Comparison – R vs. Python: head to head data analysis

Re:Comparison – R vs. Python: head to head data analysis by ForHackernews on Wednesday October 14, 2015 @10:46AM (#10386883)

If you haven't already, you might want to take a look at Julia. It's extremely fast, and has more native support for vectors than Python. It's still immature, but I think it has great potential as the truly great language for scientific/data computing.

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Re:Comparison – R vs. Python: head to head data analysis by vegabook on Wednesday October 14, 2015 @11:10AM (#10387041)

I heard that the vector operations were very slow though. Has this changed?

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Re:Comparison – R vs. Python: head to head data analysis by optimali on Wednesday October 14, 2015 @12:11PM (#10387461)

It seems that though with in Julia, vectorized code is typically slower than non-vectorized, it is still faster than in R [1] / Python [2].

[1] http://www.johnmyleswhite.com/notebook/2013/12/22/the-relati...

[2] http://blog.rawrjustin.com/blog/2014/03/18/julia-vs-python-m...

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Re:Comparison – R vs. Python: head to head data analysis by yummyfajitas on Wednesday October 14, 2015 @12:05PM (#10387400)

Vector operations are not slow - they are basically the same as python/R (compiled down to C).

However, devectorization (i.e. replacing vector ops with a for-loop) is sometimes a performance improvement because Julia can usually provide C-like speeds in for-loops and avoid creating intermediate arrays.

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Re:Comparison – R vs. Python: head to head data analysis by ced on Wednesday October 14, 2015 @12:02PM (#10387370)

Julia's for loops are comparable to C in performance, and its vectorized operations are comparable to Numpy/R, although some cases can be optimized using https://github.com/lindahua/Devectorize.jl (see the benchmarks table)

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Re:Comparison – R vs. Python: head to head data analysis by vegabook on Wednesday October 14, 2015 @12:28PM (#10387570)

Okay. This post:

http://www.johnmyleswhite.com/notebook/2013/12/22/the-relati...

had worried me a couple of years ago. JMW shows that vectorized was much slower also in Julia (though still both faster than R - but that's not difficult).

Glad to see Julia is very fast in both cases, though it's still somewhat perplexing the extent to which vectorized code is necessarily slower. I'm thinking that the future of GPU enabled languages will mean vectorized code will be faster, so I prefer languages with a bias towards vectorisation.

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Re:Comparison – R vs. Python: head to head data analysis by ced on Wednesday October 14, 2015 @01:01PM (#10387819)

it's still somewhat perplexing the extent to which vectorized code is necessarily slower

The vectorized code typically allocates all kinds of intermediate results (more GC, more memory accesses). Apparently, turning it into loops is less trivial than it seems.

I'm thinking that the future of GPU enabled languages will mean vectorized code will be faster, so I prefer languages with a bias towards vectorisation.

I share that concern. Julia has some libraries to support GPU programming, but I don't know of any plans to have the core compiler take advantage of it.

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Re:Comparison – R vs. Python: head to head data analysis by johnmyleswhite on Wednesday October 14, 2015 @01:14PM (#10387911)

I think you may have misinterpreted that post. Look at the table under "Comparing Performance in R and Julia" again.

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