• Ignotum
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    287 days ago

    Every now and then a new hire comes along with a windows pc, every time they decide they want to try to get everything working on windows, after a week they give up.

    On linux it’s one pip install and you’re done

    • @Aceticon@lemmy.world
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      27 days ago

      Last time I checked, it was way easier in Windows to have a VM running Linux just for Python, than to get Python to reliably work nativelly in Windows.

    • @HappyRedditRefugee@lemm.ee
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      27 days ago

      We have a development system for python on Windows at work, works very well also.

      On linux is one pip install, buy maybe first do a venv^^

    • WalrusDragonOnABike [they/them]
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      77 days ago

      On linux it’s one pip install and you’re done

      Isn’t that how packages/dependencies work on windows as well? Once I got pip updated, I’ve never had any issues with it.

      • Ignotum
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        67 days ago

        No clue, all i know is that i never have to do more than that, and noone has managed to get it working on windows 🤷‍♂️

        When i started learning programming, everything was always a pain to set up, needed to install weird IDEs from shady websites and they only worked half the time. Then a friend showed me linux where stuff just worked out of the box, just slap some code in a textfile and compile it, i never looked back (was working in c/c++ but from what i’ve seen it’s not much better for python)

        • OsaErisXero
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          27 days ago

          Since some wsl features started coming with windows out of the box python has been pretty trivial to install. It’s a far cry from the conda/cygwin nightmare hell scape it used to be

      • Ephera
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        67 days ago

        I believe, it’s because various Python libraries ship with a pre-compiled C/C++/Rust library. That library needs to be compiled for a specific target, and you often only get Linux x86_64 on Pypi, because that’s what most library devs use themselves.

        Conda tries to solve that by providing a separate repository, where they do have builds for more targets available, but as a result, they have fewer libraries available in that repo. That’s why we needed to install some via Conda and some via Pipenv/Pypi.