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However Python is getting attacked by new language in all areas where it is being used. Go attacks just one area.

Julia is a much better language for data science than Python. It was essentially designed for it, while for Python it was an afterthought.

Python has a head start today with a large selection of packages and mindshare, but as we've seen with Go, I think that will start eroding in the future.

Python simply can't keep up with the development pace and possibilities offered with Julia. For Data Science and AI packages in Python to have acceptable performance they need to be written in C/C++, this means slow development. Julia has high enough performance that all these packages can be developed in Julia itself. And this is what people in this area is observing. That package development is moving much faster in the Julia community than in Python.

You can also combine these packages in ways that are simply impossible to do in Python. The need to interface with C/C++ creates a lot of artificial restrictions. E.g. processing lots of data fast requires big NumPy buffers with plain data types.

In Julia you can e.g. create a an image buffer made up of color objects and process it fast.

Unlike Go, Julia offers the ability of a gradual transition to Julia from Python, since Python packages can be used in Julia.




To me, Julia feels clunky as all hell. The other thing is that Julia doesn't help for deep learning, where something like 99.99% of the computation time is actually CUDA kernels.

I don't know; I actually really like the optional typing of Julia in theory, I don't know why I find the language so unpleasant.




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