Project Jupyter’s tools are available for installation via the Python Package Index, the leading repository of software created for the Python programming language.
This page uses instructions with pip, the recommended installation tool for Python. If you require environment management as opposed to just installation, look into conda, mamba, pipenv, and Homebrew.
JupyterLab
Install JupyterLab with pip:
Note: If you install JupyterLab with conda or mamba, we recommend using the conda-forge channel.
Once installed, launch JupyterLab with:
Jupyter Notebook
Install the classic Jupyter Notebook with:
To run the notebook:
Voilà
Install Voilà with:
Once installed, launch Voilà with:
Homebrew
Homebrew is a package manager for macOS and Linux.
You can use it to install Jupyter by running:
Jupyter Notebook
The Jupyter notebook is a web-based notebook environment for interactive
computing.
Maintained versions
We maintain the two most recently released major versions of Jupyter Notebook, Notebook v5 and Classic Notebook v6. After Notebook v7.0 is released, we will no longer maintain Notebook v5. All Notebook v5 users are strongly advised to upgrade to Classic Notebook v6 as soon as possible.
The Jupyter Notebook project is currently undertaking a transition to a more modern code base built from the ground-up using JupyterLab components and extensions.
There is new stream of work which was submitted and then accepted as a Jupyter Enhancement Proposal (JEP) as part of the next version (v7): https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html
There is also a plan to continue maintaining Notebook v6 with bug and security fixes only, to ease the transition to Notebook v7: https://github.com/jupyter/notebook-team-compass/issues/5#issuecomment-1085254000
Notebook v7
The newest major version of Notebook is based on:
- JupyterLab components for the frontend
- Jupyter Server for the Python server
This represents a significant change to the jupyter/notebook code base.
To learn more about Notebook v7: https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html
Classic Notebook v6
Maintenance and security-related issues are now being addressed in the 6.4.x branch.
A 6.5.x branch will be soon created and will depend on nbclassic for the HTML/JavaScript/CSS assets.
New features and continuous improvement is now focused on Notebook v7 (see section above).
If you have an open pull request with a new feature or if you were planning to open one, we encourage switching over to the Jupyter Server and JupyterLab architecture, and distribute it as a server extension and / or JupyterLab prebuilt extension. That way your new feature will also be compatible with the new Notebook v7.
Jupyter notebook, the language-agnostic evolution of IPython notebook
Jupyter notebook is a language-agnostic HTML notebook application for
Project Jupyter. In 2015, Jupyter notebook was released as a part of
The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic
release containing both language-agnostic code, such as the IPython notebook,
and language specific code, such as the IPython kernel for Python. As
computing spans across many languages, Project Jupyter will continue to develop the
language-agnostic Jupyter notebook in this repo and with the help of the
community develop language specific kernels which are found in their own
discrete repos.
- The Big Split™ announcement
- Jupyter Ascending blog post
Installation
You can find the installation documentation for the
Jupyter platform, on ReadTheDocs.
The documentation for advanced usage of Jupyter notebook can be found
here.
For a local installation, make sure you have
pip installed and run:
pip install notebook
Usage — Running Jupyter notebook
Running in a local installation
Launch with:
jupyter notebook
Running in a remote installation
You need some configuration before starting Jupyter notebook remotely. See Running a notebook server.
Development Installation
See CONTRIBUTING.md for how to set up a local development installation.
Contributing
If you are interested in contributing to the project, see CONTRIBUTING.md.
Community Guidelines and Code of Conduct
This repository is a Jupyter project and follows the Jupyter
Community Guides and Code of Conduct.
Resources
- Project Jupyter website
- Online Demo at jupyter.org/try
- Documentation for Jupyter notebook
- Korean Version of Installation
- Documentation for Project Jupyter [PDF]
- Issues
- Technical support — Jupyter Google Group
About the Jupyter Development Team
The Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.
The core team that coordinates development on GitHub can be found here:
https://github.com/jupyter/.
Our Copyright Policy
Jupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.
With this in mind, the following banner should be used in any source code file
to indicate the copyright and license terms:
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The Jupyter notebook combines two components. A web application, which is a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output. And Notebook documents, which are a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects. Notebook documents contain the inputs and outputs of a interactive session as well as additional text that accompanies the code but is not meant for execution.
Features
- In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion/introspection
- The ability to execute code from the browser, with the results of computations attached to the code which generated them
- Displaying the result of computation using rich media representations, such as HTML, LaTeX, PNG, SVG, etc.
- In-browser editing for rich text using the Markdown markup language, which can provide commentary for the code, is not limited to plain text
- The ability to easily include mathematical notation within markdown cells using LaTeX, and rendered natively by MathJax
- Notebook files can serve as a complete computational record of a session, interleaving executable code with explanatory text, mathematics, and rich representations of resulting objects
Project Samples
License
BSD License
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User Reviews
Jupyter Notebook
The Jupyter notebook is a web-based notebook environment for interactive
computing.
Maintained versions
We maintain the two most recently released major versions of Jupyter Notebook, Notebook v5 and Classic Notebook v6. After Notebook v7.0 is released, we will no longer maintain Notebook v5. All Notebook v5 users are strongly advised to upgrade to Classic Notebook v6 as soon as possible.
The Jupyter Notebook project is currently undertaking a transition to a more modern code base built from the ground-up using JupyterLab components and extensions.
There is new stream of work which was submitted and then accepted as a Jupyter Enhancement Proposal (JEP) as part of the next version (v7): https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html
There is also a plan to continue maintaining Notebook v6 with bug and security fixes only, to ease the transition to Notebook v7: jupyter/notebook-team-compass#5 (comment)
Notebook v7
The newest major version of Notebook is based on:
- JupyterLab components for the frontend
- Jupyter Server for the Python server
This represents a significant change to the jupyter/notebook code base.
To learn more about Notebook v7: https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html
Classic Notebook v6
Maintenance and security-related issues are now being addressed in the 6.4.x branch.
A 6.5.x branch will be soon created and will depend on nbclassic for the HTML/JavaScript/CSS assets.
New features and continuous improvement is now focused on Notebook v7 (see section above).
If you have an open pull request with a new feature or if you were planning to open one, we encourage switching over to the Jupyter Server and JupyterLab architecture, and distribute it as a server extension and / or JupyterLab prebuilt extension. That way your new feature will also be compatible with the new Notebook v7.
Jupyter notebook, the language-agnostic evolution of IPython notebook
Jupyter notebook is a language-agnostic HTML notebook application for
Project Jupyter. In 2015, Jupyter notebook was released as a part of
The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic
release containing both language-agnostic code, such as the IPython notebook,
and language specific code, such as the IPython kernel for Python. As
computing spans across many languages, Project Jupyter will continue to develop the
language-agnostic Jupyter notebook in this repo and with the help of the
community develop language specific kernels which are found in their own
discrete repos.
- The Big Split™ announcement
- Jupyter Ascending blog post
Installation
You can find the installation documentation for the
Jupyter platform, on ReadTheDocs.
The documentation for advanced usage of Jupyter notebook can be found
here.
For a local installation, make sure you have
pip installed and run:
Usage — Running Jupyter notebook
Running in a local installation
Launch with:
Running in a remote installation
You need some configuration before starting Jupyter notebook remotely. See Running a notebook server.
Development Installation
See CONTRIBUTING.md for how to set up a local development installation.
Contributing
If you are interested in contributing to the project, see CONTRIBUTING.md.
Community Guidelines and Code of Conduct
This repository is a Jupyter project and follows the Jupyter
Community Guides and Code of Conduct.
Resources
- Project Jupyter website
- Online Demo at jupyter.org/try
- Documentation for Jupyter notebook
- Korean Version of Installation
- Documentation for Project Jupyter
- Issues
- Technical support — Jupyter Google Group
About the Jupyter Development Team
The Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.
The core team that coordinates development on GitHub can be found here:
https://github.com/jupyter/.
Our Copyright Policy
Jupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.
With this in mind, the following banner should be used in any source code file
to indicate the copyright and license terms:
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.
Jupyter has support for over 40 different programming languages and Python is one of them. Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the Jupyter Notebook itself.
Jupyter Notebook can be installed by using either of the two ways described below:
- Using Anaconda:
Install Python and Jupyter using the Anaconda Distribution, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science. To install Anaconda, go through How to install Anaconda on windows? and follow the instructions provided. -
Using PIP:
Install Jupyter using the PIP package manager used to install and manage software packages/libraries written in Python. To install pip, go through How to install PIP on Windows? and follow the instructions provided.
Installing Jupyter Notebook using Anaconda:
Anaconda is an open-source software that contains Jupyter, spyder, etc that are used for large data processing, data analytics, heavy scientific computing. Anaconda works for R and python programming language. Spyder(sub-application of Anaconda) is used for python. Opencv for python will work in spyder. Package versions are managed by the package management system called conda.
To install Jupyter using Anaconda, just go through the following instructions:
Launching Jupyter:
Installing Jupyter Notebook using pip:
PIP is a package management system used to install and manage software packages/libraries written in Python. These files are stored in a large “on-line repository” termed as Python Package Index (PyPI).
pip uses PyPI as the default source for packages and their dependencies.
To install Jupyter using pip, we need to first check if pip is updated in our system. Use the following command to update pip:
python -m pip install --upgrade pip
After updating the pip version, follow the instructions provided below to install Jupyter:
- Command to install Jupyter:
python -m pip install jupyter
- Beginning Installation:
- Downloading Files and Data:
- Installing Packages:
- Finished Installation:
Launching Jupyter:
Use the following command to launch Jupyter using command-line:
jupyter notebook
Last Updated :
05 Oct, 2021
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