For that kind of workflow you would be far better off with e.g. Julia. You get the same advantages as Python as having a language you can experiment with until you find a solution. Only difference is the optimization step later does not involve having to rewrite in another language.
If you already know Python, and Python packages already do all you will ever need then sure stick with that. But I don't get why people would go to such lengths to avoid using a new language. Being proficient in Julia is a lot less work than maintaining proficiency in Python and C++.
The last time I checked, using Julia was clunky at best with ridiculously high jit-compile times, packages that refused to build on my machine etc..
What is more, many of the "best" Julia libraries were seemingly just linked-in Python code.
I don't mean to discredit the advantages Julia clearly has over Python, but these are just the kinds of problems that make people like me stick with tried and tested last-gen languages like Python.
Did you ever check after 1.0 was released? In the earlier days it was a lot of problems with packages. Totally agree. JIT compile times are much better now.
A lot of the issues are simply that people have not learned a sensible workflow with Julia. Python guys have a lot of habits that don't translate well to Julia. I know because I work daily with two hardcore python guys. I notice all the time how we approach problems in very different ways.
Python guys seem to love making lots of separate little programs they launch from the shell. Or they just relaunch whole programs all the time.
In Julia in contrast you focus on packages from the get go and you work primarily inside the Julia REPL. You run Revise.jl package which picks up all the changes you make to your Julia package.
I guess it just depends on the workflows you are used to. For me it is the opposite. Whenever I have to jump into our Python code base I absolutely hate it. It is very unnatural for me to work in the Python way. I also find Python code kind of hard to read compared to Julia code.
But I know Python coders have the opposite problem. Basically Python guys look a lot at module names when reading code. Julia developers look more at types. The difference makes some sense since you don't really write types in Python code.
I found that the new Python type annotation system helped me feel at home in Python.
OMG. Until now I didn't realize Cobol is more popular than Haskell and Delphi. I had just read there are ~2.5 Cobol programmers left in the world, who are old like Gandalf, enjoy ridiculously high salaries and can't retire because nobody learns Cobol any more while the world needs somebody to maintain those legacy systems that still run its economy. While Delphi used to be the PHP of the desktop world until very recently (and could hardly be expected to decline so fast) and Haskell seems to be the computer science lingua franca.
>The more a language tutorial is searched, the more popular the language is assumed to be. It is a leading indicator. The raw data comes from Google Trends.
That's the problem, nobody is actively learning Cobol these days and nobody knows how much is running out there in the wild, because none of the big banks or credit companies will actually admit it.
If you already know Python, and Python packages already do all you will ever need then sure stick with that. But I don't get why people would go to such lengths to avoid using a new language. Being proficient in Julia is a lot less work than maintaining proficiency in Python and C++.