And you get all of this WITHOUT static typing and yes in Julia everything is still a full-fledged object. There is no difference between numbers and other objects in Julia. But numerics is still fast.
My biggest qualm with Julia (and maybe this speaks to my inexperience with the language) is that it isn't always obvious when Julia is going to make a copy. We spent about an hour working through some code that was very slow (props to Julia's profiling tools) but couldn't figure out _why_ it was slow. It turned out that despite our best efforts, Julia was still copying a vector despite us using pre-allocated scratch space for the work.
From my point of view, if I am comparing algorithms then Python's performance doesn't really matter and it's ergonomics win. If performance matters I'd just use C++ or Rust.
If you're talking about slices of an array, those always create a copy unless created with the @views macro (or the equivalent function call).
When Julia makes a copy is pretty straightforward and natural IMHO. I would have been curious to see an example of the code you used where a copy was made without you knowing.
I started with Python, but I find Julia better is almost every single way I can think of. Like even if Julia was slower than Python I would have picked it because I find it so much nicer to use.
I wrote an article here about some of the observations I had about using Python after coming back to it from Julia:
https://medium.com/@Jernfrost/python-vs-julia-observations-e...
There are some exchanges further down. Would have been interesting to hear your feedback on some of those things.
It's not as nice as Python, nor as fast as C++. And much less supported (tools/libraries/...) than both.
So it sits in this awkward middle between Python and C++, basically sucking at both and excelling at none.
(The problem that I'm having with Julia isn't the math/computational aspect, it's Julia's use as a more general purpose programming language in additional to math.)
With Julia I get first class meta programming. I get awesome multiple dispatch. I get environments and package system really well integrated. I get awesome integration with the shell. Better module system. More natural syntax for arrays. Much better system for closures. Better named functions.
REPL programming in Julia is just light years ahead of anything in Python. The OOP design of Python really kills the REPL experience.
Unless you are a very skilled C++ programmer, Julia is going to outperform you as the program gets larger. C++ programmers are going to get themselves tangled up when trying to run multi-threaded code, running on multiple machines on GPUs and specialized hardware. Julia does this effortlessly.
C++ cannot do JIT, hence as soon as you deal with complicated machine learning algorithms with custom kernels, C++ is going to tie itself into a knot.
Why do you think large Astronomy projects like Celeste and the next major climate models are built in Julia and not C++? Because developers realized that when you need to run massive calculations on super computers on hundreds of thousands of cores, C++ is going to get in the way.
As for libraries and tools. All the Python tools I have tried to match my Julia tools have just sucked. Julia tools often excel over much older Python tools.
Library development moves much faster on Julia than Python. It is not hamstrung by relying on complicated C++ code based. Also Julia libraries integrate very well, while Python libraries are often their own deserted island. That means a few Julia libraries can do what must be accomplished with dozens of Python libraries.