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I think the biggest thing that is "light years ahead" is that numpy is built right in and insanely extensible. Libraries can be very lightweight / maintainable
by WanderPanda 6y ago
I think the biggest thing that is "light years ahead" is that numpy is built right in and insanely extensible. Libraries can be very lightweight / maintainable because they don't need to roll their own numpy like tensorflow pytorch etc. do (which makes them depend on bigcorps to improve and maintain). In julia somehow everything is compatible and composable. On the other hand counting from 1 is is quite a big issue for me and leads to constant errors when I switch between python and julia. Also the startup time it takes is a huge bummer. They should have an interpreted mode for debugging (as long as the debugger is unusable for e.g. breakpoints)
- UncleOxidant 6y agoI haven't tried it yet as it's still a release candidate, but startup time is said to be much improved in v1.6.0.
- adgjlsfhk1 6y agoI think the key is less that it has good multi-dimensional arrays built in, and more that Multiple dispatch makes Julia more composable than python. For an example of this, consider that BandedMatrices is a package that can be used in conjunction with any library that is expecting a Matrix like object, despite the fact that most of them weren't designed to do so.