Jupyter Notebook
- Web-based environment for combining executable code, equations, visualizations, and narrative text in a single document.
- Enables interactive, incremental execution useful for exploration, debugging, and seeing results and plots in real time.
- Supports multiple languages (e.g., Python, R, Julia), collaboration, sharing, and a rich ecosystem of extensions and related tools.
Definition
Section titled “Definition”Jupyter Notebook is a web-based interactive development environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
Explanation
Section titled “Explanation”Jupyter Notebook is used by data scientists, researchers, and developers to explore and analyze data, build and test algorithms, and share findings. It supports running code interactively and incrementally so users can test and debug without executing an entire script, and it displays results and plots inline. The environment supports multiple programming languages such as Python, R, and Julia, and includes features for version control, collaboration, and sharing. A rich ecosystem of extensions and libraries extends its capabilities (for example, Jupyter Lab provides a more flexible interface), and an active community contributes resources, tutorials, and events like JupyterCon and Jupyter Day.
Examples
Section titled “Examples”Data exploration and analysis
Section titled “Data exploration and analysis”A data scientist can import and manipulate large datasets, visualize the data with plotting libraries, and perform statistical analysis to uncover insights and trends. The notebook permits running code, and adding markdown text and images to document the process and findings in a reproducible and shareable format.
Machine learning and artificial intelligence
Section titled “Machine learning and artificial intelligence”A developer can build and train machine learning models, evaluate performance, and fine-tune hyperparameters to improve accuracy. The notebook allows writing and running code alongside markdown text and images to document steps and results in a clear and organized manner.
Use cases
Section titled “Use cases”- Data exploration and analysis workflows.
- Building, training, and evaluating machine learning models.
- Collaborative and reproducible computational research and reporting.
Related terms
Section titled “Related terms”- Jupyter Lab
- Python
- R
- Julia
- Extensions (e.g., spell-checking, code folding, code formatting)
- JupyterCon
- Jupyter Day