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Kim Jong-un: fragile and under pressure but he won’t give up

The Observer The North Korean leader’s surprise freeze of his nuclear programme is less a genuine move than a diplomatic manoeuvre,...

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Monday, 23 September 2019

New top story on Hacker News: Ask HN: How do you handle/maintain local Python environments?

Ask HN: How do you handle/maintain local Python environments?
7 by PascLeRasc | 5 comments on Hacker News.
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/

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