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That's exactly what this is doing. Numpy is glue around lapack/blas. R is okay for batch processing, but what happens when management wants engineering to impl
by kortex 5y ago
That's exactly what this is doing. Numpy is glue around lapack/blas.
R is okay for batch processing, but what happens when management wants engineering to implement data science's models alongside some pytorch models?
Python is totally performant enough if you know how to wield it.
- qualudeheart 5y agoMy mistake. I scrolled through the article but didn’t see the part where numpy is called.
- igouy 5y agoSo "Python is totally performant" when it's Fortran :-)
- jpgvm 5y agoPretty much. The only fast parts of Python aren't Python.
- odonnellryan 5y agoI always feel this is a little unfair. Sure, a Python program and an R program, written the same way, using the same data structures, etc.. is going to usually show the R program is faster. But getting to that point is where the challenge is, and I feel that Python makes thinking about things like data structures and the algorithms you're using (in the case of external libraries) or writing much easier than other languages. In my experience once you "get there" that is enough.
- mattkrause 5y agoThe catch, IMO, is that the need to stay in the numpy sandbox really constrains how you choose data structures and algorithms. Storing N-dimensional points in a Point class, for example, is often so slow as to be a non-starter. Admittedly, it's not just a Python problem: struggles with array-of-structs vs. struct-of-array representations are pretty ubiquitous.
- odonnellryan 5y agoThat's true if you need to stay in that sandbox, which isn't always the case!