Jupyter Notebook
Running code alongside text, equations, and visualisations in interactive documents
Jupyter Notebook is an open-source web-based environment where code, markdown text, mathematical equations, and visualisation outputs are combined in a single document called a notebook. Data scientists and researchers write Python — or R, Julia, or other supported kernels — in executable cells, running code incrementally and seeing outputs including plots, tables, and printed values inline. This makes Jupyter the standard environment for exploratory data analysis, where the ability to run one section at a time and see results immediately is more useful than a traditional script. Notebooks can be shared as HTML exports or through platforms like JupyterHub, Binder, and Google Colab. Jupyter Notebook and its successor JupyterLab are the dominant interactive computing environments across data science, machine learning, and academic research.
Official Website
Jupyter Notebook official site →Pricing
View pricing →Jupyter Notebook Use Cases
- Exploring and profiling a new dataset interactively by running cells to compute statistics, check distributions, and inspect sample records
- Building end-to-end machine learning workflows with code and visualisation in one document, from data loading through model evaluation
- Creating reproducible research documents that combine narrative explanation, code, and results for sharing with colleagues or publication
- Prototyping data transformation logic and visualisations before converting them into production scripts or pipeline steps
- Teaching and demonstrating data science concepts with executable examples that students or colleagues can run and modify themselves
Jupyter Notebook Learning Resources
The official Jupyter documentation at docs.jupyter.org covers installation, notebook structure, and kernel management. Try Jupyter at jupyter.org/try provides a free browser-based environment for running notebooks without any local setup.