{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Polars-ds adds data science and machine learning operations to Polars dataframes, including linear regression, logistic regression, statistical tests, string and array distances, and feature engineering transforms.","skillfed_tags":["polars-extension","statistical-modeling","feature-engineering"],"use_cases":["Compute ML evaluation metrics (ROC-AUC, log-loss) in parallel across data segments using group_by.","Fit and predict with linear or logistic regression inline within a Polars expression without materializing intermediate tables.","Generate polynomial features and produce statsmodels-style regression summaries with coefficients, p-values, and confidence intervals.","Calculate string edit distances (Levenshtein, Jaro-Winkler) or array distances (squared L2) for fuzzy matching or similarity scoring.","Run statistical hypothesis tests (t-test, chi-squared, F-test) grouped by category within a single lazy query."],"what_it_does":"Polars-ds is a Polars extension that brings data science and statistical modeling directly into lazy dataframe expressions. It lets you compute ML metrics, fit regression models, run statistical tests, and calculate string/array distances all within Polars' query engine, avoiding intermediate data copies. The package covers linear regression (with L1/L2 regularization), logistic regression, rolling and recursive regression, statistical tests (t-test, chi-squared, F-test), string distances (Levenshtein, Jaro-Winkler, OSA), and array distances. Models are non-persistent\u2014fit and predict in a single expression\u2014and work alongside Polars' group_by and lazy evaluation.\n\nIt requires Python 3.9+ and depends only on polars and typing-extensions. The package is written in Rust for performance and ships as compiled wheels for macOS (x86_64 and arm64), Linux (x86_64 and aarch64), and Windows. It is in Beta status and actively maintained.","worth_installing":"Yes, if you work with Polars and need inline statistical modeling or ML metrics. The permissive MIT license, minimal dependencies, and active maintenance make it low-risk. Medium install friction (compiled wheels) is typical for performance-critical packages. Beta status is acceptable for exploratory or non-critical workflows; verify stability requirements for production use."},"id":"polars-ds","links":{"html":"https://skillfed.io/packages/polars-ds","md":"https://skillfed.io/packages/polars-ds.md","pypi":"https://pypi.org/project/polars-ds/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-01","license_spdx":null,"license_treatment":"permissive","name":"polars-ds","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":177847,"position":10205,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}