Google’s Python Style Guide for Data Scientists

Most Data Scientists have learned to code well on the job. Indeed, higher education generally focuses more on theory (mathematics, statistics, etc.) rather than software engineering, deployment, etc.

I don't know if it's a good or bad choice, the attraction to theory is probably also a bit cultural, and it's also what has produced some exceptional profiles that shine internationally along the way.

In any case, for the rest, the mere mortals who have to improve the quality of their code on a daily basis, Google has made available their best practices and their development style in Python (apparently, it's also a subject for them). We're talking about style because it's primarily a matter of preferences, and also because Python has its own usages and "tacit conventions", we're talking about coding in a "Pythonic" way.

This is a useful document for anyone who wants to improve in this area, especially since the choices are argued. This is important because sometimes certain recommendations or "Craftsman" rules can seem a bit sectarian :)

Link to the document:
https://google.github.io/styleguide/pyguide.html