Julia vs R/Python

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/datascience

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  • crystal

    The Crystal Programming Language

    But there's also a pretty unknown underdog called Crystal Lang.

  • icrystal

    Jupyter kernel for the Crystal language

    There are some data libraries as well (i.e. here and you can use it within Jupyter as well.

  • Scout APM

    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • csvzip

    A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.

    Some people are simply converting traditional Python libraries to Crystal to get a performance boost (even for 'simpler' things like csvzip).

  • db-benchmark

    reproducible benchmark of database-like ops

  • diffeqpy

    Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

    10-100x speed increase was not an exaggeration for me. With julia I was able to run things quickly on my own machine which I had been running on a compute cluster. I agree that numba could be just as fast as julia. I also just saw that you can run that DE library from julia that I like so much from python using this package. https://github.com/SciML/diffeqpy

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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