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Scientific Computing with Python - Second Edition

You're reading from  Scientific Computing with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Pages 392 pages
Edition 2nd Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Preface Getting Started Variables and Basic Types Container Types Linear Algebra - Arrays Advanced Array Concepts Plotting Functions Classes Iterating Series and Dataframes - Working with Pandas Communication by a Graphical User Interface Error and Exception Handling Namespaces, Scopes, and Modules Input and Output Testing Symbolic Computations - SymPy Interacting with the Operating System Python for Parallel Computing Comprehensive Examples About Packt Other Books You May Enjoy References

16.2.1 Symbols – the basis of all formulas

The basic construction element to build a formula in SymPy is the symbol. As we saw in the introductory example, a symbol is created by the command symbols. This SymPy command generates symbol objects from a given string:

x, y, mass, torque = symbols('x y mass torque')

It is actually a short form of the following command:

symbol_list=[symbols(l) for l in 'x y mass torque'.split()]

Followed by an unpacking step to obtain variables:

 x, y, mass, torque = symbol_list

The arguments of the command define the string representation of the symbol. The variable name of the symbol chosen is often identical to its string representation, but this is not required by the language:

row_index=symbols('i',integer=True)
print(row_index**2) # returns i**2

Here, we also defined that the symbol is assumed to be an integer.

An entire set of symbols can be defined in a very compact way:

integervariables = symbols...
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