{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Bridges pandas DataFrames and scikit-learn by mapping DataFrame columns to transformations and recombining them into features for machine learning pipelines.","skillfed_tags":["pandas-sklearn-bridge","feature-engineering","abandoned"],"use_cases":["Preprocessing mixed-type DataFrames by applying different transformers to different columns before feeding them to a classifier","Building reproducible feature engineering pipelines that map raw DataFrame columns through standardization and encoding in a single object","Tracking which original DataFrame columns produced which transformed features for model interpretability and debugging","Dynamically selecting columns at fit time using callables to handle datasets with unknown or variable column sets"],"what_it_does":"sklearn-pandas provides a DataFrameMapper class that acts as a bridge between pandas DataFrames and scikit-learn's transformers. Instead of manually extracting columns, applying transformations, and reassembling arrays, you define a list of (column_selector, transformer, options) tuples, and the mapper handles the plumbing\u2014selecting columns from your DataFrame, passing them through sklearn transformers, and combining the results back into a feature matrix.\n\nThe mapper supports flexible column selection (single columns, multiple columns, or callable selectors), custom naming of output features via aliases, prefixes, and suffixes, and automatic tracking of transformed feature names. It integrates directly into sklearn pipelines and works with both fit and transform operations. The four runtime dependencies\u2014scikit-learn, scipy, pandas, and numpy\u2014are standard in the ML stack, so installation is straightforward.","worth_installing":"No. The project is abandoned (last release 2021-05-08, last commit 2023-06-08) with no active maintenance. While the MIT License is permissive and install friction is low, the lack of updates means it may break with newer versions of scikit-learn, pandas, or numpy. For new projects, consider actively maintained alternatives."},"id":"sklearn-pandas","links":{"html":"https://skillfed.io/packages/sklearn-pandas","md":"https://skillfed.io/packages/sklearn-pandas.md","pypi":"https://pypi.org/project/sklearn-pandas/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-05-08","license_spdx":null,"license_treatment":"permissive","name":"sklearn-pandas","python_support":"unspecified","summary":"Pandas integration with sklearn"},"popularity":{"monthly_downloads":205933,"position":9580,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.0"}