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Still, people leave a lot of performance on the table when using Numpy in sub-optimal ways or when the problem just doesn't translate neatly to Numpy.

It's tempting to get lazy and just use a for-loop to iterate over an array sometimes and that will absolutely kill your performance.

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There is often no real value in optimizing such code, if the computation finishes in a time that doesn’t mess with your workflow. Spending more time on it will often just take time away from something more valuable to the research.

Ahh yes, that's a good point. If you're, for example working in a Jupyter Notebook, it absolutely doesn't matter if a cell needs 3 seconds or 3 milliseconds to execute.

Frequently because those performance gains aren't actually needed. We live in an age where you can cheaply and quickly scale the hardware for 99% of tasks. Tasks that are too expensive to compute inefficiently are also unlikely to be profitable enough to be doing at all.

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