{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"DataSieve extends scikit-learn's Pipeline to handle row and feature transformations while propagating changes across X, y, and sample_weight arrays\u2014enabling outlier removal, feature selection, and dimensionality reduction in a coordinated pipeline.","skillfed_tags":["data-preprocessing","outlier-detection","pipeline-extension"],"use_cases":["Remove outliers from training data while keeping y and sample_weight synchronized for supervised learning.","Apply feature selection or dimensionality reduction and automatically track feature name changes.","Build multi-stage preprocessing pipelines that filter rows based on complex criteria across multiple arrays.","Flag anomalies in new data without removing them, returning both transformed features and outlier indicators.","Wrap transforms in a coordinated pipeline that handles weighted or imbalanced datasets with row-level filtering."],"what_it_does":"DataSieve is a Pipeline extension that coordinates transformations across feature arrays (X), target arrays (y), and sample weights simultaneously. Unlike standard pipelines that only transform X, DataSieve propagates row removals and feature modifications through all three arrays in lockstep\u2014so when an outlier detection step removes rows from X, it automatically removes the corresponding rows from y and sample_weight.\n\nThe package includes built-in transforms for common tasks like variance-based feature filtering, SVM-based outlier detection with automatic removal, and PCA with feature renaming. It supports wrapping transforms directly, and allows custom transforms that manipulate any combination of X, y, and sample_weight. A key feature is outlier flagging without removal: you can fit a pipeline and call transform with outlier_check=True to get a binary vector marking outliers while keeping all data intact.","worth_installing":"Yes, if you need to coordinate row or feature removals across X, y, and sample_weight in a single pipeline. The low install friction, permissive license, and absence of known vulnerabilities make it safe to try. However, the aging maintenance status (460 days since last release) means you should verify compatibility with your specific pandas and scikit-learn versions before relying on it in production."},"id":"datasieve","links":{"html":"https://skillfed.io/packages/datasieve","md":"https://skillfed.io/packages/datasieve.md","pypi":"https://pypi.org/project/datasieve/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-11","license_spdx":null,"license_treatment":"permissive","name":"datasieve","python_support":"supports_current","summary":"This package implements a flexible data pipeline to help organize row removal (e.g. outlier removal) and feature modification (e.g. PCA)"},"popularity":{"monthly_downloads":214114,"position":9424,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.9"}