Please don't break HN's guidelines by being snarky or putting down others' work in a shallow way. If you know more or have a different perspective to offer, try sharing some of what you know so we can all learn something!
Being in the data science community myself, I prefer straight venv + pip to conda. It’s simpler for me to manage errors. I only use conda when I have to.
Hi whalesalad, good to meet you! Now that you know me, you can never say that again anymore :-) Although, tbf, I only use conda for my machine learning related projects. I've tried using pip for that but was at risk of massive hair loss.
In the scientific community, there is a widespread "just use anaconda" message. Many people spend their entire lives inside anaconda, and equate it with Python.
1. Argument from authority doesn’t mean anything to me. I also don’t believe creating Django or running a Python consultancy endow someone with especially useful opinions of Python packaging tooling. (Not that the author isn’t knowledgeable, just you seem to think there’s an A implies B relationship between those two items and having good opinions about Python packaging, and there’s not).
2. Conda is quite widely used outside of data science. It’s for example part of Anaconda enterprise offerings used by huge banks, government agencies, universities, etc., on large projects often with no use cases related to data science. Conda itself has no logical connection with data science, it’s just a package & environment manager.
In each of my last 4 jobs, 2 at large Fortune 500 ecommerce companies, conda has been the environment manager used for all internal Python development. Still use pip a lot within conda envs, but conda is the one broader constant.
Giving a counterexample is not argument from authority. I did not respond to the parent comment to discuss any feature of conda, only to dispel the wrong claim that only mostly data science projects rely on it.