No tool, even one as powerful and flexible as IPython, can be everything to everybody. This chapter takes a look at some specialized tools that integrate well with IPython and provide useful, if specialized, functionality. Of particular interest are tools that can be used for data analysis and machine learning.

The choice of which language to use on a project is impacted by many factors; familiarity, fitness to the task, supporting libraries, curiosity, managerial fiat, and many other considerations come into play. Each project has its own reasons, and general advice on which tool is "better" is very limited in applicability.

As such, this chapter will attempt to steer away from questions of the form, "Why would I use X instead of Y?" and instead stick to the more practical "How do other tools that I am interested in using work with IPython?" A few popular and interesting examples have been selected as important representatives...