A Solid Foundation for Statistics in Python with SciPy
Warren Weckesser (University of California, Berkeley; NumFOCUS)
Matt Haberland (California Polytechnic State University, NumFOCUS)
The project will improve the SciPy library's statistics functionality to better serve biomedical research and downstream projects. In addition, an outreach component will engage female students, inspiring them to participate in open source code development.
SciPy is a library of numerical routines for the Python programming language that provides fundamental building blocks for modeling and solving scientific problems. SciPy includes algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, and many other classes of problems; it also provides specialized data structures, such as sparse matrices and k-dimensional trees. SciPy is built on top of NumPy, which provides array data structures and related fast numerical routines, and SciPy is itself the foundation on which higher level scientific libraries, including scikit-learn and scikit-image, are built.