alt Hacker NewsWhy? I've build some massive analytic data flows in Python with turbodbc + pandas which are basically C++ fast. It uses more memory which supports your point, but on the flip-side we're talking $5-10 extra cost a year. It could frankly be $20k a year and still be cheaper than staffing more people like me to maintain these things, rather than having a couple of us and then letting the BI people use the tools we provide for them. Similarily when we do embeded work, micro-python is just so much easier to deal with for our engineering staff.
The interoperability between C and Python makes it great, and you need to know these numbers on Python to know when to actually build something in C. With Zig getting really great interoperability, things are looking better than ever.
Not that you're wrong as such. I wouldn't use Python to run an airplane, but I really don't see why you wouldn't care about the resources just because you're working with an interpreted or GC language.
Replies
From the complete opposite side, I've built some tiny bits of near irrelevant code where python has been unacceptable, e.g. in shell startup / in bash's PROMPT_COMMAND, etc. It ends up having a very painfully obvious startup time, even if the code is nearing the equivalent of Hello World
time python -I -c 'print("Hello World")'
real 0m0.014s
time bash --noprofile -c 'echo "Hello World"'
real 0m0.001s
> you need to know these numbers on Python to know when to actually build something in C
People usually approach this the other way, use something like pandas or numpy from the beginning if it solves your problem. Do not write matrix multiplications or joins in python at all.
If there is no library that solves your problem, it's a great indication that you should avoid python. Unless you are willing to spend 5 man-years writing a C or C++ library with good python interop.