A lightweight JavaScript library for data analysis with CSV reading, statistical methods, and chart plotting.
Datakit is a lightweight JavaScript library designed for data analysis tasks such as reading CSV files, performing statistical calculations, and creating basic charts. It provides essential tools for data manipulation and visualization directly in JavaScript environments, solving the need for a simple, dependency-free analysis toolkit.
JavaScript developers and data enthusiasts who need quick, in-browser or Node.js data analysis without the overhead of larger frameworks like Python's pandas or R.
Developers choose Datakit for its minimal footprint and ease of use, offering core data analysis functions in pure JavaScript with no external dependencies, making it ideal for prototyping, education, and lightweight applications.
A lightweight framework for data analysis in JavaScript.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The library is self-contained with no external packages, reducing bundle size and simplifying setup, as emphasized in its philosophy of avoiding complexity.
With dk.csv and dk.numeric, it easily reads CSV files and converts columns to numbers, including customizable fill values for missing data, as shown in the examples.
Provides essential statistical functions like mean, standard deviation, and simple linear regression (OLS), demonstrated with clear code snippets in the README.
The Chart class allows creating basic plots with configurable labels and multiple series, enabling rapid visualization without additional libraries.
Only covers elementary methods; lacks advanced analyses like hypothesis testing, clustering, or time series modeling, which are common in data science.
Visualizations are rudimentary with no support for interactive elements or advanced chart types beyond line and point plots, limiting professional use.
The README offers basic examples but lacks comprehensive API documentation, error handling guides, or performance considerations for edge cases.