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Mastering Python Scientific Computing

You're reading from   Mastering Python Scientific Computing A complete guide for Python programmers to master scientific computing using Python APIs and tools

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781783288823
Length 300 pages
Edition 1st Edition
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Author (1):
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 Kumar Mehta Kumar Mehta
Author Profile Icon Kumar Mehta
Kumar Mehta
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Table of Contents (12) Chapters Close

Preface 1. The Landscape of Scientific Computing – and Why Python? 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes FREE CHAPTER 3. Efficiently Fabricating and Managing Scientific Data 4. Scientific Computing APIs for Python 5. Performing Numerical Computing 6. Applying Python for Symbolic Computing 7. Data Analysis and Visualization 8. Parallel and Large-scale Scientific Computing 9. Revisiting Real-life Case Studies 10. Best Practices for Scientific Computing Index

Symbols, expressions, and basic arithmetic


In SymPy, we need to define symbols before using them in any expression. Defining a symbol is very simple. We just need to use the symbol function from the Symbol class to define a symbol, as used in the following program. We can use the evalf()/n() method to get the float numerical approximation of any object.

The following program uses three ways to create symbols. For creating only one symbol the name of method is symbol and for creating multiple symbols the method name is symbols. There are two ways of creating multiple symbols: one is by passing space-separated symbol names to the symbols method, and the other is by creating a sequence of symbols such as m0, m1, m2, m3, m4 by passing m0:5 to the symbols method. In this second option, m0 is the first value of index and the number 5 after : denotes that a total of five such variables should be created.

Generally, division of two integers truncates the decimal part. To avoid this, the first line...

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