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Why Jupyter is Data Scientists' Computational Notebook of Choice

Tuesday October 30, 2018. 06:21 PM , from Slashdot
Jeffrey M. Perkel, writing for Nature: Perched atop the Cerro Pachon ridge in the Chilean Andes is a building site that will eventually become the Large Synoptic Survey Telescope (LSST). When it comes online in 2022, the telescope will generate terabytes of data each night as it surveys the southern skies automatically. And to crunch those data, astronomers will use a familiar and increasingly popular tool: the Jupyter notebook. Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document. Computational notebooks have been around for decades, but Jupyter in particular has exploded in popularity over the past couple of years. This rapid uptake has been aided by an enthusiastic community of user-developers and a redesigned architecture that allows the notebook to speak dozens of programming languages -- a fact reflected in its name, which was inspired, according to co-founder Fernando Perez, by the programming languages Julia (Ju), Python (Py) and R. For data scientists, Jupyter has emerged as a de facto standard, says Lorena Barba, a mechanical and aeronautical engineer at George Washington University in Washington DC. Mario Juric, an astronomer at the University of Washington in Seattle who coordinates the LSST's data-management team, says: 'I've never seen any migration this fast. It's just amazing.' Computational notebooks are essentially laboratory notebooks for scientific computing. Instead of pasting, say, DNA gels alongside lab protocols, researchers embed code, data and text to document their computational methods. The result, says Jupyter co-creator Brian Granger at California Polytechnic State University in San Luis Obispo, is a 'computational narrative' -- a document that allows researchers to supplement their code and data with analysis, hypotheses and conjecture. For data scientists, that format can drive exploration.

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