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://www.python.org/downloads/
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://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793
[2] https://www.reddit.com/r/Python/comments/46r8u0/numpylinalgsolve_is_6x_faster_on_my_mac_than_on/
1 - Python shipped with OS X/Ubuntu
2 - brew/apt install python
3 - Anaconda
4 - Getting Python from https://www.python.org/downloads/
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://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793
[2] https://www.reddit.com/r/Python/comments/46r8u0/numpylinalgsolve_is_6x_faster_on_my_mac_than_on/