4 ms·

> Julia may be more interesting (and it offers types!) plus much more focus om performance for iterative operations (which Haskell is lacking). Though, each tim

> Julia may be more interesting (and it offers types!) plus much more focus om performance for iterative operations (which Haskell is lacking). Though, each time I tried it, I had to go back to Python - again, due to maturity of its data science ecosystem. I definitely agree that the Julia ecosystem is still maturing and not everything is as developed, however I've often found that the lack of maturity is less of an issue in Julia because the flexability of the language through generics, parametric types and macros makes a lot of the infrastructure needed in Python unessessary in Julia. Obviously your milage may vary in this regard though.

nextos 8y ago
Same here, Julia's lack of a very broad package ecosystem is less of an issue because i) it's very easy to interface with R, Python and C; ii) types and multiple dispatch plus basic libraries and efficient code generation make it quick to develop good number crunching code. Furthermore, the ecosystem is growing quickly, and it already has some unique math & stats packages not found elsewhere. Real gems. See https://discourse.julialang.org/t/what-package-s-are-state-of-the-art-or-attract-you-to-julia-and-make-you-stay-there-not-easily-replicateable-in-e-g-python-r-matlab/11294 https://discourse.julialang.org/t/what-package-s-are-state-o...