Jupyter Notebook Environment Platform

A Jupyter Notebook Environment Platform is a notebook environment for managing Jupyter notebook documents.

• Context:
  • It can (typically) create Jupyter Notebook Files (in Jupyter notebook format).
  • It can (typically) be managed by a Project Jupyter Organization.
  • It can include a Jupyter Notebook Server, file manager, a text editor, a terminal emulator, a monitor for running Jupyter processes, an IPython cluster manager and a pager to display help.
  • It can support Jupyter Third-Party Extensions.
  • It can drive a Jupyter Notebook-based Notebooking Platform.
  • ...
• Example(s):
  • Jupyter v7.0.2 (~2023-08-06) [1]
  • ...
  • Jupyter v5.5 (2018-05-09) [2]
  • Jupyter v5.3 (2018-01-16) [3]
  • Jupyter v4.1 (2016-01-08)[4]
  • Jupyter v4.2.2 (2016-08-03)[5].
  • …
• Counter-Example(s):
  • Zeppelin Notebook Environment.
  • a Python IDE.
  • Online Spreadsheet Platform, like Google Sheets.
• See: Human-Centered Computing, Jupyter binder, REPL, iPython Kernel for Jupyter, OpenDreamKit Project, iPython, R kernel for Jupyter, JupyterHub.

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References

2018b

• Brian Granger. (2018). “Project Jupyter: From Computational Notebooks to Large Scale Data Science with Sensitive Data begins in two days."
  • QUOTE: …

2018

• https://medium.com/@NetflixTechBlog/notebook-innovation-591ee3221233
  • QUOTE: Project Jupyter began in 2014 with a goal of creating a consistent set of open-source tools for scientific research, reproducible workflows, computational narratives, and data analytics. ...

    core functionality it provides:

    • a messaging protocol for introspecting and executing code which is language agnostic
    • an editable file format for describing and capturing code, code output, and markdown notes
    • a web-based UI for interactively writing and running code as well as visualizing outputs
  • The Jupyter protocol provides a standard messaging API to communicate with kernels that act as computational engines. The protocol enables a composable architecture that separates where content is written (the UI) and where code is executed (the kernel). By isolating the runtime from the interface, notebooks can span multiple languages while maintaining flexibility in how the execution environment is configured. If a kernel exists for a language that knows how to communicate using the Jupyter protocol, notebooks can run code by sending messages back and forth with that kernel.

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/IPython#Project_Jupyter Retrieved:2017-4-27.
  • In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. IPython will continue to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython will move under the Jupyter name. Jupyter added support for Julia, R, Haskell and Ruby.

2017b

• https://cloud.google.com/datalab/
  • QUOTE: Cloud Datalab - An easy to use interactive tool for data exploration, analysis, visualization and machine learning.

     Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

    … Cloud Datalab is built on Jupyter

2016a

• http://jupyter.org/
  • QUOTE: The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

2016b

• http://jupyter-notebook.readthedocs.io/en/latest/notebook.html
  • QUOTE: The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The Jupyter notebook combines two components:
    • A web application: a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output.
    • Notebook documents: a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects.

2016c

• http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/
  • QUOTE: In reality, even today's “Jupyter Notebook” is a bit of a misnomer: the Notebook application includes not only support for Notebooks but also a file manager, a text editor, a terminal emulator, a monitor for running Jupyter processes, an IPython cluster manager and a pager to display help. And that is just what ships "out of the box", without counting the many third-party extensions for it. This rich toolset evolved organically, driven by the needs of our users and developers, even if we kept the increasingly ill-fitting "Notebook" name for the whole thing.

2015

• http://ipython.org/
  • QUOTE: … the notebook format, message protocol, qtconsole, notebook web application, etc. will move to new projects under the name Jupyter.

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