Python predates Julia by 3 decades. In many ways Julia is a response to Python's shortcomings. Julia could've never taken off "instead of" python but it clearly hopes to become the mature and more performant alternative eventually
Some small additional details: 23 years not 30. Also, I think Julia was started as much in response to Octave/Matlab’s shortcomings. I don’t know if it is written down, but I was told a big impetus was that Edelman had just sold his star-p company to Microsoft, and star-p was based around octave/matlab.
Thanks for the corrections/added context and the fun link. The whole blog post feels like an attempt to name-drop as many related languages as possible
> 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?). While we're being demanding, we want something that provides the distributed power of Hadoop — without the kilobytes of boilerplate Java and XML
> We are power Matlab users. Some of us are Lisp hackers. Some are Pythonistas, others Rubyists, still others Perl hackers. There are those of us who used Mathematica before we could grow facial hair. There are those who still can't grow facial hair. We've generated more R plots than any sane person should. C is our desert island programming language.
If ever there was a language justified in claiming to be heavily inspired by a dozen other languages, I'm sure Julia is it.
When I first heard about Julia I understood it to be a faster alternative to Python. As I started to learn it I realised that's really not what it's about, it's trying hard to compete simultaneously with Matlab and R and Fortran and C++ (and the template metaprogramming language hiding in C++) and APL and Lisp and maybe OCaml just as much as Python (but not Rust or Java or Agda), and I can't even speak to the other languages mentioned.
When Julia came out neither Python nor data science and ML had the popularity they have today. Even 7-8 years ago people we're still having Python vs R debates.
In 2012, python was already well-established in ML, though not as dominant as it is today. scikit-learn was already well-established and Theano was pretty popular. Most of the top entries on Kaggle were C++ or Python.
Julia only came on to my radar in scientific computing (not ML) in about 2015-2016 or so but while I tried it at the time, it was really not very stable and my view was that it was very immature compared to Python’s scientific ecosystem. Looking at the dates, v1.0 came out in 2018 and I remember going to a talk about it at my academic institution where someone showed off the progress and we had a play again in our research group but it still didn’t have many things we needed and the trade offs felt not great as we were heavy users of IPython and then when it came out Jupyter and while Ju stood for Julia the kernel development environment didn’t work so well because rerunning cells could often cause errors if you’d changed a type for e.g.
At the time we were part of the wave I suppose that was trying to convince people that open source Python was a better prospect than MATLAB which was where many people in physics/engineering were on interpreted languages. At least in my view, it wasn’t until much more recently that Julia became a workable alternative to those, regardless of the performance benefits (which were largely workable in Python and MATLAB anyway - and for us at least we were happy developing extension modules in C for the flexibility that the Python interface gave us over the top).
