[akbo]

I see where you're coming from.

My use case is this: as a data scientist, I start new code bases all the time. Each project, simple experiment, data analysis, etc. needs its own cleanly separated dependency environment so I don't end up in dependency hell (I have 12 conda environments on my machine right now). Conda allows me to handle these environments with ease (one tool and a handful of commands -> as detailed in the article). With conda, I also have my data science Python cleanly separated from my system Python.

Of course there are other tools that can handle this use case. But pip alone won't do the trick. I don't like to have three separate tools for this (pip + venv + pyenv).

When I put something into production, I naturally want to keep using my conda environment.yml and have the same environment in dev and prod instead of switching to pip + requirements.txt, which might introduce inconsistencies.