notebook
Jupyter Interactive Notebook
Jupyter Notebook is a browser-based tool where you write and run code in cells alongside explanatory text, charts, and images, the standard environment for data science, research, and reproducible analysis.
Jupyter Notebook is a web-based tool that lets you write and run code in your browser, mixed in with text, images, and charts, all in a single document. The document is called a notebook. You open it in your browser, type some code in a cell, run that cell, and see the output right below it. You can then add a paragraph of text explaining what you did, insert a chart of your results, and share the whole thing as a file that others can open and run themselves.
It is language-agnostic, meaning it is not limited to any one programming language. The most common use is Python, but you can install kernels (separate components that run the actual code) for R, Julia, and many others. The notebook interface itself stays the same regardless of which language you are running underneath.
This repository is the source code for the Jupyter Notebook application. Two major versions are currently maintained. Version 7 is the current release and is built on top of JupyterLab components, which gives it a more modern foundation. Version 6 (called Classic Notebook) is in maintenance mode, receiving only security fixes. Extensions written for version 5 or 6 are not compatible with version 7.
Installation is a single pip command: pip install notebook. Once installed, you launch it from the terminal with jupyter notebook, and your browser opens automatically to a file browser where you can create or open notebooks.
Jupyter Notebook grew out of the IPython project. In 2015, the language-agnostic notebook part was separated from the Python-specific tools and became Jupyter, while IPython continued as a Python-focused kernel. Today it is one of the most widely used tools in data science, research, and education for writing code that is meant to be readable and reproducible.
Where it fits
- Write Python data analysis code and display charts and results inline without switching between files.
- Share a single notebook file that others can open and rerun to reproduce your analysis step by step.
- Teach or learn programming by mixing executable code cells with explanatory text in the same document.
- Run R, Julia, or other languages in notebooks by installing the appropriate kernel alongside Python.