Gaurav-Gosain

Gaurav-Gosain / golars

Public

Pure-Go DataFrames modeled on polars, built on Apache Arrow. No cgo.

golars.gaurav.zip
11
2
100% credibility
Found Apr 25, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

Golars is a fast, pure-Go library for DataFrame analysis like Polars, with terminal tools for browsing data, running SQL queries, scripting pipelines, and Jupyter notebook support.

How It Works

1
🔍 Discover golars

You hear about a friendly tool that lets you explore and analyze data files like spreadsheets without needing complex software.

2
📥 Get it set up

Run a simple command to install it on your computer, like grabbing a new app.

3
📁 Open your data file

Tell it which CSV or data file you want to look at, and it loads right away.

4
🖥️ Browse and peek around

Use the interactive viewer to scroll through rows, sort columns, and spot patterns like flipping pages in a book.

5
⚡ Query with simple questions

Type natural questions like 'sum sales by department' or run quick scripts to get instant answers in neat tables.

🎉 Unlock your data insights

You quickly find what you need, export results, or build reports, feeling like a data wizard without the hassle.

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Star Growth

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AI-Generated Review

What is golars?

Golars delivers pure-Go DataFrames modeled on Polars, built on Apache Arrow with no cgo dependencies. Go developers get eager and lazy APIs for filtering, grouping, joins, and aggregations, plus a full CLI suite for SQL queries, TUI browsing, file diffs, format conversion, and scripting in a pipe-friendly .glr dialect. It handles CSV, Parquet, Arrow, JSON, and more, with Arrow-native interop to Polars, PyArrow, or DuckDB.

Why is it gaining traction?

This pure-Go GitHub project stands out by matching or beating Polars 1.39 on benchmarks like groupby, joins, and filters, thanks to AVX2/NEON kernels and a plan optimizer—all cross-compiled with one go build. Users love the zero-install terminal stack: REPL, LSP for editors, Jupyter kernel, and TUI browser that scales to millions of rows without copying data. No Rust crate or Python env needed; just drop binaries on PATH for instant data wrangling.

Who should use this?

Data engineers building ETL pipelines in Go services, where Python deps slow deploys. Analysts scripting quick SQL or transforms on CSVs/Parquets via CLI. Teams embedding fast DataFrames in tools, avoiding cgo linkage issues.

Verdict

Early alpha (11 stars, 1.0% credibility) with strong docs, tests, and benches, but light on real-world users—trial it for pure-Go data needs. Solid foundation; watch for v1.

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