{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Provides a lightweight compatibility layer that lets you write dataframe code once and run it against pandas, Polars, or any other library implementing the Python Dataframe API Standard.","skillfed_tags":["dataframe-abstraction","cross-library-compat"],"use_cases":["Write dataframe transformation code that works with both pandas and Polars without conditional imports or adapter code.","Build a library or tool that accepts dataframes from multiple sources and processes them with a single code path.","Prototype analysis with pandas, then swap to Polars for production performance without refactoring your dataframe calls.","Support multiple dataframe backends in a data pipeline without maintaining separate code branches for each."],"what_it_does":"Dataframe API Compat is a thin abstraction layer that sits between your code and dataframe libraries like pandas and Polars, allowing you to write dataframe operations using the Python Dataframe API Standard. Instead of learning library-specific syntax, you write against a common interface that any compliant dataframe library can implement. The package itself is intentionally minimal\u2014under 50 kB, pure Python, with only packaging as a dependency\u2014making it easy to add to any project without bloat.\n\nThe main use case is writing library-agnostic dataframe code: you can prototype with pandas, switch to Polars for performance, or support multiple backends without rewriting your logic. It's designed for teams or libraries that want to avoid lock-in to a single dataframe implementation while still using standard, predictable operations.","worth_installing":"Yes, if you need to write dataframe code that works across multiple libraries or want to avoid lock-in to a single implementation. The low install friction and permissive license make it a safe addition. However, maintenance is dormant (no commits since April 2024), so verify that the standard implementation covers your actual use cases and that the libraries you target are fully compliant before committing to it in production."},"id":"dataframe-api-compat","links":{"html":"https://skillfed.io/packages/dataframe-api-compat","md":"https://skillfed.io/packages/dataframe-api-compat.md","pypi":"https://pypi.org/project/dataframe-api-compat/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-04-26","license_spdx":null,"license_treatment":"permissive","name":"dataframe-api-compat","python_support":"supports_current","summary":"Implementation of the DataFrame Standard for pandas and Polars"},"popularity":{"monthly_downloads":122128,"position":11959,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.7"}