key takeaway

For Machine Learning Engineers or Data Scientists initiating new Python projects, adopting the `uv`, `Ruff`, `Ty`, and `Polars` stack can significantly reduce setup complexity and improve development workflow efficiency. This integrated approach minimizes tool sprawl and configuration headaches, allowing you to focus on code rather than environment management. Consider migrating to this stack to benefit from faster execution, consistent environments, and streamlined code quality checks, especially if your current setup involves multiple disparate tools.

Key insights

A consolidated Python stack with `uv`, `Ruff`, `Ty`, and `Polars` simplifies project setup and improves efficiency.

Principles

• Centralize configuration in `pyproject.toml`.
• Prefer lazy execution for data processing.
• Automate environment management with `uv`.

Method

Install `uv`, scaffold a project, add dependencies with `uv add`, configure `Ruff`, `Ty`, and `pytest` in `pyproject.toml`, then use `uv run` for all tool execution.

In practice

• Use `uv` to manage Python versions and dependencies.
• Configure `Ruff` for linting and formatting.
• Adopt `Polars` for high-performance dataframes.

Topics

• Python Project Setup
• uv Tool
• Ruff Linter
• Ty Type Checker
• Polars Dataframes

Code references

• microsoft/pyright
• PyCQA/flake8-bugbear
• asottile/pyupgrade

Best for: Machine Learning Engineer, Data Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.