- NumPy Cookbook
- Table of Contents
- NumPy Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Winding Along with IPython
- 2. Advanced Indexing and Array Concepts
- 3. Get to Grips with Commonly Used Functions
- 4. Connecting NumPy with the Rest of the World
- Introduction
- Using the buffer protocol
- Using the array interface
- Exchanging data with MATLAB and Octave
- Installing RPy2
- Interfacing with R
- Installing JPype
- Sending a NumPy array to JPype
- Installing Google App Engine
- Deploying NumPy code in the Google cloud
- Running NumPy code in a Python Anywhere web console
- Setting up PiCloud
- 5. Audio and Image Processing
- 6. Special Arrays and Universal Functions
- 7. Profiling and Debugging
- 8. Quality Assurance
- 9. Speed Up Code with Cython
- 10. Fun with Scikits
- Introduction
- Installing scikits-learn
- Loading an example dataset
- Clustering Dow Jones stocks with scikits-learn
- Installing scikits-statsmodels
- Performing a normality test with scikits-statsmodels
- Installing scikits-image
- Detecting corners
- Detecting edges
- Installing Pandas
- Estimating stock returns correlation with Pandas
- Loading data as pandas objects from statsmodels
- Resampling time series data
- Index
In this chapter, we will cover the following topics:
- Installing Pyflakes
- Performing static analysis with Pyflakes
- Analyzing code with Pylint
- Performing static analysis with Pychecker
- Testing code with docstrings
- Writing unit tests
- Testing code with mocks
- Testing the BDD way
Quality assurance, contrary to popular belief, is not so much about finding bugs as it is about preventing them. We will discuss two ways to improve the code quality, thereby preventing issues. First, we will do static analysis of already existing code. Second, we will cover unit testing; this includes mocking and Behavior Driven Development (BDD).
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