Hacker News Reader

Comment by ViralBShah | Hacker News Reader

Compared to a few years back, Julia is in seriously good shape. The long and short of this is that while the language has largely been stable, the array library in Julia will get an upgrade to become a lot more modern and clean.

For most users, this should not change much. However, it gives us the ability to do some really amazing things with our array infrastructure going forward.

View on HN

I must say that while some of this work may not change much for users, like array slices as views, for many - the entire set of the arraypocalypse work is certainly a huge change.

The key is to get it all done sooner so that we can keep getting that much closer to 1.0.

"However, it gives us the ability to do some really amazing things with our array infrastructure going forward."

What kind of things? Super curious.

The github issue linked actually has a detailed list of all the things that are planned - native bounds checking and removal, reshaped arrays, a lot more consistency in indexing and concatenation, slices as views, etc.

Also, any plans for the stats ecosystem? (dataframe, dashboards, imputation, data cleaning etc)

It is still a bit wanting.

I've been doing a lot of work behind the scenes there, but it's still going to take a while to offer a mature product. What we really need are more qualified engineers to speed up the process, but there's a vanishingly small group of people who are interested in doing eng work on statistics libraries.

Makes sense. Thanks for both your open source work and your reply!

A lot of interesting things are going on in the DataFrames area - especially with the recent work on NullableArrays by David Gold and John Myles White. A blog post will be out soon describing it all. In my opinion, Escher.jl makes it really easy to build amazing dashboards and is progressing well.

But, a lot more remains to be done, and contributions would be really welcome in these areas that take Julia's statistical computing to the next level.

I'd like to add a note about my experience with doing this; I ended up forking Distributions.jl and added a fitting function for the Weibull distribution. Still need to clean it up and do a pull request,.

The amazing thing about Julia I've come to enjoy is how clean the source code, how easy it is to find functionality, and it's all written IN Julia. That means I can modify libraries that are normally C or C++ for perf reasons. It's changed the prospect of needing to add functionality from one of hesitation to feeling that it's actually enjoyable.

I'm really glad to hear this! I'd like to think that the community and inspectability argument for Julia is actually one of the strongest ones out there, but it's not easy convincing people who haven't had that experience.

It really is pretty great.

Reply on news.ycombinator.com