3 ms·
i dont seem to follow. if you are doing data science, all the bottle necked stuff will be running in numpy or pyspark. Choosing python over R, SPSS, Matlab us
by autokad 9y ago
i dont seem to follow. if you are doing data science, all the bottle necked stuff will be running in numpy or pyspark. Choosing python over R, SPSS, Matlab usually doesnt come down to which one is faster, and R as far as i know is at least not vastly superior in speed.
- make3 9y agoor a cuda wrapper
- michaelsbradley 9y agoAs with Python, the fast libraries written for R are usually implemented with something else under the hood. Take the data.table library, for example: https://github.com/Rdatatable/data.table https://github.com/Rdatatable/data.table It's wicked fast for many kinds of tasks, but its R API is just a thin layer on top of C.
- kazagistar 9y agoThis was explicitly addressed in the article: as soon as you have to do anything which isn't a trivial numpy operation, performance goes off a cliff, and that can be a problem.
- adamson 9y agoThis also isn't necessarily true. Take the example of TensorFlow. You build a representation of the computation you want to run, and then you can run nearly the whole thing end-to-end in native C++ using Eigen data structures, with occasional shuttling of data back into PyObjects (rare) or numpy (common) for metrics tracking. Cython is a much more powerful tool than I think the author of this article realizes.