For your interest: of your core packages: numpy, scipy, sympy, matplotlib, and f2py
numpy functionlity should be all in Base. Sympy has a julia version <https://github.com/jverzani/SymPy.jl>, wrapping pyCall, and it is great. Similarly so does matplotlib <https://github.com/stevengj/PyPlot.jl>, though I've not used it, personally it was a relief to get a way from matplot lib and onto something less explicit and more intutive like GadFly <https://github.com/dcjones/Gadfly.jl> I've not used f2py, but I suspect core ccall <http://julia.readthedocs.org/en/latest/manual/calling-c-and-fortran-code/>will do it. Scipy is huge and vast, and I constantly forgetting what is in there. The most useful parts of Scipy, tend to be in the Managed Julia Github Groups, or including the Core Group <https://github.com/JuliaLang> The managed groups (self managed that is) can be found on the communities page <http://julialang.org/community/> Cutting it down to just the things that I believe are in Scipy: (Some overlap with sklearn) - JuliaStats <https://github.com/JuliaStats> – Statistics <http://www.juliastats.org/> - JuliaOpt <https://github.com/JuliaOpt> – Optimization <http://www.juliaopt.org/> - BioJulia <https://github.com/BioJulia> - Biology - JuliaAstro <https://github.com/JuliaAstro> – Astronomy - JuliaQuantum <https://github.com/JuliaQuantum> – Julia libraries for quantum science and technology <http://juliaquantum.github.io/> - JuliaSparse <https://github.com/JuliaSparse> – Sparse matrix solvers - JuliaDiff <https://github.com/JuliaDiff/> – Differentiation tools <http://www.juliadiff.org/> - JuliaDSP <https://github.com/JuliaDSP> – Digital signal processing - JuliaGraphs <https://github.com/JuliaGraphs> – Graph Theory and Implementation As Matt Bauman points out, the julia devs / major contributors tend to be major players in one or more communities. Packages within the communities tend to be of higher confidence in there reliability. More certain to be maintained. You can normally trust a package from one of the communities. So they are always my first stop when looking for a package to do something. My second stop right now tends to by using PyCall (for sklearn and NLTK). Hope that helps.
