nodejs-polars VS skia-canvas

Compare nodejs-polars vs skia-canvas and see what are their differences.

skia-canvas

A GPU-accelerated 2D graphics environment for Node.js (by samizdatco)
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nodejs-polars skia-canvas
2 3
316 1,635
7.0% -
8.4 0.0
13 days ago about 2 months ago
TypeScript Rust
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

nodejs-polars

Posts with mentions or reviews of nodejs-polars. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-17.

skia-canvas

Posts with mentions or reviews of skia-canvas. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-17.

What are some alternatives?

When comparing nodejs-polars and skia-canvas you can also consider the following projects:

ibis - the portable Python dataframe library

undici - An HTTP/1.1 client, written from scratch for Node.js

arquero - Query processing and transformation of array-backed data tables.

html2canvas - Screenshots with JavaScript

falcon - Brushing and linking for big data

proposal-async-iterator-helpers - Methods for working with async iterators in ECMAScript

proposal-array-from-async - Draft specification for a proposed Array.fromAsync method in JavaScript.

proposal-arraybuffer-base64 - TC39 proposal for Uint8Array<->base64/hex

parquet-wasm - Rust-based WebAssembly bindings to read and write Apache Parquet data

proposal-zero-copy-arraybuffer-li

proposal-resizablearraybuffer - Proposal for resizable array buffers

functime - Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.