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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Evaluating symbolic expressions

In the context of scientific computing, there is often the need of first making symbolic manipulations and then converting the symbolic result into a floating-point number .

The central tool for evaluating a symbolic expression is evalf. It converts symbolic expressions to floating-point numbers by using the following:

pi.evalf()   # returns 3.14159265358979

The data type of the resulting object is Float (note the capitalization), which is a SymPy data type that allows floating-point numbers with an arbitrary number of digits (arbitrary precision). The default precision corresponds to 15 digits, but it can be changed by giving evalf an extra positive integer argument specifying the desired precision in terms the numbers of digits,

pi.evalf(30)   # returns  3.14159265358979323846264338328

A consequence of working with arbitrary precision is that numbers can be arbitrary small, that is, the limits of the classical floating-point representation...

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