Repo Compare › Dataframes

pandas vs Polars vs DuckDB

A side-by-side look at how actively pandas, Polars and DuckDB are developed on GitHub — popularity, contributor base, commit activity and how quickly issues and pull requests move.

What the numbers say

Head-to-head

Snapshot taken 30 September 2026.

Metricpandas
Active
Polars
Active
DuckDB
Active
Stars50k40k42k
Forks20k3.1k3.8k
Contributors4.3k776903
Commits, last 52 weeks2.7k2.4k23k
Open issues2.2k2.6k592
Open pull requests251371351
Closed / merged PRs39k16k14k
Watchers1.1k221279
Last push30 Sep 202630 Sep 202630 Sep 2026
Latest releasev3.0.6 (Sep 2026)py-1.44.2 (Sep 2026)v1.5.6 (Sep 2026)
Main languagePythonRustC++
LicenceBSD-3-ClauseMITMIT
CreatedAug 2010May 2020Jun 2018

The wide bar is that repository’s share of the largest value in its own row — half the length means half the number. The thin gauge beneath it is the same row rescaled from its smallest value to its largest, which makes a close gap such as 61k against 59k visible; on that gauge the row’s lowest value always sits at zero however big it is, so read it as a spread and never as a quantity. Bars compare repositories with each other and never one metric against another. Colour identifies the repository, matching the dot beside its name. On the two issue rows a longer bar means more open work, which is usually the opposite of better. The highlighted cell is the highest value in a row. “Active / Slowing / Dormant” is a simple heuristic from how recently code was pushed and open issues per star — a starting point, not a verdict. Stars measure popularity, not quality.

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