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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

Chapter 5. Performing Numerical Computing

In this chapter, we will discuss most of the features of NumPy and SciPy with the help of example programs. We will start with a detailed discussion on arrays and the operations that can be performed on them, using examples. This will lay a solid foundation for discussing various advanced functionalities supported by NumPy and SciPy.

In this chapter, we will cover the following topics:

  • Scientific numerical computing using NumPy and SciPy
  • The fundamental objects of NumPy
  • The various packages/modules of NumPy
  • The basics of the SciPy package
  • Mathematical functions of SciPy
  • Advanced mathematical modules and packages

NumPy is the base of numerical computing in Python, and its most fundamental and important idea is support for multidimensional arrays. Let's start our discussion with the underlying concepts of arrays in NumPy. After the basics, we will discuss the various operations that can be performed on multidimensional arrays. We will also...

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