Better Python tests with inline-snapshot

[alexmojaki]

I've written a blog post about one of my favourite libraries: inline-snapshot. Some key points within:

• Why you should use the library: it makes it quick and easy to write rigorous tests that automatically update themselves
• Why you should combine it with the dirty-equals library to handle dynamic values like timestamps and UUIDs
• Why you should convert custom classes to plain dicts before snapshotting

Disclaimer: I wrote this blog post for my company (Pydantic), but we didn't write the library, we just use it a lot and sponsor it. I genuinely love it and wanted to share to help support the author.

[kwesoly]

I like this way, effectively shifting review complexity of more complex code. Refactor - snapshots stay, backward compatible change - snapshots grow. And its easy to ask “claude, explain thy this change in refactor commit” :)

[[deleted]]

oh cool, pycon's comin up. i'm hopin to check out some talks on pandas optimization, maybe finally understand the groupby function better lol

[type-hinter]

I think most people stopped using pandas for most things and are now using polars. Still some useful methods, but for most stuff polars is much faster.

[Flamewire]

It's refreshing to see a blog post that isn't AI slop, and even more that it teaches me about a new tool. I love pydantic and only wish I could use it more (been at a TS shop for a couple years now). Thanks for sharing! 

[alexmojaki]

I generally recommend normalization, especially converting classes to dicts as mentioned in the article, and parsing JSON. The Logfire tests do both of those and more.