“The Jupyter Notebook enables users to create and share documents that combine live code with narrative text, mathematical equations, visualizations, interactive controls, and other rich output. It also provides building blocks for interactive computing with data: a file browser, terminals, and a text editor.”
Jupyter Notebook is a development environment that supports languages like Python and many other languages. Data Scientists and Machine Learning practitioners use notebook to clean data, build models, show case and share findings, collaborate, present data visualizations. It serves many more purposes.
Question:What languages do you use in Jupyter?
Answer: C (and derivatives), R, Go, Ruby, Groovy, Rust, Java, Scala, JavaScript, Spark SQL, Julia, SQL, NodeJS, TypeScript, Perl, U? Jupyter, php, Python
“The Jupyter Notebook now supports over 100 programming languages, most of which have been developed by the community.”
“There are over 1.7 million public Jupyter notebooks hosted on GitHub. Authors are publishing Jupyter notebooks in conjunction with scientific research, academic journals, data journalism, educational courses, and books.”
Why JupyterLab
”At the same time, the community has faced challenges in using various software workflows with the notebook alone, such as running code from text files interactively. The classic Jupyter Notebook, built on web technologies from 2011, is also difficult to customize and extend.”
“JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area.”
“JupyterLab provides a high level of integration between notebooks, documents, and activities:
* Drag-and-drop to reorder notebook cells and copy them between notebooks.
* Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).
* Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.
* Edit popular file formats with live preview, such as Markdown, JSON, CSV, Vega, VegaLite, and more.
JupyterLab has been over three years in the making, with over 11,000 commits and 2,000 releases of npm and Python packages.”
“JupyterLab is built on top of an extension system that enables you to customize and enhance JupyterLab by installing additional extensions”
Source : https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906
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