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At this point in time, I think that the Python/Numpy stack offers the best performance, productivity, and expressiveness trade-off. With the [Numba](http://num

by pwang 10y ago

At this point in time, I think that the Python/Numpy stack offers the best performance, productivity, and expressiveness trade-off. With the [Numba](http://numba.pydata.org http://numba.pydata.org) just-in-time compiler, you can now easily bounce between numeric SIMD codes that leverage tuned BLAS/MKL, then go into more explicit loop-oriented constructs that perform equivalently to hand-coded C, while still being Python. If I were starting anew, it would be hard to justify investing in a big J/K/Q code base or team, despite the potential performance benefits. I agree with your overall point that we're seeing a confluence of factors. The advances in compiler technology, combined with the vectorial nature of the problems that are interesting to solve in an era of big data, mean that we can achieve a great deal of productivity by using high-level vector-capable languages.

eggy 10y ago
You may be right. I can't argue with Python's ubiquity; I have even steered my son in that direction, but with a hitch: I still had him learn some J. The creator of Pandas, Wes McKinney, had a link up a few years back mentioning he was looking for people who were familiar with APL, J or K. It seems he was working on a new project/startup I think (could this have been the shuttered DataPad?). The links are dead now, but I will double check. If the creator of Pandas is/was eyeing the older APL, and its newer brethren, I'd say it's a safe bet to keep J or K or Q on your radar because they fit. They're vector/array based; they are fast and iterative with a REPL; there is a lot of mathematical formalism in their origins and usage throughout the years, yet they are more beginner-friendly than say Haskell IMHO. I like Haskell too!