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A Jupyter Notebook document is a JSON file, following a versioned schema, usually ending with the ".ipynb" extension. The main parts of the Jupyter Notebooks are: Metadata, Notebook format and list of cells. Metadata is a data Dictionary of definitions to set up and display the notebook. Notebook Format is a version number of the software.
According to Stephen Wolfram: "The idea of a notebook is to have an interactive document that freely mixes code, results, graphics, text and everything else.", [4] and according to the Jupyter Project Documentation: "The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based ...
A common use of Binder is for sharing a Jupyter notebook in a way that the recipient can immediately execute in a browser. [3] The Binder project maintains core libraries and documentation for running Binder services, which make those projects available, as well as BinderHub, a tool for deploying such services via common cloud computing ...
Python and any with a Jupyter Kernel: JSON format Specification for ipynb: No Yes: Works in the format of notebooks, which combine headings, text (including LaTeX), plots, etc. with the written code. nbdev: Python and Jupyter Notebook: nbdev is a library that allows one to develop a python library in Jupyter Notebooks, putting all code, tests ...
The default can be overridden (e.g. in source code comment) to Python 3 (or 2) syntax. Since Python 3 syntax has changed in recent versions, Cython may not be up to date with the latest additions. Cython has "native support for most of the C++ language" and "compiles almost all existing Python code". [7] Cython 3.0.0 was released on 17 July ...
IPython continued to exist as a Python shell and kernel for Jupyter, but the notebook interface and other language-agnostic parts of IPython were moved under the Jupyter name. [ 11 ] [ 12 ] Jupyter is language agnostic and its name is a reference to core programming languages supported by Jupyter, which are Julia , Python , and R .
Enterprise users can write applications in multiple development environments, including Jupyter Notebook. [41] Dash Enterprise ships with several “development engines” for drag-and-drop application editing, application design, and automated reporting, as well as dozens of artificial intelligence and machine learning application templates.
Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] Elixir is a high-level functional programming language based on the Erlang VM. Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for ...