Nrpf

Its also for general programming.

What do you think about Julia?

Julia

Looking forward. I was about to embark with python on a new long term project, but I might delay that pending the new blog post. If possible do you have a guestimate on what sort of time window we are looking at for this blogpost? Days, weeks, month?

Type unstable Julia is often slower than python.

Actually no. Naive Julia is often slower than python. Vectorized Array expressions are on par with numpy and slower than numba.

"type unstable", IO and text processing code is also slower.

Very glad to hear that. Anywhere roadmap/ central place where I can contribute and follow progress?

Thanks. The link seemed still to be single inheritance focus (sorry, I don't know the right terminology I come from a OO background.) but I could be wrong. Though I did get the sense that its just a interim step towards multi inheritance.

What about the dataframe and stats infrastructure? its currently in shambles. Any Idea when this can be expected to be fixed?

User defined types/classes are currently being worked on in an open PR. Excellent counterpoint for the time being, though.

Aside- Do you know if multiple inheritance/traits will happen at some point? I need this for modeling, even though it can be worked around for general software architecture.

no its not. Rcpp is cpp syntax and semantics. Look at the Numba example..its pure python with one decorator.

Julia also requires subsets of the language. Try writing rolled array expressions (in a loop or otherwise) in Julia and in Numba and see which one is faster.

Also try IO or text processing in Julia. Python is known to be faster right now.

Right now, numba is a better compiler than Julia has. You can write array expressions and loops in numba and it will do fusion/deforestation to eliminate temporaries giving you faster vectorized in loop code than Julia currently does.

Of course, this is currently being worked on in julia as well, but I don't see your point.

The dev team is pretty responsive and prioritize adding features based on bug reports like this. Can you open an issue?

https://github.com/numba/numba

Or you can use Numba to write gil free imperative code as well.

You mean IRLS or SGD or something like that?

Any tidbits about whats in store for the stats ecosystem? Tidyr/Dplyr style workflow? Flexible and Generalized stats MLE/ Bayesian modeling using distributions? Streaming and out of core support? What will the timeline be like?

I'm trying to make an architecture decision right now and this would be helpful (and whet my curiosity :p)

Or you can use numba and with pure python syntax and get really fast.

Actually I'm waiting for 3 based....

"many of the cons in the language will be solved"

Can you elaborate on this?

Re type system, how does it compare to python in use?

Thanks for your honesty re the web stuff.

I agree multi dispatch is awesome, and python has some implementations of it as well.

http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Disp...

Can't you call out to python with pycall for that?

Is it possible to have both? Quiet your mind and be in the moment for physical stuff but call on its rumination powers to think when needed?

Yes escher is amazing thanks :)

"more natural for general purpose modeling of data than, say, Python's classes"

Can you elaborate? In most cases single dispatch does just fine. Then class = type except much slower

Is this going to get us better dataframes and nullable integration?

Makes sense. But regardless, the core language is better than go in almost every way (as far as I know)... and it has python like syntax. I wonder if thats enough.

What python solutions are scatterbrained? Have you tried Numba?

What do you think about Julia?