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Numpy Beginner's Guide (Update)

You're reading from   Numpy Beginner's Guide (Update) Build efficient, high-speed programs using the high-performance NumPy mathematical library

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781785281969
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. NumPy Quick Start 2. Beginning with NumPy Fundamentals FREE CHAPTER 3. Getting Familiar with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Moving Further with NumPy Modules 7. Peeking into Special Routines 8. Assuring Quality with Testing 9. Plotting with matplotlib 10. When NumPy Is Not Enough – SciPy and Beyond 11. Playing with Pygame A. Pop Quiz Answers B. Additional Online Resources C. NumPy Functions' References
Index

Chapter 8. Assuring Quality with Testing

Some programmers test only in production. If you are not one of them, then you're probably familiar with the concept of unit testing. Unit tests are automated tests written by a programmer to test his or her code. These tests could, for example, test a function or part of a function in isolation. Each test covers only a small unit of code. The benefits are increased confidence in the quality of the code, reproducible tests, and, as a side effect, clearer code.

Python has good support for unit testing. Additionally, NumPy adds the numpy.testing package to that for NumPy code unit testing.

Test-driven development (TDD) is one of the most important things that happened to software development. TDD focuses a lot on automated unit testing. The goal is to test automatically the code as much as possible. The next time we change the code, we can run the tests and catch potential regressions. In other words, any functionality already present...

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