JSON3.jl

with the first example on https://www.json.org/example.html and the following Julia code using JSON3 data = JSON3.read("test.json") println(data[:glossary][:title]) running `time julia +release --project=. --startup-file=no test.jl` gives a total time spend of 0.39 seconds (running on a dev version of Julia brings it down to 0.30). The translation of this into python is faster (.02 seconds), but this means that as long as your script has at least a second or so of work to do, Julia will be faster. Specifically, the timing breakdown is 0.07 seconds to launch julia, 0.07 seconds to load JSON3, 0.0001 seconds to parse the file, .07 seconds of compilation for the indexing (I'm pretty sure this is fixable on the package side, see https://github.com/quinnj/JSON3.jl/pull/271), and 0.0001 seconds to do the indexing

>numerical optimisation stuff Yes, far exceeding python actually. - https://github.com/jump-dev/JuMP.jl - https://github.com/JuliaNLSolvers Are just some. > graphing Excellent support. - https://github.com/JuliaPlots - https://github.com/JuliaPlots/Makie.jl >Munging xml/Json etc - https://github.com/quinnj/JSON3.jl - https://github.com/JuliaData - https://github.com/Algocircle/Cascadia.jl - https://github.com/queryverse/Query.jl >oauth2 etc - https://github.com/JuliaCloud - https://github.com/JuliaWeb/HTTP.jl for anything not available yet, you can call it from python using PyCall.jjl