Looking for an open source alternative to Mathematica or MatLab for solving algebraic equations? Look no further than the excellent SymPy project. It is a well built and easy to use Computer Algebra System (CAS) and in this episode we spoke with the current project maintainer Aaron Meurer about its capabilities and when you might want to use it.
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
- Subscribe on iTunes, Stitcher, TuneIn or RSS
- Follow us on Twitter or Google+
- Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
- Join our community at discourse.pythonpodcast.com to follow up with the guests and help us make the show better!
- I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
- Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
- I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit and double your signing bonus to $4,000.
- We are recording today on January 18th, 2016 and your hosts as usual are Tobias Macey and Chris Patti
- Today we are interviewing Aaron Meurer about SymPy
Interview with Aaron Meurer
- How did you get introduced to Python? – Chris
- What is Sympy and what kinds of problems does it aim to solve? – Chris
- How did the SymPy project get started? – Tobias
- How did you get started with the SymPy project? – Chris
- Are there any limits to the complexity of the equations SymPy can model and solve? – Chris
- How does SymPy compare to similar projects in other languages? – Tobias
- How does Sympy render results using such beautiful mathematical symbols when the inputs are simple ASCII? – Chris
- What are some of the challenges in creating documentation for a project like SymPy that is accessible to non-experts while still having the necessary information for professionals in the fields of mathematics? – Tobias
- Which fields of academia and business seem to be most heavily represented in the users of SymPy? – Tobias
- What are some of the uses of Sympy in education outside of the obvious like students checking their homework? – Chris
- How does SymPy integrate with the Jupyter Notebook? – Chris
- Is SymPy generally used more as an interactive mathematics environment or as a library integrated within a larger application? – Tobias
- What were the challenges moving SymPy from Python 2 to Python 3? – Chris
- Are there features of Python 3 that simplify your work on SymPy or that make it possible to add new features that would have been too difficult previously? – Tobias
- Were there any performance bottlenecks you needed to overcome in creating Sympy? – Chris
- What are some of the interesting design or implementation challenges you’ve found when creating and maintaining SymPy? – Chris
- Are there any new features or major updates to SymPy that are planned? – Tobias
- How is the evolution of SymPy managed from a feature perspective? Have there been any occasions in recent memory where a pull request had to be rejected because it didn’t fit with the vision for the project? – Tobias
- Which of the features of SymPy do you find yourself using most often? – Tobias