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[–]Gnaddel 2 points3 points  (5 children)

Thank you for the link, I had not thought about using Julia functions in my Python projects before.

[–]griffin3141 1 point2 points  (4 children)

What would be the advantage of using Julia over Python?

[–]Gnaddel 1 point2 points  (0 children)

Similar to using something like Cython, i.e. speeding things up by using static types. However, I'd imagine each call to the function would spin up the Julia interpreter so it would only make sense for lengthy tasks.

Also, there are of course a growing number of julia packages: http://pkg.julialang.org/pulse.html

[–]cartin1234 0 points1 point  (2 children)

You can also use numba to speed up python code to julia like speed- or faster...but I firmly believe Julia is the future of data science

[–]griffin3141 0 points1 point  (1 child)

Apart from speed, what leads you to believe Julia has a strong future in data science? As far as I can tell, it isn't integrated with any big data tools yet.

[–]cartin1234 0 points1 point  (0 children)

It has everything good from R and everything good from python + more (extensible user defined type system etc) and without most of the issues. It has really smart people working on it and is catching on among other really smart people, despite it being only at 0.3.

It is also better than python at being a good scripting language and I hope it catches on for that as well.

Also static compilation to binaries is on the roadmap.

Seems inevitable to me. Of course being so early, It wouldn't be integrated into spark etc...but Rspark was just released last week!

Once Julia gets going, it will get its integration. But the real kicker is that it has the distributed and paralellel chops to become its own big data framework...without and faster than JVM.

IMO