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I think this is a bit much. Pip takes almost no time to set up and use. It ships with MacOS in fact.

As long as you only have pure python dependencies, pip is fine. Unfortunately, that's very uncommon for DS/ML python at least.

pip install numpy

pip install pandas

??

I'm really really tired of explaining this, tbh.

The trouble is firstly that pip will happily install conflicting versions of numpy which breaks your code.

This occurs because pip didn't check dependencies until last year, which is one of the reasons it's a terrible package manager.

Now it just gets stuck spinning it's wheels for ages and then errors out, which is better but still not good.

Conda, while really slow actually handles c level dependencies which makes it usable.

> I'm really really tired of explaining this, tbh.

So don't!

Fair.

I consider it a public service to keep mentioning this though, so that in future jobs, I don't have to explain over and over again why I don't want to use pip to "manage" our dependencies.

My experience with stuff with C-extensions (although not specifically ML packages) is that there is almost always a wheel published for them these days, so pip works just fine and I almost never have to think about if something is pure python or not.

I wish I could say I never had to think about it, though.

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