Jupyter notebooks are built upon a number of popular open-source libraries: Underneath the interface, a notebook is a JSON document, following a versioned schema, usually ending with the '.ipynb' extension. Jupyter Notebook (formerly IPython Notebooks) is a web-based interactive computational environment for creating notebook documents.Ī Jupyter Notebook document is a browser-based REPL containing an ordered list of input/output cells which can contain code, text (using Markdown), mathematics, plots and rich media. According to the Project Jupyter website, 'Jupyter will always be 100% open-source software, free for all to use and released under the liberal terms of the modified BSD license'. Project Jupyter's operating philosophy is to support interactive data science and scientific computing across all programming languages via the development of open-source software. In 2015, GitHub and the Jupyter Project announced native rendering of Jupyter notebooks file format (.ipynb files) on the GitHub platform. Jupyter is language agnostic and it supports execution environments (aka kernels) in several dozen languages among which are Julia, R, Haskell, Ruby, and of course Python (via the IPython kernel). IPython continues to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython moved under the Jupyter name. In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. Data visualization: Graphic presentation of data clarifies patterns, trends, etc.Galileo Galilei's manuscript from 1610 with observations of Jupiter (⊛) and four of its moons (✱), whose depiction helped inspire the Jupyter logo.Creation and training of machine learning models: Design, programming and training of models based on machine learning.Statistical modeling: Mathematical methods for identifying the estimated distribution probability of a specific feature.Data cleaning: Differentiation between important and unimportant data in big data analysis.The major usage purposes of Jupyter Notebook include: Thanks to the numerous additional kernels Jupyter is not limited to Python as a program language, which means a great deal of flexibility with coding and analysis. Users benefit from the fact that any code in independent cells can be hosted in the prototype phase in particular, which makes it possible to test specific code blocks individually. In a single instance, user codes can be written, documented and used, data visualized, calculations carried out and the relevant results appraised. Jupyter Notebook provides an environment that is perfectly tailored to the requirements and workflow of data science and simulation.

Hosting a jupyter notebook online