{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Atmospheric Science","url":"https://skillfed.io/packages/category/scientific-engineering-atmospheric-science"}],"enrichment":{"capability":"MetPy provides tools for reading, visualizing, and performing calculations on weather data, integrating with the scientific Python ecosystem (NumPy, SciPy, Matplotlib).","skillfed_tags":["meteorology","atmospheric-science","geospatial"],"use_cases":["Plot weather maps with geographic projections and overlay meteorological fields from gridded data","Calculate thermodynamic indices from radiosonde or model output","Create Skew-T log-P diagrams for analyzing atmospheric stability and convection","Extract and reuse individual meteorological calculations in custom analysis scripts","Visualize cross-sections of atmospheric data with proper coordinate transformations","Process and analyze gridded datasets with numpy and xarray integration"],"what_it_does":"MetPy is a Python library for meteorological and atmospheric science workflows, designed to bring GEMPAK-like functionality into the scientific Python ecosystem. It handles the three core tasks of weather analysis: reading meteorological data, performing thermodynamic and kinematic calculations, and visualizing results on maps and specialized diagrams such as Skew-T plots. The library is built on top of numpy, scipy, matplotlib, and xarray, so it integrates naturally with existing scientific Python workflows.\n\nMetPy is intended for researchers, educators, and operational meteorologists who want to script weather analysis and visualization. Its design emphasizes modularity\u2014you can extract individual calculations and reuse them in your own applications\u2014and it prioritizes clear documentation and test coverage to ensure long-term maintainability. The package has been actively maintained since its first release in 2015 and follows semantic versioning, so code written for version 1.y will work on future 1.x releases.","worth_installing":"Yes. MetPy is a mature, actively maintained library with no known vulnerabilities, low install friction, and a permissive license. It fills a clear niche for meteorological analysis in Python and integrates well with the broader scientific ecosystem. Install it if you work with weather or atmospheric data."},"id":"metpy","links":{"html":"https://skillfed.io/packages/metpy","md":"https://skillfed.io/packages/metpy.md","pypi":"https://pypi.org/project/metpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-29","license_spdx":null,"license_treatment":"permissive","name":"MetPy","python_support":"supports_current","summary":"Collection of tools for reading, visualizing and performing calculations with weather data."},"popularity":{"monthly_downloads":221366,"position":9281,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.7.1"}