# Show & Tell: Measuring the Authoring Complexity of Polars Queries in Dash Apps **URL:** \ **Category:** Dash Python\ **Tags:** show-and-tell\ **Created:** [June 24, 2026, 11:12am UTC](https://community.plotly.com/t/show-tell-measuring-the-authoring-complexity-of-polars-queries-in-dash-apps/97172 "2026-06-24T11:12:49Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![fran6wol](https://sea2.discourse-cdn.com/flex024/user_avatar/community.plotly.com/fran6wol/32/11148_2.png) [@fran6wol](https://community.plotly.com/u/fran6wol)\ **Post date:** [June 24, 2026, 11:12am UTC](https://community.plotly.com/t/show-tell-measuring-the-authoring-complexity-of-polars-queries-in-dash-apps/97172/1 "2026-06-24T11:12:49Z")
Polars is a fast-growing DataFrame library and an increasingly popular alternative to pandas, see Plotly’s blog: [How Polars Can Help You Build Fast Dash Apps for Large Datasets](https://plotly.com/blog/polars-to-build-fast-dash-apps-for-large-datasets/) I built a lightweight tool that estimates the **authoring complexity** of Polars `LazyFrame` queries; that is, the cognitive effort required to write and to understand a query. The library parses the unoptimized logical plan returned by `LazyFrame.explain(optimized=False)` and computes a complexity score and tier. Because it’s lightweight and has no heavy dependencies, it integrates smoothly into Dash applications via a simple context manager. Repo: [GitHub - fran6w/polars-query-dev-complexity: A lightweight tool for measuring the \*\*authoring complexity\*\* of Polars LazyFrame queries. · GitHub](https://github.com/fran6w/polars-query-dev-complexity) The repository includes: - a README showing how to integrate it with Dash; - a standalone demo; - a runnable implementation on TPC-H benchmark queries. I’d love to hear feedback from the community, especially on potential Dash use cases or ideas for improving the scoring model.