{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/14"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Woodwork adds a typing layer to pandas DataFrames, letting you annotate columns with logical types and semantic tags for use in machine learning workflows and data science tools.","skillfed_tags":["dataframe-typing","ml-metadata"],"use_cases":["Annotate DataFrame columns with logical types and semantic tags before passing data to Featuretools for automated feature engineering.","Store and communicate column metadata (e.g., which columns are numeric, categorical, or contain personal names) for reproducible ML pipelines.","Filter and select DataFrame subsets by logical type or semantic tag without manual column name lists.","Standardize data typing across multiple DataFrames in a machine learning project to ensure consistent downstream processing.","Integrate with EvalML for automated machine learning workflows that rely on consistent column type information."],"what_it_does":"Woodwork is a data typing library that extends pandas DataFrames with a semantic layer for machine learning. It lets you assign logical types (Integer, Categorical, DateTime, NaturalLanguage, etc.) and semantic tags to columns, then query and filter your data based on those annotations. The library automatically infers types from underlying data when you don't specify them, and it stores metadata alongside your DataFrame for use in downstream ML workflows.\n\nThe package is designed as a common typing namespace for Featuretools and EvalML, so if you're using those tools, Woodwork provides a standardized way to communicate data structure and meaning. It depends on pandas, scikit-learn, scipy, numpy, python-dateutil, and importlib-resources\u2014all standard data-science libraries\u2014so installation is straightforward. The project is maintained by Alteryx but has not seen active development since mid-2024.","worth_installing":"Yes, if you are using Featuretools or EvalML and need a standardized way to annotate and manage DataFrame column types. The low install friction and permissive license make it a straightforward addition. However, be aware that the project is aging\u2014last release was May 2024 and there have been no commits since September 2025\u2014so verify compatibility with your current versions of pandas and scikit-learn before relying on it for new projects."},"id":"woodwork","links":{"html":"https://skillfed.io/packages/woodwork","md":"https://skillfed.io/packages/woodwork.md","pypi":"https://pypi.org/project/woodwork/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-05-14","license_spdx":null,"license_treatment":"permissive","name":"woodwork","python_support":"supports_current","summary":"a data typing library for machine learning"},"popularity":{"monthly_downloads":170445,"position":10394,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.31.0"}