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Sidhant Nagpal edited this page Aug 19, 2018 · 1 revision

SymPy is a pure Python library for symbolic computation. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries. The tutorial will cover the following topics and more.

  • Introduction
    • What is Symbolic Computation?
    • A More Interesting Example
    • The Power of Symbolic Computation
    • Why SymPy?
  • Gotchas
    • Symbols
    • Equals sign
    • Two Final Notes: ^ and /
  • Basic Operations
    • Substitution
    • Converting Strings to SymPy Expressions
    • evalf
    • Printing
    • Simplification
    • Powers
    • Exponentials and logarithms
    • Special Functions
  • Calculus
    • Derivatives
    • Integrals
    • Limits
    • Series Expansion
  • Solvers
    • A Note about Equations
    • Solving Equations Algebraically
    • Solving Differential Equations
  • Matrices
    • Basic Operations
    • Basic Methods
    • Matrix Constructors
    • Advanced Methods

Prerequisites:

The tutorial will only assume a basic knowledge of Python. No prior knowledge of SymPy or other Python libraries is required, although it is suggested that attendees be familiar with the IPython notebook. A mathematical knowledge of calculus is recommended.

We recommend that the attendees install the Anaconda Python distribution which includes SymPy, NumPy, and IPython. Once Anaconda is installed simply type the following in a terminal to install the necessary packages:

$ conda install numpy ipython-notebook sympy

Alternative installation instructions can be found here: http://docs.sympy.org/dev/install.html.

Content URLs:

SymPy team has developed and delivered many talks and tutorials at SciPy and other conferences. We are constantly building on new content and improving the present at the same time. The website for the workshop is here. You can find the introduction slides here, the official sphinx tutorial here and the exercises in form of IPython notebooks.

Note: The notebooks are hosted statically, you can download the zip from here and run locally to have an interactive session.

See Also: Workshop content for SciPy conference in 2016, 2014 and 2013 and PyCon conference in 2015.

Speaker Links:

Past SymPy Tutorials:

SymPy team has delivered many talks and tutorials at SciPy, PyCon, EuroSciPy and PyData. Following are a few references for the same:

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