SymPy: symbolic computing in Python
Aaron Meurer(University of South Carolina), Christopher P. Smith, Mateusz Paprocki, Ondřej Čertı́k(Los Alamos National Laboratory), Sergey B Kirpichev(Lomonosov Moscow State University), Matthew Rocklin, Amit Kumar(Delhi Technological University), Sergiu Ivanov(Université Paris-Est Créteil), Jason K. Moore(University of California, Davis), Sartaj Singh(Indian Institute of Technology BHU), Thilina Rathnayake(University of Moratuwa), Sean Vig(University of Illinois Urbana-Champaign), Brian Granger(California Polytechnic State University), Richard P. Muller(Sandia National Laboratories), Francesco Bonazzi(Max Planck Institute of Colloids and Interfaces), Harsh Gupta(Indian Institute of Technology Kharagpur), Shivam Vats(Indian Institute of Technology Kharagpur), Fredrik Johansson(Institut national de recherche en sciences et technologies du numérique), Fabian Pedregosa(Institut national de recherche en sciences et technologies du numérique), Matthew Curry(University of New Mexico), Andy R. Terrel(NumFOCUS), Štěpán Roučka(Charles University), Ashutosh Saboo(Birla Institute of Technology and Science, Pilani - Goa Campus), Isuru Fernando(University of Moratuwa), Sumith Kulal(Indian Institute of Technology Bombay), Robert Cimrman(University of West Bohemia in Pilsen), Anthony Scopatz(University of South Carolina)
Cited by 1,631Open Access
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 a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
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