SymPy: Symbolic computing in Python
Year: 2016
Type: preprint
Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become the standard symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select domain specific submodules. The supplementary materials provide additional examples and further outline details of the architecture and features of SymPy.
Authors: Aaron Meurer, Christopher P Smith, Mateusz Paprocki, Ondřej Čertík, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E Granger, Richard P Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J Curry, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, Anthony Scopatz
Institutions: University of South Carolina, Los Alamos National Laboratory, Delhi Technological University, Université Paris-Est Créteil, University of California, Davis, Indian Institute of Technology BHU, Banaras Hindu University, University of Moratuwa, University of Illinois Urbana-Champaign, California Polytechnic State University, Sandia National Laboratories, Max Planck Institute of Colloids and Interfaces, Indian Institute of Technology Kharagpur, Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'université de Bordeaux, SIERRA: Apprentissage Statistique et optimisation, University of New Mexico, Birla Institute of Technology and Science, Pilani - Goa Campus, Indian Institute of Technology Bombay, University of West Bohemia in Pilsen
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4239606659