{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/2"}],"enrichment":{"capability":"Registers custom methods and accessors on Pandas DataFrames, Series, and GroupBy objects, extending Pandas with domain-specific functionality without modifying Pandas itself.","skillfed_tags":["pandas-extension","decorator-pattern"],"use_cases":["Build a data-cleaning library that adds methods like `.clean_column_names()` or `.remove_duplicates()` directly to DataFrames.","Create domain-specific accessors (e.g., `.bio.translate()` for bioinformatics) that namespace related methods without polluting the DataFrame API.","Extend GroupBy objects with custom aggregation or transformation methods for specialized analytics workflows.","Develop internal company data tools that add proprietary methods to Pandas objects for consistent, discoverable APIs.","Register Series methods for specialized data types or transformations (e.g., `.text.clean()` for text processing)."],"what_it_does":"Pandas Flavor provides decorators to register custom methods and accessors on Pandas objects\u2014DataFrames, Series, and GroupBy\u2014without forking or modifying Pandas itself. Instead of writing utility functions that operate on Pandas objects from the outside, you use decorators like `@register_dataframe_method` or `@register_dataframe_accessor` to attach your functions directly to the object, so they appear as native methods or namespaced accessor attributes.\n\nThe package originated to backport Pandas' extension API to older versions, but has evolved into a tool for building domain-specific Pandas extensions. It is used by projects like pyjanitor and pdvega to add specialized data-cleaning and visualization methods. The library handles the subtle work of creating accessor classes that mimic method behavior, preserving docstrings and call semantics, so your custom code integrates cleanly into Pandas workflows.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, imposes minimal install friction, and solves a real problem: extending Pandas cleanly without monkeypatching or external wrapper functions. Use it if you are building a Pandas-based library or tool that needs to add custom methods or accessors. The MIT license carries no restrictions."},"id":"pandas-flavor","links":{"html":"https://skillfed.io/packages/pandas-flavor","md":"https://skillfed.io/packages/pandas-flavor.md","pypi":"https://pypi.org/project/pandas-flavor/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-22","license_spdx":null,"license_treatment":"permissive","name":"pandas-flavor","python_support":"supports_current","summary":"The easy way to write your own Pandas flavor"},"popularity":{"monthly_downloads":1089726,"position":4380,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.8.1"}