SymPy: Symbolic computing in Python
A peer-reviewed article of this Preprint also exists.
Author and article information
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.
Cite this as
2016. SymPy: Symbolic computing in Python. PeerJ Preprints 4:e2083v3 https://doi.org/10.7287/peerj.preprints.2083v3note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This article is to be submitted to PeerJ Computer Science. There are minor changes over the previous preprint version.
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Supplemental Information
Additional Information
Competing Interests
Christopher P Smith is an employee of Polar Semiconductor, Inc., Bloomington, Minnesota, United States; Mateusz Paprocki and Matthew Rocklin are employees of Continuum Analytics, Inc., Austin, Texas, United States; Andy R Terrel is an employee of Fashion Metric, Inc, Austin, Texas, United States.
Author Contributions
Aaron Meurer wrote the paper, performed the computation work, reviewed drafts of the paper.
Christopher P Smith wrote the paper, performed the computation work, reviewed drafts of the paper.
Mateusz Paprocki wrote the paper, performed the computation work, reviewed drafts of the paper.
Ondřej Čertík wrote the paper, performed the computation work, reviewed drafts of the paper.
Sergey B Kirpichev wrote the paper, performed the computation work, reviewed drafts of the paper.
Matthew Rocklin wrote the paper, performed the computation work, reviewed drafts of the paper.
AMiT Kumar wrote the paper, performed the computation work, reviewed drafts of the paper.
Sergiu Ivanov wrote the paper, performed the computation work, reviewed drafts of the paper.
Jason K Moore wrote the paper, performed the computation work, reviewed drafts of the paper.
Sartaj Singh wrote the paper, performed the computation work, reviewed drafts of the paper.
Thilina Rathnayake wrote the paper, performed the computation work, reviewed drafts of the paper.
Sean Vig wrote the paper, performed the computation work, reviewed drafts of the paper.
Brian E Granger wrote the paper, performed the computation work, reviewed drafts of the paper.
Richard P Muller wrote the paper, performed the computation work, reviewed drafts of the paper.
Francesco Bonazzi wrote the paper, performed the computation work, reviewed drafts of the paper.
Harsh Gupta wrote the paper, performed the computation work, reviewed drafts of the paper.
Shivam Vats wrote the paper, performed the computation work, reviewed drafts of the paper.
Fredrik Johansson wrote the paper, performed the computation work, reviewed drafts of the paper.
Fabian Pedregosa wrote the paper, performed the computation work, reviewed drafts of the paper.
Matthew J Curry wrote the paper, performed the computation work, reviewed drafts of the paper.
Andy R Terrel wrote the paper, performed the computation work, reviewed drafts of the paper.
Štěpán Roučka wrote the paper, performed the computation work, reviewed drafts of the paper.
Ashutosh Saboo wrote the paper, performed the computation work, reviewed drafts of the paper.
Isuru Fernando wrote the paper, performed the computation work, reviewed drafts of the paper.
Sumith Kulal wrote the paper, performed the computation work, reviewed drafts of the paper.
Robert Cimrman wrote the paper, performed the computation work, reviewed drafts of the paper.
Anthony Scopatz wrote the paper, performed the computation work, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The source for the paper is at https://github.com/sympy/sympy-paper. The source code for SymPy is at https://github.com/sympy/sympy.
Funding
The authors received no funding for this work.
