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Don't get me wrong; I'm not suggesting that anyone dump Python altogether to switch to a different language for any arbitrary project or purpose. Many businesses I work with use different languages for different components or applications, using the network or storage (or even shared memory) to intercommunicate when necessary. The right tool for the job, as it were.

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I use python mostly for numpy/tensorflow/etc. The machine learning ecosystem. That ecosystem cares a lot about multithreaded performance. So historically answer has been write c/c++ and then bind to python. This work is mainly motivated by libraries like that wanting to write less c extensions and be able to just write python and still have proper thread performance.

As I mention elsewhere, I think what they care about is parallelism, not concurrency, and you don’t need threads to attain that efficiently.

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