taskipy VS Python Fire

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

The cli tool [fire](https://github.com/google/python-fire/blob/master/docs/guide...) has a nifty feature where it can generate a cli for any file for you.

So random and math are somewhat usable that way

    $ python -m fire random uniform 0 1

With python-fire you can use either function or class to create your subcommands. But I find working with classes more intuitive and manageable. Our first command is going to be a sub-command that shows us the UTC time.

Have you checked out fire? Personally, I think it's a really elegant solution to turning a callable object into command line. Plus, the chaining function calls feature lets you build some pretty complex command line patterns likes you never seen with other frameworks. Definitely worth giving it a try!

I started with click but found python fire to be so much easier to use.

I highly recommend checking out fire for adding a CLI quickly to little utility scripts that aren't going to be published to the world but just for you.

python fire autogenerates CLI wrappers for python modules, which really synergizes with method-chaining APIs like pandas.

Fire can basically do the first step (object -> CLI):

https://github.com/google/python-fire

Gooey can do (CLI -> GUI):

https://github.com/chriskiehl/Gooey

also, while we're on the subject, fire may not be the same kind of workhorse as argparse or click, but for really simple stuff it's pretty awesome

I'm relatively new to Go (coming from Python) so I haven't been using Cobra (or Go, for that matter) for long but it's clearly very polished -- only friction I was experiencing with it is there's a lot of boilerplate to creating commands and subcommands, that IMO (idea as proven by python-fire) can be naturally (better) expressed as types / fields / methods that are already built into the language.