{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"EigenPy provides Python bindings between NumPy arrays and the Eigen C++ linear algebra library, enabling zero-copy memory sharing and access to Eigen's matrix decomposition and tensor operations from Python.","skillfed_tags":["linear-algebra","c++-bindings","numerical-computing"],"use_cases":["Prototyping numerical algorithms in Python that leverage Eigen's optimized matrix decompositions without rewriting in C++.","Building robotics or computer vision pipelines that need fast linear algebra operations with zero-copy overhead.","Wrapping existing Eigen-based C++ libraries to expose them safely and efficiently to Python code.","Performing tensor computations via Eigen::Tensor while maintaining full interoperability with Python arrays.","Developing scientific simulations that require both Python's flexibility and C++'s performance for linear algebra kernels."],"what_it_does":"EigenPy is a C++ binding layer that bridges arrays and the Eigen linear algebra library via Boost.Python. It enables Python code to work directly with Eigen's high-performance matrix and tensor operations without copying data between Python and C++. The package exposes Eigen's standard decomposition routines (Cholesky, SVD, QR), geometry module, tensor support, and STL/Boost types (optional, std::pair, maps, variants), all while maintaining memory alignment for vectorization.\n\nThe package is designed for scientific and numerical computing workflows where performance matters. Researchers and engineers working with large matrices, tensor computations, or algorithms that benefit from Eigen's optimizations can use it to prototype and deploy code that seamlessly integrates Python's ease of use with C++'s computational speed. Installation is straightforward on Linux, macOS, and Windows via conda, apt, or Homebrew, with pre-built wheels for modern Python versions.","worth_installing":"Yes, if you need high-performance linear algebra in Python with tight array integration and are on a supported platform (Python >=3.10, Linux/macOS/Windows). The zero-copy memory sharing and access to Eigen's decompositions make it valuable for numerical computing. Medium install friction (compiled dependencies) is offset by pre-built wheels and active maintenance. No known security issues and permissive licensing. Not necessary if you only use built-in operations or existing alternatives."},"id":"eigenpy","links":{"html":"https://skillfed.io/packages/eigenpy","md":"https://skillfed.io/packages/eigenpy.md","pypi":"https://pypi.org/project/eigenpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-21","license_spdx":"BSD-2-Clause","license_treatment":"permissive","name":"eigenpy","python_support":"supports_current","summary":"Bindings between Numpy and Eigen using Boost.Python"},"popularity":{"monthly_downloads":1260266,"position":4151,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.13.0"}