[pacofvf]

"Python is slow" argument just shows complete ignorance about the subject. (and there may be good arguments for not using python)

First of all if you are doing "for i in range(N)" then you are already doing it wrong, for ML and data analytics you should be using NumPy "np.arange()", Numpy arange doesnt even run in "python" it's implemented in C. So it may even be faster than swift '..<' . Let me know when you can use swift with spark.

[SaxonRobber]

This is actually one of the most frustrating parts about using python. You can’t write normal python code that performs well. Instead you have to use the numpy dsl, which I often find unintuitive and too often results in me needing to consult stack overflow. This is very frustrating because I know how I want to solve the problem, but the limitations of the language prevent me from taking the path of least resistance and just writing nested loops.

[pacofvf]

my point is that the benchmark is deceiving, again if you are doing data analytics or ML, then you already are using numpy/pandas/scipy, so thats not a valid argument.

[SaxonRobber]

But it is. A good compiler could unroll my loop and rewrite it with the appropriate vector ops. But that isn’t possible with just python right now.