Parse, Don't Validate (2019)

This principle is how pydantic[0] utterly revolutionized my python development experience. I went from constantly having to test functions in repls, writing tons of validation boilerplate, and still getting TypeErrors and NoneTypeErrors and AttributeErrors left and right to like...just writing code. And it working! Like one time I wrote a few hundred lines of python over the course of a day and then just ran it... and it worked. I just sat there shocked, waiting for the inevitable crash and traceback to dive in and fix something, but it never came. In Python! Incredible.

[0] https://pydantic-docs.helpmanual.io/

I've found this to be simply a matter of experience, not tooling. As the years go by I find the majority of my code just working right - never touched anything like pydantic or validation boilerplate for my own code, besides having to write unit tests as an afterthought at work to keep the coverage metric up.

Man, for a dev with as much experience as you’re claiming to have, this comment ain’t a great look.

I’d argue that the more experience you get the more you write code for other people which involves adding lots of tooling, tests, etc. Even if the code works the first time, a more senior dev will make sure others have a “pit of success” they can fall into. This involves a lot more than just some “unit tests as an afterthought to keep the coverage up.”

Adding lots, no. I agree with the grandparent.

Keeping the code simple, finding the right abstractions, untangling coupling, gets the most bang for the buck. See the “beyond pep8” talk for a enlightened perspective.

That said, lightweight testing and tools like pyflakes to prevent egregious errors helps an experienced dev write very productively. Typing helps the most with large, venerable projects with numerous devs of differing experience levels.

> Typing helps the most with large, venerable projects

I disagree. I've started using types from the ground up and it helps almost equally at every stage of the game. Also I aggressively rely on autocomplete for methods. It's faster this way than usual "dynamic" or "pythonic" python.

Part of it might be exactly because writing my datatypes first helps me think about the right abstractions.

The big win with python is maybe 2-10% of functions, I just want to punt and use a dict. But I have shifted >80% of what used to be dicts to Models/dataclasses and it's so much faster to write and easier to debug.