We're all familiar with the data science tools like numpy, pandas, and others. These are numerical tools working with floating point numbers, often to represent real-world systems. But what if you exactly specify the equations, symbolically like many of us did back in Calculus and Differential Equations courses? With SymPy, you can do exactly that. Create equations, integrate, differentiate, and solve them. Then you can convert those solutions into Python (or even C++ and Fortran code). We're here with two of the core maintainer: Ondřej Čertík and Aaron Meurer to learn all about SymPy.

Links from the show

Ondrej Certik: @OndrejCertik
Aaron Meurer: @asmeurer
SymPy: sympy.org
SymPy Docs: docs.sympy.org/dev
Tutorials: docs.sympy.org
The SymPy/HackerRank DMCA Incident: asmeurer.com
SymEngine: github.com
SymPy Gamma: gamma.sympy.org
Sovled derivative problem - wait for derivative steps to appear: gamma.sympy.org
Github Takedown Repo: github.com
e: The Story of a Number book: amazon.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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