Linklog

Error handling is one of the most important aspect of writing good code which is why it's also a difficult thing to do well. This article makes a great case for handling errors defending on what the caller can do, instead of by where it was made. It provides concrete examples which really illustrate the point well.

Let's face it, logging is tough. This piece reframes the way to think about log events, not as small messages but as wide events enriched with context. The tail sampling they present is also an excellent practice.

A very interesting article on the Decimal type in the finance industry. I also always heard the maxim "don't use floats for money", but like everyone else there are parts of my code which do use them for money. This article is a fantastic reminder that it's good to measure your risk yourself: are the rounding errors ever going to be meaningful enough to warrant the complexity of a pure Python type?

I've been trying out devcontainers recently, and this article really helped understanding exactly how useful they can be.

This article brings forth an interesting aspect of documentation: making it "load bearing" ensures it is maintained more regularly.

Some musings on the importance of good naming conventions in programming.

An interesting take on the rigidity of best practices and how much more productive we can be once we let go of them.

I'm not a great SQL user, I have experience (mainly from database management in web development and now as a data scientist) but I don't consider myself an SQL wizard. This list of opinionated "tips" was quite useful to me.

A series of examples of proper software design in data science beyond Jupyter notebooks. Very good examples of proper refactoring, step by step, from a messy script to a properly encapsulated program.

A short list of best practices. Some are obvious from a software development perspective (VCS, package manager, linter), but some others have some good recommendations on ML specific tools (Hydra for configs, Optuna for hyperparameter tuning).

Series of articles on best practices around Python coding and tooling. Definitely worth checking it out if you're still building your workflow.

A set of best-practices in Python development. Given the permissiveness of Python in terms of syntax and design, I find that following community accepted best practices is the best way to learn how to write good code too.

An interesting and opinionated take on SQL formatting. You might not be able to impose it at work, but you can always try!

An interesting explanation of implicit data contracts through naming conventions.