When it comes to computational notebooks, Jupyter has become a dominant open-source web tool. Key to Jupyter’s rapid growth is its ability to converse in dozens of computer languages, among them Julia (Ju), Python (Py), and R, which form the basis for its name.
A Github review reveals that there are now around 2.5 million public Jupyter notebooks crunching away at data in September 2018, up from just 200,000 in less than three years.
Jeffrey M. Perkel wrote about the tremendous growth of Jupyter in Nature:
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 Jurić, 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. Notebooks, Barba says, are a form of interactive computing, an environment in which users execute code, see what happens, modify and repeat in a kind of iterative conversation between researcher and data. They aren’t the only forum for such conversations — IPython, the interactive Python interpreter on which Jupyter’s predecessor, IPython Notebook, was built, is another. But notebooks allow users to document those conversations, building “more powerful connections between topics, theories, data and results”, Barba says.
