SymPy : symbolic computing in Python

Meurer, Aaron; Smith, Christopher P.; Paprocki, Mateusz; Čertík, Ondřej; Kirpichev, Sergey B.; Rocklin, Matthew; Kumar, AMiT; Ivanov, Sergiu; Moore, Jason K.; Singh, Sartaj; Rathnayake, Thilina; Vig, Sean; Granger, Brian E.; Muller, Richard P.; Bonazzi, Francesco; Gupta, Harsh; Vats, Shivam; Johansson, Fredrik; Pedregosa, Fabian; Curry, Matthew J.; Terrel, Andy R.; Roučka, Štěpán; Saboo, Ashutosh; Fernando, Isuru; Kulal, Sumith; Cimrman, Robert; Scopatz, Anthony

doi:10.7717/peerj-cs.103

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SymPy : symbolic computing in Python

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Bonazzi, Francesco
Thomas Weikl, Theorie & Bio-Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Citation

Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., et al. (2017). SymPy: symbolic computing in Python. PeerJ Computer Science, 3: e103. doi:10.7717/peerj-cs.103.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-962F-9

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.