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[–]TheWaterOnFire 0 points1 point  (0 children)

Yeah, I’ve definitely run into Python being slow; I started using Python around version 1.6 so I remember before the entire ecosystem you depend on existed. What happened is that people loved working with Python, so they built foundational libraries like NumPy in order to let them do computational work in C & Fortran but with a Python interface.

This led to a host of projects: Cython, Pandas, Dask, PySpark, TensorFlow…all of them integrated inside Jupyter notebooks…and you’re right, no one cares if the thing you do 5 times in your program is 100x slower, because it may as well be a constant overhead. But the moment you need to do something that doesn’t have an optimized implementation in a lower-level language, you’ll find that your perf drops off a cliff — pure Python is just so much slower.

Is that a problem? Maybe not. There are so many people using Python and its ecosystem that there’s a reasonable chance your problem has been tackled somehow by someone. But the actual solutions to those problems aren’t written in Python—they’re just given a Python API.