Re:CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster tha...
by coliveira on Sunday October 11, 2020 @05:16PM
(#24749179)
The argument is simple: Python is by design made so that if you want things to go fast then you need to implement it in C. This creates a constant need to use specialized libraries (such as pandas), where in other languages they can simply do a better job in optimizing the standard library.
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Re:CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster tha...
by mlthoughts2018 on Sunday October 11, 2020 @07:53PM
(#24750149)
This is untrue, you can compartmentalize your C implementations using tools like Cython.
The fact that you write these as isolated special implementations is a good thing and not a deficiency of generics.
This way it can be gradual. You only target certain functions or modules for optimized C implementation, you don’t waste static typing overhead or compliance to a generic interface on the 99% of the code that will have no material gain from any optimization.
It’s similar with gradual typing in Python as well. Annotate what you need, omit annotations for what you don’t. Implement an extension module or multiple dispatch for what you need, use plain Python for everything else.