I started a new project in Python in late 2019. Coming from a C++/Java background I elected from the start to use type checking and data classes heavily. The p

I started a new project in Python in late 2019. Coming from a C++/Java background I elected from the start to use type checking and data classes heavily. The project has now scaled to multiple developers and many thousands of lines of code. If I compare my experiences this time around with past experiences in Python, I feel those early choices are paying big dividends in terms of code readability and correctness. In particular: The ergonomics of data classes is great (IMO). They avoid a lot of boiler plate, integrate well with type checking and auto completion, and provide clear context to anyone reading the code. I’ve found Mypy a bit flaky (sometimes it misses errors it seemingly should catch) and some things that cannot be expressed in the type system to be pain points but overall having type annotations present and enforced helps document code, works well with IDE auto completion, and occasionally picks up errors that would have been otherwise missed. In particular I’ve found it’s picked up a lot of errors around missing None checks (nullability). These seem to be particularly easy to miss in unit tests. I’ve also found that PyCharm’s built in code code checking has often been able to pick up and highlight problems which it would have otherwise missed thanks to having type annotations. Having early feedback on problems is nice productivity boost. One hint if you do use Mypy make sure to enable check-untuned-defs. You’ll get much better coverage.