[Mehdi2277]

Python is also the dominant language for machine learning which does care for performance. The person who made recent nogil work is one of the core maintainers of key ML library. The standard workaround is ML libraries, the performance sensitive stuff is written in C/C++ (either manually or with cython) and then uses python bindings. But it would be much friendlier if we could just use python directly.

It's also commonly used language for numerical work in general. Most of the time numpy is enough and then occasionally you'll need something not already implemented and then have to do your own bindings.

[cycomanic]

> Python is also the dominant language for machine learning which does care for performance. The person who made recent nogil work is one of the core maintainers of key ML library. The standard workaround is ML libraries, the performance sensitive stuff is written in C/C++ (either manually or with cython) and then uses python bindings. But it would be much friendlier if we could just use python directly.

Multithreading is not really the reason why things get written in cython etc., you can easily see 100x improvements in single threaded performance (compared to maybe a factor of 2-8x for multithreading). If you care about performance you'd definitely write the performance critical stuff in cython/pythran/c.

[pjmlp]

Nope, C++ and Fortran are.

The bindings available in Python can also be used from other languages.