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Except it isn't really. Yes, Python is incredibly slow for day to day stuff, but the sheer amount of easy to use numerical libraries (numpy, scipy, scikit-learn
by anon1253 9y ago
Except it isn't really. Yes, Python is incredibly slow for day to day stuff, but the sheer amount of easy to use numerical libraries (numpy, scipy, scikit-learn, tensorflow, keras, opencv, just to name a few) make it one of the fastest out there for numerical computation. I tried doing some numerical heavy stuff on the JVM (with Java and Clojure) and it fights you every step of the way ... and that has static typing and all the things the article mentions. Of course, Python derives that functionality from C and Fortran … but just having that interop at your fingertips is magical in terms of productivity. I still get nightmares from working with the JNI.
- pjmlp 9y agoThe point is not having to write C and Fortran in first place.
- dr_zoidberg 9y agoAs a heavy user of numpy, I use a lot of C and Fortran code, without having to write it.
- deleted 9y ago[deleted]
- jeremyjh 9y agoThis point is addressed in literally the first paragraph of the submitted article.
- plasticxme 9y agoIf you read the post, you’d know that you’ve basically summarized it.