4 ms·
The original title emphasises that NumPy and Pandas now are functional on PyPy. The PyPy JIT cannot look inside C code, and crossing the python-c interface is
by mattip 9y ago
The original title emphasises that NumPy and Pandas now are functional on PyPy.
The PyPy JIT cannot look inside C code, and crossing the python-c interface is slow, but give it a chance and you may be pleasantly surprised how fast your pure python code can run.
- bischofs 9y agoSo do you think it is worth switching if you are using pandas and numpy heavily?
- fijal 9y agoIt depends: we are heavily invested in making both numpy and pandas faster, so next releases will improve that. That said, you are quite likely to find your program slower (but it really depends on what you do). It's a good time to experiment, but definitely not to fully switch just yet
- mattip 9y agoIf you have for loops there are opportunities for the JIT to speed them up. It very much depends how close those loops are to pure python, if they are than PyPy should be able to help. Many code constructs do look like large for loops. The JIT will trace them (which requires memory to record the code paths) and after about 1000 times through will convert the python AST to assembler (with checks to bail out if assumptions are violated). So the question you ask is very specific to your workflow, and is best answered by trying it out