Navigating the Scientific Python Communities - the missing guide
Authors: Ivanov, Paul, UC Berkeley
The awful truth: our newcomers have a deluge of options to wade through as they begin their journey. What tools are available, how do I install them, how do I make them work together -- all hard questions facing a budding scipythonista.
On developer-friendly platforms, the popular approach is to just install numpy, scipy, matplotlib, and ipython using package management facilities provided by the operating system. On Mac OS X and Windows, the least painful, bootstrapping approach is to use a Python distribution like Python(X,Y), EPD, Anaconda, or Sage.
Both of these paths obscure a reality which must be stated explicitly: the development of packages is fundamentally decentralized. The scientific python ecosystem consists of a loose but thriving confederation of projects and communities.
The chaos of installation options and lack of centralization around a canonical solution, which on the surface appears to be a point of weakness that is detrimental to community growth, is a symptom of one of its greatest strengths. Namely, what we have is a direct democracy for user-developers. They vote with their feet: filing bug reports, testing pre-release versions, participating in mailing lists, writing and reviewing pull requests, and advertising the tools they use in talks, papers, and conversations at conferences.
I will discuss why this is the case, why this is a good thing, and how we can embrace it. The talk will present the ethos and expectations of an effective newcomer, as well as resources and strategies for incremental progress toward becoming a master scipythonista.