for most "normal" uses, you'd probably be fine with having your code written in numpy or pandas
In my experience this is the case. Occasionally though, there might be an bottleneck, and Numba can be a good way to handle it. E.g. when implementing seam carving, I found normal Python / NumPy too slow, and same with Naive Bayes, so used Numba. (Why was I implementing these, instead of using a library? As an exercise. But I guess the takeaway is that if you want to do something more intensive, and are implementing it yourself rather than using a library, Numba can be a good option).
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There's an example here of using Numba to speed up data science code, (disclaimer, I'm the author): https://sidsite.com/posts/python-corrset-optimization/
In my experience this is the case. Occasionally though, there might be an bottleneck, and Numba can be a good way to handle it. E.g. when implementing seam carving, I found normal Python / NumPy too slow, and same with Naive Bayes, so used Numba. (Why was I implementing these, instead of using a library? As an exercise. But I guess the takeaway is that if you want to do something more intensive, and are implementing it yourself rather than using a library, Numba can be a good option).