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Python is not the de-facto standard to my knowledge. It really depends on the field and task. R is huge in my research area, MatLab is king in some other areas,

Python is not the de-facto standard to my knowledge. It really depends on the field and task. R is huge in my research area, MatLab is king in some other areas, and Fortran is still being used for large-scale number crunching. Will Julia replace all these? That's the ambition but I doubt it's going to happen. For instance, R has a unique and vast ecosystem of extension packages. It would take decades to build something comparable in Julia.

FridgeSeal 8y ago
If there were pandas, sklearm and Keras equivalents (don’t even need feature compleye, just mostly there) I would abandon python literally today.
- mindB 8y ago
  - pandas :: your choice of DataFrames or JuliaDB - scikitlearn :: no single package since skl is a meta-package of sorts, but most of the stuff is there spread across the eco-system - Keras :: checkout Flux or Mocha Welcome to Julia! Also take a look here [1] for more package that might be to your interest. [1] https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e782f625677c08ad36f5d918/AI.md https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e78...
  - oxinabox 8y ago
    Pandas actually has many alternatives. Including JuliaDB, DataFrames, and Pandas.jl Keras also many. TensorFlow.jl, Flux, Mocha, KNet, MxNet. Sklearn is all there but bring it together will take a bit. Some important parts are in JuliaML org. Also Clustering.jl, and MultiariateStats.jl (For DR) the classifiers are really scattered. I'ld love to fix that if I had time.
  - FridgeSeal 8y ago
    You're really making my day here! :D
Certhas 8y ago
Where I am people are really pushing out of the MATLAB/Python world into Julia. It's tailor made for scientific computing in a way that none of the others are. Clearly we're talking decades, but I am already realizing pay-offs from the switch to Julia (from Python) in my domain. Rackauckas pointed out an amazing example recently [1]: https://discourse.julialang.org/t/differentialequations-jl-and-measurements-jl/6350 https://discourse.julialang.org/t/differentialequations-jl-a... You have a library that implements calculating with numbers with uncertainties. You have the differential equations library. They don't know about each other but you can use the former in the latter to solve differential equations with uncertainties with a whole bunch of advanced solving algorithms. This sort of power, having an ecosystem like Python that combines in a performant way, will be huge. [1] http://www.stochasticlifestyle.com/why-numba-and-cython-are-not-substitutes-for-julia/ http://www.stochasticlifestyle.com/why-numba-and-cython-are-...

FridgeSeal 8y ago

If there were pandas, sklearm and Keras equivalents (don’t even need feature compleye, just mostly there) I would abandon python literally today.

mindB 8y ago
- pandas :: your choice of DataFrames or JuliaDB - scikitlearn :: no single package since skl is a meta-package of sorts, but most of the stuff is there spread across the eco-system - Keras :: checkout Flux or Mocha Welcome to Julia! Also take a look here [1] for more package that might be to your interest. [1] https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e782f625677c08ad36f5d918/AI.md https://github.com/svaksha/Julia.jl/blob/e305195ab60e6859e78...
- oxinabox 8y ago
  Pandas actually has many alternatives. Including JuliaDB, DataFrames, and Pandas.jl Keras also many. TensorFlow.jl, Flux, Mocha, KNet, MxNet. Sklearn is all there but bring it together will take a bit. Some important parts are in JuliaML org. Also Clustering.jl, and MultiariateStats.jl (For DR) the classifiers are really scattered. I'ld love to fix that if I had time.
- FridgeSeal 8y ago
  You're really making my day here! :D

Certhas 8y ago

Where I am people are really pushing out of the MATLAB/Python world into Julia. It's tailor made for scientific computing in a way that none of the others are. Clearly we're talking decades, but I am already realizing pay-offs from the switch to Julia (from Python) in my domain. Rackauckas pointed out an amazing example recently [1]: https://discourse.julialang.org/t/differentialequations-jl-and-measurements-jl/6350 https://discourse.julialang.org/t/differentialequations-jl-a... You have a library that implements calculating with numbers with uncertainties. You have the differential equations library. They don't know about each other but you can use the former in the latter to solve differential equations with uncertainties with a whole bunch of advanced solving algorithms. This sort of power, having an ecosystem like Python that combines in a performant way, will be huge. [1] http://www.stochasticlifestyle.com/why-numba-and-cython-are-not-substitutes-for-julia/ http://www.stochasticlifestyle.com/why-numba-and-cython-are-...