> 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

> 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. I understand the sentiment, but in this case I wonder if it is less a python issue and more an issue with numpy. I don't see any reason why numpy couldn't execute the last line of the following multi-threaded: >>> import numpy as np >>> a = np.arange(1000000000) >>> b = 2 \* a The same thing applies to matrix multiplication. I'm guessing numpy has chosen not to for good reason though. edit: Actually it seems numpy is multi-threaded under certain circumstances: https://stackoverflow.com/questions/16617973/why-isnt-numpy-mean-multithreaded https://stackoverflow.com/questions/16617973/why-isnt-numpy-...