Why Jupyter is data scientists’ computational notebook of choice

"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 Pérez, by the programming languages Julia (Ju), Python (Py) and R. One analysis of the code-sharing site GitHub counted more than 2.5 million public Jupyter notebooks in September 2018, up from 200,000 or so in 2015. In part, says Pérez, that growth is due to improvements in the web software that drives applications such as Gmail and Google Docs; the maturation of scientific Python and data science; and, especially, the ease with which notebooks facilitate access to remote data that might otherwise be impractical to download — such as from the LSST. “In many cases, it’s much easier to move the computer to the data than the data to the computer,” says Pérez of Jupyter’s cloud-based capabilities. “What this architecture helps to do is to say, you tell me where your data is, and I’ll give you a computer right there.” 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. ..."