{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"Vector provides 2D, 3D, and 4D space-time vector operations optimized for working with arrays of vectors rather than individual vectors in loops, with support for multiple coordinate systems and data backends.","skillfed_tags":["hep-physics","spatial-math","array-computing"],"use_cases":["Perform batch geometric calculations on arrays of 3D points or vectors in scientific simulations.","Work with 4D space-time vectors and Lorentz transformations in high-energy physics analysis.","Convert between coordinate systems (Cartesian, polar, pseudorapidity) for physics calculations.","Integrate vector operations into Numba-compiled functions for performance-critical code paths.","Manipulate nested or ragged vector data structures using Awkward Arrays backend.","Perform symbolic vector algebra using SymPy for analytical physics derivations."],"what_it_does":"Vector is a Python library for working with 2D, 3D, and 4D space-time vectors, designed primarily for performing geometric and momentum calculations on arrays of vectors. It supports multiple coordinate systems (Cartesian, polar, pseudorapidity, etc.) and can work with several data backends including pure Python objects, NumPy structured arrays, Awkward Arrays, SymPy expressions, and Numba-compiled functions. Each backend provides the same interface, allowing you to write code once and run it across different data structures.\n\nThe library is part of the Scikit-HEP project and aligns with conventions from ROOT's TLorentzVector and related high-energy physics tools. It offers both geometric and momentum flavors of vectors, where momentum vectors provide physics-specific property names (like transverse momentum) alongside geometric equivalents. This makes it useful for scientific computing workflows that need to manipulate spatial data efficiently without writing explicit loops.","worth_installing":"Yes. Vector is actively maintained, has no known vulnerabilities, installs with minimal friction, and is well-suited for scientific and physics applications requiring efficient array-based vector operations. The permissive BSD-3-Clause license poses no restrictions. Install it if you need to work with spatial vectors at scale or in high-energy physics contexts."},"id":"vector","links":{"html":"https://skillfed.io/packages/vector","md":"https://skillfed.io/packages/vector.md","pypi":"https://pypi.org/project/vector/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-21","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"vector","python_support":"supports_current","summary":"Vector classes and utilities"},"popularity":{"monthly_downloads":685620,"position":5353,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.8.1"}