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\section{Introduction}  \label{sec:intro}  Much of modern Astronomy research depends on software. Digital images and numerical simulations are central to the work of most astronomers today, and anyone who is actively involved in astronomy research has a variety of software techniques in their toolbox. Furthermore, the sheer volume of data has increased dramatically in recent years. The efficient and effective use of large data sets increasingly requires more than rudimentary software skills. Finally, as astronomy moves towards the open code model, propelled by pressure from funding agencies and journals as well as colleagues, readability and reusability of code will become increasingly important (Figure \ref{{fig:xkcd}}). \ref{fig:xkcd}).  Yet we know few details about the software practices of astronomers. In this work we aim to gain a greater understanding the prevalence of software tools, the demographics of their users, and the level of software training in astronomy. The astronomical community has, in the past, provided funding and support for software tools intended for the wider community. Examples of this include the Goddard IDL library (funded by the NASA ADP), IRAF (supported and developed by AURA at NOAO) and STSDAS (supported and developed by STScI). As the field develops, new tools will be required and we need to be focusing our efforts on tools that will have the widest user base and the lowest barrier to utilization. For example, as our work here shows, the much larger astronomy user base of Python relative to R suggests that tools in the former language are likely to get many more users and contributers than the latter.  
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