{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Pint-Pandas adds physical unit support to pandas DataFrames, allowing you to store, manipulate, and compute with quantities that have units (like torque in lbf\u00b7ft or angular velocity in rpm) directly within DataFrame columns.","skillfed_tags":["unit-conversion","scientific-computing"],"use_cases":["Store and manipulate engineering measurements (force, torque, velocity) in a DataFrame while preserving unit information throughout calculations.","Perform dimensional analysis on scientific datasets to catch unit mismatches early and ensure results have the correct derived units.","Build data pipelines for physics simulations or experimental data where automatic unit propagation reduces manual conversion overhead.","Generate reports with physical quantities that display units alongside values, improving clarity and reducing interpretation errors."],"what_it_does":"Pint-Pandas bridges pandas DataFrames and the Pint unit library, letting you define DataFrame columns with physical units (e.g., \"pint[lbf ft]\" for torque) and perform arithmetic operations that propagate and combine units correctly. When you multiply a torque column by an angular velocity column, the result automatically carries the correct combined unit. This eliminates the need to track units separately or convert everything to a common base unit before analysis.\n\nThe package is built on top of pandas' extension array system, so unit-aware columns behave like standard DataFrame columns in most contexts. It's aimed at scientific and engineering workflows where dimensional consistency matters\u2014simulations, data analysis, and reporting where mixing incompatible units would be a silent error without this library.","worth_installing":"Yes, if you work with physical quantities in pandas. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The package solves a real problem\u2014unit tracking in data analysis\u2014that would otherwise require manual bookkeeping or separate unit columns. Suitable for scientific, engineering, and research workflows where dimensional consistency is important."},"id":"pint-pandas","links":{"html":"https://skillfed.io/packages/pint-pandas","md":"https://skillfed.io/packages/pint-pandas.md","pypi":"https://pypi.org/project/pint-pandas/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-20","license_spdx":null,"license_treatment":"permissive","name":"Pint-Pandas","python_support":"supports_current","summary":"Extend Pandas Dataframe with Physical quantities module"},"popularity":{"monthly_downloads":507903,"position":6282,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}