Quick Start: VS Code Python Development Environment Setup Guide (2025 Edition)
This article provides a detailed demonstration of the complete process for building an efficient Python development environment using `uv`, `Ruff`, and `Ty`. First, it covers managing virtual environments and dependencies with `uv`, leveraging its high-performance characteristics to quickly install libraries such as `numpy`, `pandas`, and `Streamlit`. It also demonstrates how to configure domestic mirror sources to accelerate the download of large packages. Next, it delves into configuring VS Code with the `Ruff` plugin to automatically format code, clean up unused imports, and auto-fix syntax issues upon saving, significantly enhancing code cleanliness and development efficiency. Following this, the `Ty` tool is introduced for static type checking, complementing `Ruff`'s limitations in type safety by detecting potential type errors before code execution, with real-time diagnostics enabled via a VS Code plugin. Finally, the article compares the applicable scenarios of `Anaconda` and `uv`, recommending that beginners or research collaboration environments continue using `Anaconda`, while enterprise-level developers or advanced users pursuing ultimate efficiency and code quality should prioritize the combination of `uv`, `Ruff`, and `Ty` to achieve a more concise, fast, and standardized Python development experience.