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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 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.
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 and documentation in one place. Julia (programming language)
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. [13]
A Binder or "Binder-ready repository" is a code repository that contains both code and content to run, and configuration files for the environment needed to run it. [1] Since 2017, when the Binder Project was merged into the JupyterHub project, [2] the development communities share many people in common. A common use of Binder is for sharing a ...
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 ...
Cython (/ ˈ s aɪ θ ɒ n /) is a superset of the programming language Python, which allows developers to write Python code (with optional, C-inspired syntax extensions) that yields performance comparable to that of C.
[10] [11] [12] It is available both in browsers via Jupyter notebooks, [12] [13] and locally on Linux and macOS. [ 14 ] [ 15 ] Mojo aims to combine the usability of a high-level programming language , specifically Python , with the performance of a system programming language such as C++ , Rust , and Zig . [ 16 ]