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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
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14
C. NumPy Functions' References
15
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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