We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.
Julia's advantage is that good performance is not limited to a small subset of “built-in” types and operations, and one can write high-level type-generic code that works on arbitrary user-defined types while remaining fast and memory-efficient.
Types in languages like Python simply don't provide enough information to the compiler for similar capabilities, so as soon as you used those languages as a Julia front-end you would be stuck.
Local installation
If does not work for you, you can install the package locally. There are two options, using docker, or using plain julia.
Docker (recommended)
This process is identical to how mybinder.org is actually doing it. Hence you will get the very same environment.
Usually, a browser is opened automatically for you, but if not, an url is also printed at the very end of the command output. Copy that one to your browser and you are ready to go.
Julia only
This process is installing dependencies tracked by Julia, which will suffice for 98% of the material here.
ProjectFolder="$HOME/JuliaProjects"
mkdir -p "$ProjectFolder"cd"$ProjectFolder"
git clone https://github.com/jolin-io/fall-in-love-with-julia
cd fall-in-love-with-julia
julia --project --eval 'import Pkg; Pkg.instantiate(); Pkg.add("IJulia")'
Run jupyter
Either use your favorite jupyter installation or run
ProjectFolder="$HOME/JuliaProjects"cd"$ProjectFolder"
julia --project --eval 'import IJulia; notebook()'
In case a specific example (e.g. the Streaming Kaggle example) does not work, you may need to install extra packages similar to those described in apt.txt or execute further steps on the commandline similar to those described in postBuild.
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