Learn how to normalize inconsistent data structures in Python with Pydantic. This post from Gabriel Côté-Carrier and Kyle Adams guides you through different approaches and pitfalls, using Pydantic's alias path and alias choices features. https://link.testdouble.com/f540d0

Python Type System and Tooling Survey 2025 #python

Python Type System and Tooling Survey 2025 #python

Welcome to this annual survey to better understand how Python developers use the type system, type checkers, and integrated development environments (IDEs). The goal of this survey is to gain insights into the tools and practices you use (if any!), the...

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You shouldn't have to choose: clean Python code or poorly-named external APIs. Pydantic field aliases let you have both: Pythonic variable names in your code, whatever naming scheme the API expects on the wire. No more manual field mapping or workarounds. https://link.testdouble.com/ddffe7 #Python

Today, we're announcing our first hosted infrastructure product: pyx, a Python-native package registry. We think of pyx as an optimized backend for uv: it’s a package registry, but it also solves problems that go beyond the scope of a traditional "package registry".

The problem was a large, unruly JSON document, the kind that likely used to be XML. Once you’d gone 4-5 levels deep, it was difficult to hold in your head 1) where you were in the structure, 2) what data was available at the current location, and 3) how to navigate to other pieces of data.

Test Double@testdouble.bsky.social · 11mo ago

Python type safety lightbulb moment: mypy = compile-time safety Pydantic = runtime safety Helped a client shift domain knowledge from heads → codebase with this combo. https://link.testdouble.com/769eb9 No more "wait, what does this field contain?" moments 🎯 #python #pydantic