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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.
Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating, executing, and visualizing Jupyter notebooks. It is similar to the notebook interface of other programs such as Maple, Mathematica, and SageMath, a computational interface style that originated with Mathematica in the 1980s. [14]
A notebook interface or computational notebook is a virtual notebook environment used for literate programming, a method of writing computer programs. [1] Some notebooks are WYSIWYG environments including executable calculations embedded in formatted documents; others separate calculations and text into separate sections.
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) Pluto.jl is a reactive ...
[4] [5] [6] It is available both in browsers via Jupyter notebooks, [6] [7] and locally on Linux and macOS. [8] [9] 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. [10]
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 ...
LeDock utilizes a simulated annealing and genetic algorithm approach for facilitating the docking process of ligands with protein targets. The software employs a knowledge-based scoring scheme that is derived from extensive prospective virtual screening campaigns.
It also launched PyData community workshops and the Jupyter Cloud Notebook service (Wakari.io). [14] In 2013, it received funding from DARPA. [20] In 2015, the company had two million users including 200 of the Fortune 500 companies [10] and raised $24 million in a Series A funding round led by General Catalyst and BuildGroup. [21]