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JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.

The Jupyter Notebook App is a server-client application that allowsediting and running notebook documentsvia a web browser.The Jupyter Notebook App can be executed on a local desktoprequiring no internet access (as described in this document)or can be installed on a remote server and accessed through the internet.

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When you open a Notebook document, the associated kernel is automatically launched.When the notebook is executed (either cell-by-cell or with menu Cell -> Run All),the kernel performs the computation and produces the results.Depending on the type of computations, the kernel may consume significantCPU and RAM. Note that the RAM is not released until the kernel is shut-down.

The Notebook Dashboard is the component whichis shown first when you launch Jupyter Notebook App.The Notebook Dashboard is mainly used to open notebook documents,and to manage the running kernels (visualize and shutdown).

Welcome to the Jupyter Notebook documentation site. Jupyter Notebookis a simplified notebook authoring application, and is a part of ProjectJupyter, a large umbrella projectcentered around the goal of providing tools (and standards)for interactive computing with computational notebooks.

A computational notebookis a shareable document that combines computercode, plain language descriptions, data, rich visualizations like 3D models,charts, graphs and figures, and interactive controls. A notebook, along withan editor like Jupyter Notebook, provides a fast interactive environment forprototyping and explaining code, exploring and visualizing data, and sharingideas with others.

Jupyter Notebook is a sibling to other notebook authoring applications underthe Project Jupyter umbrella, like JupyterLaband Jupyter Desktop.Jupyter Notebook offers a lightweight, simplified experience compared to JupyterLab.

You can use Google or search for Jupyter Notebook extensions. There are actually quite a few out there. One of the most popular extension sets is called jupyter_contrib_nbextensions, which you can get from GitHub. This is actually a collection of extensions that is provided by the Jupyter community and installed with pip.

Jupyter Notebook is a notebook authoring application, under the ProjectJupyter umbrella. Built on the power ofthe computational notebook format,Jupyter Notebook offers fast, interactive new ways to prototype and explainyour code, explore and visualize your data, and share your ideas with others.

Notebooks extend the console-based approach to interactive computing in aqualitatively new direction, providing a web-based application suitable forcapturing the whole computation process: developing, documenting, and executingcode, as well as communicating the results. The Jupyter notebook combines twocomponents:

A web application: A browser-based editing program for interactive authoringof computational notebooks which provides a fast interactive environment for prototyping andexplaining code, exploring and visualizing data, and sharing ideas with others

Notebook documents contains the inputs and outputs of a interactive session aswell as additional text that accompanies the code but is not meant forexecution. In this way, notebook files can serve as a complete computationalrecord of a session, interleaving executable code with explanatory text,mathematics, and rich representations of resulting objects. These documentsare internally JSON files and are saved with the .ipynb extension. SinceJSON is a plain text format, they can be version-controlled and shared withcolleagues.

Furthermore, any .ipynb notebook document available from a publicURL can be shared via the Jupyter Notebook Viewer .This service loads the notebook document from the URL and renders it as astatic web page. The results may thus be shared with a colleague, or as apublic blog post, without other users needing to install the Jupyter notebookthemselves. In effect, nbviewer is simply [nbconvert] asa web service, so you can do your own static conversions with nbconvert,without relying on nbviewer.

The landing page of the Jupyter notebook web application, the dashboard,shows the notebooks currently available in the notebook directory (by default,the directory from which the notebook server was started).

You can create new notebooks from the dashboard with the New Notebookbutton, or open existing ones by clicking on their name. You can also dragand drop .ipynb notebooks and standard .py Python source code filesinto the notebook list area.

When starting a notebook server from the command line, you can also open aparticular notebook directly, bypassing the dashboard, with jupyter notebook my_notebook.ipynb. The .ipynb extension is assumed if no extension isgiven.

When you are inside an open notebook, the File | Open... menu option willopen the dashboard in a new browser tab, to allow you to open another notebookfrom the notebook directory or to create a new notebook.

You can start more than one notebook server at the same time, if you wantto work on notebooks in different directories. By default the firstnotebook server starts on port 8888, and later notebook servers search forports near that one. You can also manually specify the port with the--port option.

An open notebook has exactly one interactive session connected to akernel, which will execute code sent by the userand communicate back results. This kernel remains active if the web browserwindow is closed, and reopening the same notebook from the dashboard willreconnect the web application to the same kernel. In the dashboard, notebookswith an active kernel have a Shutdown button next to them, whereasnotebooks without an active kernel have a Delete button in its place.

If you want to provide structure for your document, you can use markdownheadings. Markdown headings consist of 1 to 6 hash # signs # followed by aspace and the title of your section. The markdown heading will be convertedto a clickable link for a section of the notebook. It is also used as a hintwhen exporting to other document formats, like PDF.

Raw cells provide a place in which you can write output directly.Raw cells are not evaluated by the notebook.When passed through [nbconvert], raw cells arrive in thedestination format unmodified. For example, you can type full LaTeXinto a raw cell, which will only be rendered by LaTeX after conversion bynbconvert.

The normal workflow in a notebook is, then, quite similar to a standardIPython session, with the difference that you can edit cells in-place multipletimes until you obtain the desired results, rather than having torerun separate scripts with the %run magic command.

One major feature of the Jupyter notebook is the ability to display plots thatare the output of running code cells. The IPython kernel is designed to workseamlessly with the [matplotlib] plotting library to provide this functionality.Specific plotting library integration is a feature of the kernel.

It was spun off from IPython in 2014 by Fernando Prez and Brian Granger. Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Project Jupyter has developed and supported the interactive computing products Jupyter Notebook, JupyterHub, and JupyterLab.

The first version of Notebooks for IPython was released in 2011 by a team including Fernando Prez, Brian Granger, and Min Ragan-Kelley.[2] In 2014, Prez announced a spin-off project from IPython called Project Jupyter.[3] 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.[4][5] Jupyter supports execution environments (called "kernels") in several dozen languages, including Julia, R, Haskell, Ruby, and Python (via the IPython kernel).

In 2015, about 200,000 Jupyter notebooks were available on GitHub. By 2018, about 2.5 million were available.[6] In January 2021, nearly 10 million were available, including notebooks about the first observation of gravitational waves[7] and about the 2019 discovery of a supermassive black hole.[8]

Visual Studio Code supports local development of Jupyter notebooks. As of July 2022, the Jupyter extension for VS Code has been downloaded over 40 million times, making it the second-most popular extension in the VS Code Marketplace.[13]

The Atlantic published an article entitled "The Scientific Paper Is Obsolete" in 2018, discussing the role of Jupyter Notebook and the Mathematica notebook in the future of scientific publishing.[15] Economist Paul Romer, in response, published a blog post in which he reflected on his experiences using Mathematica and Jupyter for research, concluding in part that Jupyter "does a better job of delivering what Theodore Gray had in mind when he designed the Mathematica notebook."[16]

Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. Jupyter Notebook is built using several open-source libraries, including IPython, ZeroMQ, Tornado, jQuery, Bootstrap, and MathJax. A Jupyter Notebook application is a browser-based REPL containing an ordered list of input/output cells which can contain code, text (using Github Flavored Markdown), mathematics, plots and rich media.

Jupyter Notebook 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. Jupyter interest overtook the popularity of the Mathematica notebook interface in early 2018.[15] ff782bc1db