AstraBert

AstraBert / sunbears

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A CSV data loader for TypeScript with an API similar to Polars and Pandas, written in pure Rust.

csv data-science dataframe napi-rs pandas
20
1
100% credibility
Found Apr 10, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Sunbears provides a fast, TypeScript-friendly way to read, write, and manipulate CSV data in a structured table format similar to popular data tools.

How It Works

1
🔍 Discover sunbears

You hear about a speedy tool for loading and working with CSV files like spreadsheets in your JavaScript projects.

2
📦 Add it to your project

Bring the tool into your setup with your usual package helper, ready to use right away.

3
📊 Load your CSV data

Choose a CSV file and watch it turn into an easy-to-use data table full of columns.

4
👀 Peek at your columns

Check what kind of info each column holds, like numbers or words, and grab what you need.

5
🧹 Fix missing spots

Tidy up empty or invalid values in your data, or save the cleaned table back to a file.

🚀 Speed through big files

Handle huge datasets lightning-fast, making your data work feel effortless and quick.

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Star Growth

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AI-Generated Review

What is sunbears?

Sunbears brings Polars- and Pandas-like DataFrame handling to TypeScript for CSV data format processing. You load a CSV datei with readCsv, get a typed DataFrame that infers column types (string, int, float, bool), extracts arrays via helpers like asIntArray, and handles nulls/NaNs with drop or fill methods before writing back via writeCsv. Built in pure Rust for Node.js via NAPI, it solves slow JS CSV parsing for large files without leaving TypeScript.

Why is it gaining traction?

Its Rust core crushes JS alternatives like csv-parse—reading 1M rows in 0.3s vs. 1.2s—while nearing Polars speeds on benchmarks for csv github parser tasks. Devs dig the familiar API for quick csv datei erstellen, csv datei in excel importieren, or csv to json flows, plus null/NaN ops that chain into array filters/maps. Sun bears active traction comes from sub-millisecond small-file loads and no Python deps.

Who should use this?

Node.js backend devs ETLing csv github samples or github csv viewer exports. Data pipeline builders converting csv datei in excel umwandeln for web apps. TS scripters handling million-row csv datei öffnen without spawning Polars.

Verdict

Grab it for perf-critical CSV reader# github needs—benchmarks and tests impress—but 18 stars and 1.0% credibility scream early alpha; docs are solid, await more adoption before prod. (198 words)

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