I'm curious about the use of JIT compilation with Numba in the Python community. How prevalent is it among seasoned Python developers? Are there specific areas where it tends to excel, and are there limitations where it might not be suitable? Also, do many developers opt for other optimization tools for their projects?
2 Answers
I actually use Numba nearly every day for processing scientific data—especially when dealing with huge datasets. Porting to C++ isn’t really an option in my case since the whole system runs in Python. Numba really helps when I need to process hundreds of millions of rows; it definitely outperforms regular NumPy in those scenarios.
Yeah, I've definitely used Numba to speed up performance in computation-heavy scenarios. Even after trying to optimize with NumPy's fancy stuff like ufuncs and broadcasting, Numba provided a noticeable speed boost for numerical bottlenecks. Just remember, it’s not a one-size-fits-all solution—only use it where it makes sense.
True, you could always write C++ modules and create a wrapper for Python, but if Numba does the job well enough, why complicate things? Trust me, managing C++ modules and their Python bindings isn’t something I'd recommend unless absolutely necessary.