Ask HN: How do you handle/maintain local Python environments?
Ask HN: How do you handle/maintain local Python environments?
I'm having some trouble figuring out how to handle my local Python. I'm not asking about 2 vs 3 - that ship has sailed - I'm confused on which binary to be using. From the way I see it, there's at least 4 different Pythons I could be using: 1 - Python shipped with OS X/Ubuntu 2 - brew/apt install python 3 - Anaconda 4 - Getting Python from https://ift.tt/1fWWeAB And that's before getting into how you get numpy et al installed. What's the general consensus on which to use? It seems like the OS X default is compiled with Clang while brew's version is with GCC. I've been working through this book [1] and found this thread [2]. I really want to make sure I'm using fast/optimized linear algebra libraries, is there an easy way to make sure? I use Python for learning data science/bioinformatics, learning MicroPython for embedded, and general automation stuff - is it possible to have one environment that performs well for all of these? [1] https://ift.tt/2hnYyVI [2] https://ift.tt/1WyLqLe
I'm having some trouble figuring out how to handle my local Python. I'm not asking about 2 vs 3 - that ship has sailed - I'm confused on which binary to be using. From the way I see it, there's at least 4 different Pythons I could be using: 1 - Python shipped with OS X/Ubuntu 2 - brew/apt install python 3 - Anaconda 4 - Getting Python from https://ift.tt/1fWWeAB And that's before getting into how you get numpy et al installed. What's the general consensus on which to use? It seems like the OS X default is compiled with Clang while brew's version is with GCC. I've been working through this book [1] and found this thread [2]. I really want to make sure I'm using fast/optimized linear algebra libraries, is there an easy way to make sure? I use Python for learning data science/bioinformatics, learning MicroPython for embedded, and general automation stuff - is it possible to have one environment that performs well for all of these? [1] https://ift.tt/2hnYyVI [2] https://ift.tt/1WyLqLe
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