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

Product type Paperback

Published in Sep 2015

Publisher

ISBN-13 9781783288823

Length 300 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Author (1):

Kumar Mehta

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Table of Contents (12) Chapters

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

Chapter 6. Applying Python for Symbolic Computing

SymPy includes functionality ranging from basic symbolic arithmetic to polynomials, calculus, solvers, discrete mathematics, geometry, statistics, and physics. It mainly works on three types of numbers, namely integer, real, and rational. Integers are whole digit numbers without a decimal point, while real numbers are numbers with decimal points. Rational numbers have two parts: the numerator and the denominator. To define rational numbers, we can use the Ration class, which requires two numbers. In this chapter, we will discuss the concepts of SymPy with the help of example programs.

We will cover the following topics in this chapter: