I strongly recommend Go as a better Python. Personally, I think it's easier to write than Python (although people who care very little about correctness will be bothered a bit by the type checker), and the tooling is many times better (single-binary deployments, great dependency management, etc are awesome). Also, the performance is about 100-1000 times better for serial execution, and Go's goroutines allow you to take advantage of multiple cores much more easily than with Python.
JavaScript and TypeScript are similarly easy-to-use, performant languages with a better-than-Python tooling story. I've also heard similar things about Elixir, Closure, and Kotlin.
Some of Python's strengths that I work with regularly are dynamism, easy data exploration, visualizations, succinct & customizable syntax, extremely strong data science libraries, REPL/Jupyter, C bindings, easy to use packaging solution via PyPi (bet some people are going to disagree with that one), quick to prototype code. If you really need performance in Python, you can probably get it out of the box (if you can run deep learning with Python, performance isn't a limitation).
I love Go and use it for a bunch of projects, but I've only once wanted to move a project from Python to Go and that was a performance-centric CLI that was only written in Python originally because of how quickly it let us prototype in comparison to Go.
From my perspective Julia is sacrifising some amount of "duct tape UX" to gain speed, and that's the wrong direction.
Whenever we need more speed, we just pull the slow bits down into compiled languages, and scale them out to many cores with solutions like MPI.
R is another language that is mainly duct tape for stringing pieces of compiled code together. If it had a better UX for developers than Python, I think we would see it dominating much more today, without having any speed advantage.
Interestingly, R is probably a better UX for statisticians/data scientists than Python is (almost all the good parts of Numpy/Pandas were in R first), but it really suffers from not being well known by developers.
To be fair to R though, it's much, much easier to deploy than Python, which is a shocking indictment of the current Python packaging ecosystem.
This only works sometimes--for problems that allow you to do a relatively large amount of computation in the compiled language to justify the cost of marshalling Python data structures into native data structures. For matrices of scalar values, this works well. For many other problems (consider large graphs of arbitrarily-typed Python objects, or even a dataframe on which you need to invoke a Python callback on each element). If you rewrite a big enough piece of your Python codebase in the compiled language, then it will work, but now you're maintaining a significant C/C++/etc code base and the bindings and the build/packaging system that knows how to integrate the two on all of your target platforms. Python really doesn't have a good answer for these kinds of problems, and these are by far the more common case (though perhaps not more common in data science specifically).
What particular language features of Julia make that trade-off? (Not a rhetorical question, I'm not disagreeing with you, just curious; I'm familiar with Python, not really familiar with Julia.)
These aren't fundamental issues with the language and will be solved by tiered compilation (there's already an interpreter mode, just have to integrate that with normal use), separate compilation and incremental sysimage creation which works with the package manager.
The type-assertion escape hatch has always been largely sufficient for all but the most performance-critical projects (of which I have exactly 1, and it was a side project), and runtime panics due to failed assertions are quite easy to eliminate via wrapper types.
My advice: just go for it. You almost certainly won't miss generics.
(Edit: I've broken my own rule and given advice without first asking what kind of programming you do. My assertion holds for most run-of-the-mill stuff, e.g. writing REST interfaces, network servers, etc. If you're within 2-sigma of the industry, you won't miss generics.)
Dynamic languages do that implicitly.
Implement max in Go with interface{} without casts and reflection:
def max(a, b):
if a >= b:
a
else:
b1. The abstract idea of writing an algorithm that supports a variety of types. This allows for casting, reflection, dynamic typing, etc.
2. The specific idea of a type system that allows for parameterized types (aka "typesafe generics"). This definition excludes castng, reflection, and dynamic typing.
Typically "typesafe generics" is what people talk about when they discuss "generics", but since you chose to pick the "dynamically typed languages are generic" nit, I assumed you were talking about (1).
More than performance (pure Python can be too slow, but NumPy is usually fast enough), this is what I miss when using Python.
There's currently no good solution for bundling Python code and the interpreter into a single binary. PyInstaller works, but the resulting binaries write lots of files to `/tmp` every time they run, which is a hack that leads to long startup times. PyOxidizer avoids this, but includes the entire standard library in every binary, making them too large by an order of magnitude.
I've heard Go described this way several times, but I've found it to be a significantly lower-level language than Python.
For example, it's much more verbose. In this recent blog post [0], the author converts some C++ code to Go - and it gets longer. 57 lines of C++ become 65 lines of Go. The same code in Python is about 20 lines.
In particular, the author's Go code requires five lines to do the equivalent of Python's `with open(path) as f` and four lines for the equivalent of `word_array = list(word_counts.items())`:
f, err := os.Open(path)
if err != nil {
return err
}
defer f.Close()
// [...]
wordArray := make([]WordCount, 0, len(wordCounts))
for word, count := range wordCounts {
wordArray = append(wordArray, WordCount{word: word, count: count})
}
This is crazy. Many of those lines are closing brackets or whitespace. But moreover, optimizing for characters or LOC is absurd. Optimize for maintainability or readability, at which point Go is at least as good as Python (I would argue better). Optimize for tooling, especially package management and build tooling--Go is many times better than Python here. Optimize for performance--Go is literally hundreds or thousands of times better here. Optimize for breadth and quality of ecosystem. Optimize for deployment story (single small artifact vs hundreds of megabytes of dependencies). These are the things that matter, not lines of code.
It's still a little too wild-west for my tastes right now, but I want it to improve as I think it's got real potential.
What are those languages? I may have a blindspot, but the languages that get enough buzz for me to notice are either not competing with Python in important dimensions (e.g. Rust) or have a narrower focus (e.g. Julia). Elixir maybe? JavaScript and its derivatives? But these lack the scientific programming ecosystem that's helped drive Python recently.
I have no doubt that languages exist that might fit the bill of being both as easy as and more performant than Python -- there are a lot of languages out there. But I'm unaware of any whose mindshare has been sufficiently growing that it threatens Python in the mid-term.
If I'm off base please let me know. I'd love to find a viable competitor to Python that's strictly better than it.