[609venezia]

> I believe Julia to be the future but so far the adoption rate in house has been low.

Why do you believe it will be the future, and what do you see as the barriers to roll-out? I ask as someone who is curious about when/whether to start investing in Julia competence

[starpilot]

> We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled. (Did we mention it should be as fast as C?)

https://julialang.org/blog/2012/02/why-we-created-julia

I've been playing around with it. As a Python/MATLAB guy, the syntax is very friendly. I can see it displacing Python in production code where you need speed and might avoid some of the heavy Python DS libraries. Overall it seems like a thoughtful combo of a lot of good numerical programming features.

[meztez]

Debugging is something I could not do as easily in Julia vs debugonce and trace in R. Compiling takes time.

But so far we have seen great development. Flux is a truly beautiful ml library. Being a compiled language remove a lot of headache when building production images. The syntax, the full utf support in variable name. Package management is great. Having that abstraction layer between CPU, GPU so you don't have to rewrite code. Dispatch based on signature, type management. I don't see it going away soon. It took me 13 years to make them transition out of SAS, good thing cloud computing come around and someone realised the clusterfuck of having to manage SAS licence in the cloud.

[CoolGuySteve]

Both R and pandas force you to wrap your problem around dataframes and vectorized operations. But sometimes you really do just want to write a loop that iterates over the data.

Right now the only way to do that without significant performance costs is to drop down into C or avoid the problem completely by using Julia.

Having worked with both R and Python on large datasets, I think both languages are really easy until they aren’t. Eventually you hit a performance wall.

[amrrs]

You can increase the speed of loops in Python using Numba. It's really a great performance booster with just a few decoraters added.

[goatlover]

You can drop down into the Numpy values array in Pandas to get your performance gain when iteration is otherwise slow.