4 posts tagged with "news"

At time of writing, pandas is one of the most widely used Python libraries. It is downloaded about half-a-billion times per month from PyPI, is supported by nearly all Python data science packages, and is generally required learning in data science curriculums. Despite modern alternatives existing, pandas' impact cannot be minimised or understated.

In order to improve the developer experience for pandas' users across the ecosystem, we at Quansight Labs (with support from the Pyrefly team at Meta) decided to focus on improving pandas' typing. Why? Because better type hints mean:

• More accurate and useful auto-completions from VSCode / PyCharm / NeoVIM / Positron / other IDEs.
• More robust pipelines, as some categories of bugs can be caught without even needing to execute your code.

By supporting the pandas community, pandas' public API is now type-complete (as measured by Pyright), up from 47% when we started the effort last year. We'll tell the story of how it happened - but first, we need to talk more about type completeness, and how we measure it.

Today we’re thrilled to announce that we have reached Beta status for Pyrefly, our open-source, high-performance tool for Python code navigation and type checking! We first announced Pyrefly back in April 2025, and thanks to incredible community feedback and a ton of work from our team, we’ve hit a significant milestone.

Because NumPy is one of the most downloaded packages in the Python ecosystem, any incremental improvement can have a large impact on the data science ecosystem. In particular, improvements related to static typing can improve developer experience and help downstream libraries write safer code. We'll tell the story about how we (Quansight Labs, with support from Meta's Pyrefly team) helped bring its type-completeness score to nearly 90% from an initial 33%.

Today we are announcing an alpha version of Pyrefly, an open source Python typechecker and IDE extension crafted in Rust. Pyrefly is a static type checker that analyzes Python code to ensure type consistency and help you catch errors throughout your codebase before your code runs. It also supports IDE integration and CLI usage to give you flexibility in how you incorporate it into your workflow.