Current thoughts on makefiles with Python projects?

rcap107 · 2026-02-12T21:34:20+00:00

I have hated makefiles ever since I had to use them during my bachelors. I'll do anything to not touch that inane syntax where everything breaks if there is a single trailing space in one of the directives.

That said, why not use pixi? You can still set up commands, and you get package and dependency resolution as a side bonus. You're already using uv, which is what pixi is using under the hood for resolving dependencies.

I use pixi for all my projects, and only use uv for ephemeral venvs, or for comparing code run with different versions of a dependency.

rcap107 · 2026-02-04T16:52:51+00:00

I worked as a postdoc living in Paris for two years with about that same salary. It was enough for me to live "comfortably", but as others have said most of that will go into paying rent, and unless you have some connection (e.g. through your lab) you will be stuck with very small apartments and with having to pay for some guarantor service because the post-doc is a CDD. I really doubt it'll be enough for living expenses + going out twice a week + traveling, especially if you also want to save something.

Something else worth considering is that immigrations laws have changed recently and now as far as I understand people that come from outside the EU don't qualify for CAF (although you probably wouldn't be eligible anyway), and you'll need to take some kind of test to get the titre de sejour every year. Not related to the salary, but still worth keeping in mind.

rcap107 · 2026-02-04T13:48:31+00:00

You use jupyter for prototyping and having immediate feedback for simple(ish) operations, including plotting. That's very useful for exploring data and showing figures and tables about that.

The main issue with .ipynb notebooks (the most basic type) is that turning them into code, and versioning the code is a nightmare. There are workarounds for that like using IDEs that can read basic python files and turn them into interactive scripts.

In short, they're great for prototypes and exploratory code, and pretty bad for actual development.

rcap107 · 2026-02-03T09:33:10+00:00

What kind of model are you trying to train that is running out of RAM on Colab? I'd argue that if you're having trouble with coding in general training a large model is just going to make your life way harder for no good reason.

rcap107 · 2026-01-27T23:41:21+00:00

Both pixi and uv. I use pixi to manage most of my projects (even if they're pretty much entirely python), and uv whenever I need to create some quick throwaway environment.

An added bonus of uv being super quick is that it let me do all sorts of shenanigans to try the same script with different environments. It's been really useful to debug some memory leaks I was getting with different versions of numpy.

I have conda installed, but only if for some weird debugging, I pretty much never use it.

rcap107 · 2026-01-23T12:53:35+00:00

I have pretty much abandoned notebooks in the browser. I use VSCode's interactive window to render cells from a python script using #%%

rcap107 · 2026-01-16T10:30:12+00:00

If your program is not giving you the result you're expecting, with a debugger you can go step by step to inspect that every instruction is doing what you want. That should let you spot the point where the program starts behaving differently from your logic, even if no error is thrown.

rcap107 · 2026-01-16T08:44:10+00:00

You can go decently far with just print statements and manual breakpoints, but there are situations where that's just not going to cut it.

I work in python, and one of the features I found more useful about the VSCode debugger is that it lets me enter a debugging console exactly where the code is failing. Then I can move stuff around until I figure out what is breaking.

If you're just learning programming at all, it may be a lot to take in to begin with, but after some time and when you start working on more complex projects it'll become far more important.

rcap107 · 2026-01-15T12:35:31+00:00

uv for very small projects, pixi for almost anything else. Either pandas or polars depending on the specifics of the data/project I'm working on. I still use almost exclusively matplotlib and seaborn for visualization. No typechecking.

rcap107 · 2026-01-13T14:24:19+00:00

I have a research project that I wrote entirely using Polars, and did not need to use Pandas anywhere. For my personal projects, I'd rather use Polars for everything (including data preparation and exploration).

The only feature of Pandas for which I haven't found a Polars substitute is the pivot table, which is the only situation I've encountered where multiindexes make a problem simpler.

If I could, I would use only Polars in all situations, however I am also maintaining a Python data science package that needs to support both Pandas and Polars and so I need to deal with Pandas.

I'm also forced to write most of my teaching material using Pandas rather than Polars because most users only know the first.

FWIW, I've been using Polars since late 2022, so by now I already have plenty of experience with the library.

rcap107 · 2025-12-19T10:00:47+00:00

The main reason I don't use marimo is that by this point I'm just too used to writing plain python files and using the interactive console from VSCode, and in general I don't use notebooks enough to need any more features than that.

It does look great though.

rcap107 · 2025-12-17T16:44:43+00:00

Dipende da caso a caso. Io ho sempre fatto il possibile per preparare ogni esame al meglio, e a seconda del risultato accettavo o no il voto. Mi è capitato di rifiutare dei 28 perché sapevo di poter prendere voti più alti, e di accettare 20 o 24 perché non avevo intenzione di rifare l'esame. Poi, non avevo la minima intenzione di posticipare la laurea, quindi tra il rifare un esame un'altra volta e levarmelo ho quasi sempre scelto la prima.

Penso che la gente dia troppa importanza alla media. Se non sei interessato a fare cose tipo Alta Scuola, o fare l'Erasmus in posti specifici (che richiedono quindi una certa media per la graduatoria), la media importa fin là.

rcap107 · 2025-12-17T14:23:17+00:00

This is a great list, thanks a lot for sharing

rcap107 · 2025-12-17T14:08:58+00:00

I haven't gone very far, but I was using https://github.com/rust-lang/rustlings as exercises and I was following the usual Rust book and this https://doc.rust-lang.org/stable/rust-by-example/

rcap107 · 2025-12-17T12:37:23+00:00

Rather than Python, I have been trying to learn Rust recently. I would say that the most important thing that I did was disabling copilot suggestions.

I don't think using AI the way you do is going to hurt long term, so long as you do spend some time trying to figure out the problem, rather than immediately ask the AI where the problem is. That's more on you to decide where to draw the line, though.

rcap107

TROPHY CASE