I feel like the #1 downside of Python for the last few years is that you cannot take advantage of multiple cores of a CPU easily. Especially when you think it is heavily used in data analysis. We use Python for data analysis as well, and for 95% of operations we are doing, numpy is fast enough that we don't have any complaints. But sometimes, we do wish to be able to take advantage of all the cores in our CPUs, especially now that we can easily get an 8 core CPU for a reasonable price.
There is multiprocessing module but you cannot share memory between processes. I guess the best options are either writing a C extension or using Numba. Writing C extensions require either distributing binary packages or C compiler to be present on the target computer which is not always ideal. So is using Numba the best solution currently? I tried it a bit in the past but the errors I got was a bit hard to interpret compared to regular Python errors.
I wish the multithreading module had support for native threads. Is there any PEPs trying to bring easy multi-core support for Python?
There is multiprocessing module but you cannot share memory between processes. I guess the best options are either writing a C extension or using Numba. Writing C extensions require either distributing binary packages or C compiler to be present on the target computer which is not always ideal. So is using Numba the best solution currently? I tried it a bit in the past but the errors I got was a bit hard to interpret compared to regular Python errors.
I wish the multithreading module had support for native threads. Is there any PEPs trying to bring easy multi-core support for Python?