Show & Tell: Measuring the Authoring Complexity of Polars Queries in Dash Apps

Dash Python
1

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

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

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.

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