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Honestly, NumPy is gonna be hard to beat even for someone knowledgeable in certain use cases, especially ones where the overhead in Python is trumped by time sp

by gh02t 9y ago

Honestly, NumPy is gonna be hard to beat even for someone knowledgeable in certain use cases, especially ones where the overhead in Python is trumped by time spent in library calls. It's the same reason that it's hard to beat MATLAB or Mathematica in cases they are optimized for despite being relatively slow languages. They are calling some of the most heavily optimized libraries in existence (e.g., BLAS) and using heuristics to help choose the smartest evaluation strategy. Edit: More speed on the Python side is good though, because it gives you flexibility. Sometimes it's hard to figure out how to do stuff optimally in NumPy, versus just banging things out in a for loop. I've definitely done that when I wanted something to just work, versus spending an hour figuring out what arcane incantation I need to pass to np.einsum to get the operation I want.

luispedrocoelho 9y ago
"spending an hour figuring out what arcane incantation I need to pass to np.einsum to get the operation I want" Yes, I have also had this experience and I hate how in the end, the code is very hard to read, while the for loop was probably trivial.
- cjalmeida 9y ago
  Every time I thought I needed einsum or similar arcane ops, I found that a Numba optimization for loop did the job.
  - gh02t 9y ago
    Numba is nice, but it's another large dependency to pull in. If I can avoid it I will.
pletnes 9y ago
Sure, I’ve seen C++ experts beat by numpy, i.e BLAS/LAPACK. A lot of people don’t use BLAS from C++ either, which would improve the performance on that side, too.