Chapter 1. Laying the Foundation for Reproducible Data Analysis

In this chapter, we will cover the following recipes:

• Setting up Anaconda
• Installing the Data Science Toolbox
• Creating a virtual environment with virtualenv and virtualenvwrapper
• Sandboxing Python applications with Docker images
• Keeping track of package versions and history in IPython Notebooks
• Configuring IPython
• Learning to log for robust error checking
• Unit testing your code
• Configuring pandas
• Configuring matplotlib
• Seeding random number generators and NumPy print options
• Standardizing reports, code style, and data access