2. Best practices in Interactive Computing

Full list of references in Chapter 2 of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python, by Dr. Cyrille Rossant, Packt Publishing, 400 pages, 2014.

Python 2/Python 3

• What's new in Python 3?.
• An excellent free book about porting code to Python 3, by Lennart Regebro.
• Official recommendations about Python 2/Python 3 compatibility.
• 2to3 module to convert Python 2 code to Python 3.
• six compatibility module.
• Official wiki page about the Python 2/Python 3 question.
• Python 3 questions and answers, by Nick Coghlan.
• "Ten awesome features of Python that you can't use because you refuse to upgrade to Python 3", a presentation by Aaron Meurer.
• Using the __future__ module when writing compatibility code.
• Key differences between Python 2 and Python 3.

Integrated Development Environments for Python

• PyDev for Eclipse.
• Spyder, an open source IDE.
• PyCharm.
• PyTools for Microsoft Visual Studio on Windows.
• PyScripter.
• IEP, the Interactive Editor for Python.

Git

• msysgit: Git for Windows.
• TortoiseGit.

Hosted Git Services

• GitHub.
• BitBucket.
• Google Code.
• Gitorious.
• Sourceforge.

Learning Git

• GitHub help.
• Pro Git book.
• Hands-on tutorial.
• Git Guided Tour.
• GitHub Git tutorial.
• Atlassian Git tutorial.
• CodeSchool's online course.
• Git tutorial by Lars Vogel.
• Git tutorial for scientists.

Git workflows

• A popular but complex Git flow.
• A simpler workflow, used by GitHub.
• Different Git workflows.
• Learn Git branching.
• The Git workflow recommended on the NumPy project (and others).
• A post on the IPython mailing list about an efficient Git workflow, by Fernando Perez.

Good practices

• The Python Cookbook, by David Beazley, a must-read with many advanced recipes for Python 3.
• The Hitchhiker's Guide to Python.
• PEP8 rules.
• PyLint, a static analysis tool.
• Design patterns in Python.
• Design patterns on Wikipedia.
• Coding standards of Tahoe-LAFS.
• "How to be a great software developer", by Peter Nixey.
• "Why you should write buggy software with as few features as possible?" a talk by Brian Granger.

Packaging

• The Hitchhiker’s Guide to Packaging.
• Python Packaging User Guide.
• Conda.

Reproducibility

• "An efficient workflow for reproducible science", a talk by Trevor Bekolay.
• "Ten Simple Rules for Reproducible Computational Research", Sandve et al., PLoS Computational Biology, 2013.
• Konrad Hinsen's blog.

Tools

• Markdown, a simple markup language.
• Sphinx.
• Figshare for storing binary research data online.
• Datadryad for storing binary research data online.
• joblib, a must-have tool for interactive computing.
• ipycache, providing a %%cache magic in IPython.
• AutoIt to automate GUI actions.
• AutoHotKey to create automation scripts on Windows.

Unit testing

• Nose, a unit testing package for Python.
• Test-driven development.
• "Untested code is broken code", by Martin Aspeli.

Test coverage

• coverage.py, a Python module by Ned Batchelder.
• coveralls.io, a test coverage tool.

Continuous integration

• Documentation of Travis CI in Python.

Debugging

• WinPdb, a graphical debugger.