Interactive C++ for Data Science

This page summarizes the projects mentioned and recommended in the original post on

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  • Pluto.jl

    🎈 Simple reactive notebooks for Julia

    This is one of the selling points of Pluto.jl for Julia... less hidden state for interactive coding.

  • examples

    Pre-built mlpack models (by mlpack)

    mlpack, a C++ machine learning library, includes xeus-cling notebooks directly on their homepage:

    The xeus-cling work is awesome and has made it possible to do data science prototyping in C++. There are lots of other C++ notebook examples in the examples repository:

  • 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.

  • xeus-cling

    Jupyter kernel for the C++ programming language

    Glad they fixed this issue: which made me stop trying years ago. I would like to try again. Have been happy using Julia since then.

  • transformers

    🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

    Just a random example I encountered the other day, take a look at this:

    It’s a tutorial on how to use some NLP ML library. It’s a pretty excellent example of where this format shines, imho, and as you can see it’s absolutely literate.

    Btw I also identify as primarily a developer, but over the past few years been confronted with these notebooks more and more and I’ve learned to appreciate their use over time. But as with all good things, they’re sometimes overused.

  • tinyspec-cling

    tiny spectral synthesizer with livecoding support

    Here's a live coding music synth based on Cling

  • preplish

    A Perl 5 REPL written in Bash

    It is Jupyter is a Rube-Goldbergian nightmare. Python is a memory hog. There are better solutions, to be sure.

    A simple REPL is all that's needed to both do A-type and B-type data exploration. (I won't use the term "data scientist", it's an exaggeration in most cases.)

    Python has a REPL, R has a REPL, Perl has PDL and both a simple REPL ( and a more complex one (

    Jupyter should not be used as an IDE because it is the wrong tool for development. A-type data explorers just want a painless UI and may not care much about the horrible agglutination of incomplete/slow/broken solutions that Jupyter represents.

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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