If you use Pylance in VS Code for its completions, navigation, or GitHub Copilot integration, you can now use Pyrefly as its type checking backend. We have been working with the Microsoft Python team to make this possible. The result is a new diagnostics source option in Pylance that uses Pyrefly instead of Pyright to power the type analysis backend, keeping the Pylance experience intact.
Pyrefly v1.4 is out. Highlights:
A question that has been on our minds lately is how to build a community around Pyrefly when AI is rapidly reshaping longstanding open source norms. I don't think anyone has fully figured this out yet, including us, but we're making some changes to Pyrefly's contributing guidelines that we think are a good start. Here's what we've seen change in open source and what we're doing about it.
We've just released Pyrefly v1.3, which adds flexible tools for configuring, suppressing, and baselining errors. Also included: a composable tensor shape DSL that expands experimental tensor shape support for JAX and NumPy, improved DataFrame schema checking for Polars, and a battery of new type checking diagnostics.
Pyrefly is one of the major new Python type-checkers taking the world by storm. It's often touted for its speed, usability, and typing-spec-conformance. But it also offers an extra nifty little feature you may never have heard of: type-coverage.
Let's learn about what it is, why you might want to care about it, and what it can do for you.
Pyrefly v1.1 is now available 🎉 This is our first minor release since reaching stable v1.0 in May, and it brings improvements across the board: better performance, smarter type checking, and new IDE refactoring tools.
Mypy, Pyrefly, Pyright, ty, Zuban, and possibly more that will come in the future... how are library maintainers expected to cope?
TL;DR: Prioritise running as many type-checkers as possible on your test suite. Run at least one on your source code.
Today we are pleased to share that Pyrefly, our open source type checker and language server for Python, has reached stable version 1 status, meaning we are confident that Pyrefly is ready for production use.
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:
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.