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https://github.com/hodovani/awesome-javascript-data-science

Awesome JavaScript Data Science
https://github.com/hodovani/awesome-javascript-data-science

List: awesome-javascript-data-science

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Awesome JavaScript Data Science

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README 

        
# Awesome JavaScript Data Science [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



A curated list of awesome libraries for doing data science in your

browser or Node.js environment. That doesn't just mean JavaScript - thanks to WebAssembly, many data

science libraries from other languages are now available in the browser.



If you want to contribute to this list (please do), create a pull request.



## Contents



- [Environments](#environments)

- [Data Formats](#data-formats)

- [Data Munging](#data-munging)

- [Math and Statistics](#math-and-statistics)

- [Machine learning](#machine-learning)

- [Natural Language Processing](#natural-language-processing)

- [Deep Learning](#deep-learning)

- [Visualization](#visualization)

- [Other languages](#other-languages)



---



## Environments



- [HASH](https://hash.ai/) - Create and run multi-agent simulations in your browser.

- [Observable](https://observablehq.com/) - The magic notebook for Exploring Data.

- [Runkit](https://runkit.com/home) - A Node Playground in your Browser.

- [Iodide](https://alpha.iodide.io/) - Lets you do data science entirely in your browser.

- [Carbide](https://alpha.trycarbide.com) - A Reactive JavaScript programming environment.

- [Kaggle Notebooks](https://www.kaggle.com/kernels) - Run analyses on Google Cloud using Python or R.

- [Starboard Notebooks](https://starboard.gg/) - Interactive JavaScript notebooks in your browser with minimal setup.  



---



## Data Formats



- [Papa Parse](https://www.papaparse.com/) - Powerful, in-browser CSV parser.

- [js-xlsx](https://github.com/SheetJS/js-xlsx) - Parser and writer for various spreadsheet formats.

- [Apache Arrow](https://github.com/apache/arrow/tree/master/js) - Enable big data systems to process and transfer data quickly.



---



## Data Munging



- [sql.js](https://github.com/kripken/sql.js/) - SQLite compiled to JavaScript through Emscripten.

- [Lodash](https://lodash.com/) - A modern JavaScript utility library delivering modularity, performance & extras.

- [jq-web](https://github.com/fiatjaf/jq-web) - The command-line JSON processor, compiled with emscripten and exposed as JavaScript library.

- [datalib](http://vega.github.io/datalib/) - A JavaScript data utility library.

- [zebras](https://github.com/nickslevine/zebras) - A data manipulation and analysis library written in JavaScript offering the convenience of pandas or R.

- [Danfojs](https://github.com/opensource9ja/danfojs) - Powerful JavaScript data analysis toolkit.

- [duckdb-wasm](https://github.com/duckdb/duckdb-wasm) - DuckDB compiled to WebAssembly, letting you run fast SQL queries locally in the browser or Node.js.  

- [Polars (WASM)](https://github.com/pola-rs/polars/tree/master/py-polars/polars-wasm) - Polars, a lightning-fast DataFrame library in Rust, compiled to WebAssembly for use in JS environments.



---



## Math and Statistics



- [mathjs](https://mathjs.org/) - An extensive math library for JavaScript and Node.js.

- [bluemath](https://github.com/bluemathsoft/bluemath) - Math kernel in JavaScript.

- [libRmath.js](https://github.com/jacobbogers/libRmath.js/) - JavaScript Pure Implementation of Statistical R "core" numerical libRmath.so.

- [stdlib](https://github.com/stdlib-js/stdlib) - A standard library for JavaScript, with an emphasis on numerical and scientific computing applications.

- [Simple Statistics](https://simplestatistics.org/) - Statistical methods in readable JavaScript for browsers, servers, and people.

- [jStat](http://jstat.github.io/) - Perform advanced statistical operations.

- [BigInteger.js](https://github.com/peterolson/BigInteger.js) - Is an arbitrary-length integer library for JavaScript.

- [Fraction.js](https://github.com/infusion/Fraction.js) - Is a rational number library written in JavaScript.

- [fermat.ts](https://github.com/mathigon/fermat.js) - Powerful mathematics and statistics library, containing everything from number theory to random numbers and linear algebra classes.

- [Simple Statistics](https://github.com/simple-statistics/simple-statistics) - A JavaScript implementation of descriptive, regression, and inference statistics.



---



## Machine learning



- [mljs](https://github.com/mljs/ml) - Machine learning tools in JavaScript.

- [machinelearn.js](https://www.machinelearnjs.com/) - Machine Learning library for the web and Node.

- [ONNX Runtime Web](https://onnxruntime.ai/docs/api/javascript/) - Run ONNX models in browsers (via WebGL/WebAssembly) and Node.js.



---



## Natural Language Processing



- [Compromise](https://github.com/spencermountain/compromise) - Modest natural-language processing.

- [Natural](https://github.com/NaturalNode/natural) - General natural language facilities for node.

- [node-nlp](https://github.com/axa-group/nlp.js##readme) - A Fork of Natural with many additional capabilities.

- [sentiment](https://github.com/thisandagain/sentiment) - AFINN-based sentiment analysis for Node.js.

- [compromise](http://compromise.cool/) - Interprets and pre-parses English.

- [wink](https://winkjs.org/) - Open Source packages for NLP, ML and Statistics in Node JS to build production grade solutions.

- [twitter-text-js](https://github.com/twitter/twitter-text/tree/master/js) - A JavaScript utility that provides text processing routines for Tweets.

- [Knwl.js](https://github.com/benhmoore/Knwl.js) - Find Dates, Places, Times, and More. A .js library for parsing text for specific information.

- [Talisman](http://yomguithereal.github.io/talisman/) - A straightforward & modular NLP, machine learning & fuzzy matching library for JavaScript.

- [Franc](https://github.com/wooorm/franc) - Natural language detection.

- [Underscore.string](http://epeli.github.io/underscore.string/) - Not actually an NLP library, but a useful toolkit for working with strings in JavaScript.

- [transformers.js](https://github.com/xenova/transformers.js) - Run Hugging Face Transformer models directly in JavaScript (browser or Node.js) using WebAssembly acceleration.



---



## Deep Learning



- [TensorFlow.js](https://www.tensorflow.org/js) - TensorFlow.js is a library for developing and training ML models in JavaScript, and deploying in browser or on Node.js.

- [ml5](https://ml5js.org/) - Friendly Machine Learning for the Web.

- [WebDNN](https://mil-tokyo.github.io/webdnn/) - Fastest DNN Execution Framework on Web Browser.

- [brain.js](https://brain.js.org/) - Neural networks in JavaScript.

- [torch-js](https://github.com/pytorch/torch-js) - An experimental port of the PyTorch API to JavaScript/TypeScript for Node.js (still in early development).  

- [wasm-torch](https://github.com/abetlen/wasm-torch) - A WebAssembly port of PyTorch's C++ backend, enabling inference in the browser or Node.



---



## Visualization



- [D3](https://d3js.org) - Data-driven documents.

- [C3.js](https://c3js.org/) - D3-based reusable chart library.

- [Vega](https://vega.github.io/vega/) - A Visualization Grammar.

- [Plotly.js](https://plot.ly/JavaScript/) - General-purpose data visualization.

- [Nivo](https://nivo.rocks/) - A rich set of dataviz components, built on top of the awesome d3 and Reactjs libraries.

- [Chart.js](https://www.chartjs.org/) - Simple yet flexible JavaScript charting for designers & developers.

- [Sigmajs](http://sigmajs.org/) - A JavaScript library dedicated to graph drawing.

- [Falcon](https://github.com/uwdata/falcon) - Interactive Visual Analysis for Big Data. Crossfilter millions of records without latencies.

- [Apache ECharts](https://echarts.apache.org/) - A powerful, highly customizable charting and visualization library, suitable for large datasets.  

- [Observable Plot](https://github.com/observablehq/plot) - A high-level, experimental library for exploratory data visualization, from the creators of Observable.



---



## Other languages



- [Pyodide](https://github.com/iodide-project/pyodide) - The scientific Python stack, compiled to WebAssembly.

- [PyScript](https://pyscript.net/) - A framework that allows you to run Python in the browser, built on top of Pyodide and WebAssembly.