bambi VS taskipy

It is much less challenging with Bambi[1] and brms[2].

  [1] https://bambinos.github.io/bambi/

I’m trying to learn statistics. I’m up to implementing regressions in python using sci-kit learn.

I was playing around with Bayesian modelling last night with https://bambinos.github.io/bambi/ But I’m not really sure how to interpret the outputs.

Always open to reading about learning resources/books/videos/courses from others.

I would suggest using Python's bambi; it is based on PyMC and it is very straightforward to use. We simply define our priors argument as a dictionary (quite literally: my_priors = {"feature_1": bmb.Prior("Normal", mu=4, sigma=4), "feature_n": bmb.Prior("Normal", mu=0.4, sigma=0.4)}) when creating our Bambi Model object and we are ready to go. They have a lot of worked exampling in their website.

For those interested in Bayesian modeling in Python we also have Bambi https://github.com/bambinos/bambi

pdbpp: Improved pdb boltons: assorted python addtions twisted: event driven networking framework sorcery: Dark magic in python, things know where+how they are being called, helps reducing boilerplate sh: Better alternative for subprocess module, much more pythonic taskipy: npm run scipt_name like functionality snoop: pdb lite, record+replay function steps birdseye: graphical debugger remote-pdb: easy pdb from inside containers typer: wrapper around click for simpler code for CLIs arrow: Always TZ aware datetimes, plus more features more-itertools: more functions for iterators pydantic: data validation + dataclasses loguru: better logging notifiers: sending notifications from python

Taskipy

taskipy – complementary task runner for python

I always use Taskipy https://github.com/illBeRoy/taskipy to run tasks in my applications, works really well with Poetry so when I am running my dev Flask/FastAPI server and Celery or running my tests or format my code it's all there.

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In legacy (don't break anything) mode, there's still no reason to not switch. I export `requirements.txt` with poetry just for pip legacy reasons and it works great. If I just update some scripts, I could avoid it. It's running all the time in CI, it's exercised quite a bit.

What's wrong with just using pip and requirements.txt? There's no dev section. In addition, bumping deps is not the same. I have [a blog post](https://squarism.com/2021/09/10/sciencing-out-updates/) explaining semver updates to a python dev.

_my strong assertion:_ Python and Go missed it from the start. That's why it is so confusing. There's no other choice in Rust but Cargo. Rust devs are never confused on how to add a package, semver it. The answer is always Cargo. It's in the tutorial. It's in the book. It's in the culture.

I think I've heard that pip might support the pyproject spec, poetry already does. If you want scripts like npm, you can have that too with [taskipy](https://github.com/illBeRoy/taskipy). You don't have to.

taskipy