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This state of affairs has historically forced significant duplication of effort, with individual research groups separately developing their own unique analysis scripts to perform similar operations.  Fortunately, the issue of data management and analysis is not endemic to astronomy, and the resulting overlap with the needs of the broader scientific community and the industrial community at large provides a large pool of scientific software developers to tackle these common problems.  In recent years, this broader community has settled on 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 NumPy (Oliphant 2006; Van Der Walt et al. 2011), SciPy (Jones et al. 2001), pandas (McKinney 2010),and scikit-image; Matplotlib and seaborn for plotting; scikit-learn for machine learning, and statistics packages like  %Adoption of the platform-independent Hierarchical Data Format (HDF5) for data storage helps mitigate some of these issues, being able to load a dataset into memory is only the first step in performing useful, insight-generating analysis.