{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"linopy builds linear, integer, and quadratic optimization models using labeled n-dimensional arrays, bridging xarray data structures with external solvers.","skillfed_tags":["optimization","linear-programming","xarray-native"],"use_cases":["Energy system planning: optimize generation and storage across time and regions with coordinate-based constraints.","Supply chain optimization: define inventory, production, and distribution variables indexed by product, location, and time period.","Resource allocation: minimize cost or maximize utility subject to constraints on budget, capacity, or demand across labeled dimensions.","Scheduling problems: model shift assignments or task scheduling using time and agent coordinates with linear constraints.","Portfolio optimization: construct investment problems with asset and time-period coordinates and quadratic objectives."],"what_it_does":"linopy is a Python optimization modeling framework that lets you define linear, integer, mixed-integer, and quadratic programming problems using labeled n-dimensional arrays. It sits between data-handling libraries like xarray and external solvers, allowing you to express optimization constraints and objectives in a way that mirrors your data's natural structure\u2014with coordinates like time, location, or consumer IDs\u2014rather than flattening everything into matrices.\n\nYou define variables with bounds and coordinates, build constraints and objectives using arithmetic operations on those variables (with full broadcasting support), and then hand the model off to an external solver of your choice. linopy handles the translation to the solver's format and can import/export models via netCDF. It supports lazy evaluation with dask for large problems and is designed to make the optimization model transparent and easy to modify on the fly.","worth_installing":"Yes. linopy is actively maintained, has low install friction, carries no known vulnerabilities, and offers a genuinely useful abstraction for optimization problems that naturally live in labeled arrays. Install it if you need to build optimization models with xarray-like data structures or if you want to avoid hand-coding matrix construction for multi-dimensional problems. The trade-off is that you must install a solver separately; linopy alone is only the modeling layer."},"id":"linopy","links":{"html":"https://skillfed.io/packages/linopy","md":"https://skillfed.io/packages/linopy.md","pypi":"https://pypi.org/project/linopy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-24","license_spdx":null,"license_treatment":"permissive","name":"linopy","python_support":"supports_current","summary":"Linear optimization with N-D labeled arrays in Python"},"popularity":{"monthly_downloads":134199,"position":11485,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}