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In recent years, this broader community has settled on \code{python} as its programming language of choice due to its efficacy as a 'glue' language and the rapid speed of development it allows.   This has led to the development of a robust scientific software ecosystem with packages for numerical data analysis like \code{numpy} (Oliphant 2006; Van Der Walt et al. 2011), \code{scipy} (Jones et al. 2001), \code{pandas} (McKinney 2010), and \code{scikit-image}; \code{matplotlib} (Hunter 2007), and \code{seaborn} for plotting; \code{scikit-learn} for machine learning, and statistics and modeling packages like \code{scikits-statsmodels}, \code{pymc}, and \code{emcee} \citep{Foreman-Mackeyetal2013}.  \code{python} \code{Python}  is quickly becoming the language of choice for astronomers as well, with the Astropy project \citep{Robitailleetal2013} and its affiliated packages providing a coordinated set of tools implementing the core astronomy-specific functionality needed by researchers. Additionally, the development of flexible \code{python} packages like \code{yt} \citep{Turketal2011} and \code{pynbody} \citep{Pontzenetal2013}, capable of analyzing and visualizing astrophysical simulation data from several different simulation codes, have greatly improved the ability of computational researchers to perform useful, insight-generating analysis of their datasets.  Recently, the scientific \code{python} community has begun to converge on the \code{DataFrame} provided by the high-performance \code{pandas} data analysis library as a common data structure for the ecosystem.