Some fun with π in Julia

So it understand the concept of missing value and works with logical operation. Also it have factor concept into the language too.

General Language may not have the tool to deal with missing value and having a library doesn't necessary means it's good as a built-in concept NA.

Python panda and such does not handle NA as beautifully as R. (http://pandas.pydata.org/pandas-docs/stable/missing_data.htm...)

Python Panda default to Null and before that it uses something else to represent missing data. Null value in general is the last thing you do when you don't know how to represent a certain type. If you have a type language with sum type and pattern matching then you don't even need null. And yes R have its own Null type so NA is separate from it.

Erlang have PID as a primitive.

I think any domain specific languages are smaller in rules and syntax, and it makes it very very easy to learn for experts and people of those domains.

As for your broader question, Julia definitely fills a gap that I have been pained by for years. I've used R since it was in beta, and it is slow, which is a pain when you are discussing numerical needs. Yes, you can program in something like C/C++, but that is painful because of its overhead and dependency complexity (although it's surprisingly become less painful over time). Python could be used too, and probably is better at this point in that regard, but it has many of the same problems as R.

Julia is open-source, fast, and well-thought out with regard to modern numerical programming problems. I can write something in Julia and it performs essentially as well as something in C, which is a huge time saver in multiple respects.

I do wish Julia were more general-purpose in its orientation, or that the solutions it offers were coming from a more general-purpose language, but at the moment that doesn't seem to be in the cards. Maybe as it grows it will find use as a more general-purpose language, which is possible; maybe as languages like Rust or Go grow they will occupy this niche as well. Rust is interesting to me in this way, but currently it has little to offer in terms of simplification over C++ for numerics, and Go is not friendly to numerics. I personally like Stanza, but it's in its infancy, and no one probably even knows what I'm talking about.

For whatever reason, my experience has been that numerical programming has been a kind of isolate in programming. Numerical computing has always seemed slightly neglected in programming languages, and languages that have targeted numerical computing have often never been able to shake the "domain specific" label. I've just sort of come to see it as part of the territory.

There's nothing wrong with Python, C, or R. Also, languages change rapidly, so who knows what will happen. At the moment, though, Julia offers the best of all three and the only big downside is lack of libraries, which is becoming less and less of an issue every day (I wouldn't say there's a lack of libraries, more that there's fewer libraries). So I think it's deserving of its current attention.

I guess the question is, why would a systems programmer use C, or a web programmer use Javascript, or a network infrastructure programmer use Erlang, etc. etc. etc.?

Because you need special math support, easy access to suitable operators (and/or overloading), etc.