A Jupyter Notebook is an interactive computational environment that enables users to create and share documents containing:
- Live code
- Equations (LaTeX)
- Visualizations
- Narrative text (Markdown) Jupyter is widely used in data science, machine learning, education, and scientific computing, supporting over 40 programming languages, with Python being the most common.
Core Features
Interactive Code Execution
- Run code in cells and see results immediately below the cell.
- Ideal for iterative exploration, debugging, and data analysis.
Rich Text Support
- Supports Markdown and LaTeX.
- Enables documentation, embedded explanations, and mathematical notation.
Data Visualization
- Seamlessly integrates with libraries like Matplotlib, Seaborn, Plotly, etc.
- Outputs charts, tables, and images inline.
Extensibility and Widgets
- Supports interactive controls (e.g., sliders, buttons) using
ipywidgets. - Can embed HTML, JavaScript, and interactive maps.
Anatomy of a Jupyter Notebook
- Notebook File (
.ipynb): JSON file format containing code, outputs, and metadata. - Cells:
- Code Cells: Execute source code.
- Markdown Cells: Contain text and formatting.
- Raw Cells: Unprocessed text for advanced use cases.
Ecosystem and Tools
| Tool | Description |
|---|---|
| JupyterLab | Modern UI and IDE-like experience for Jupyter notebooks. |
| nbconvert | Convert notebooks to HTML, PDF, scripts. |
| Voila | Turn notebooks into standalone web apps. |
| Kernels | back-end engines for different languages (e.g., IPython, Julia, R). |
| JupyterHub | Multi-user Jupyter notebook server for teams or classrooms. |
Pros and Cons
Advantages
- Combines code and explanation in one place.
- Interactive and visually rich.
- Excellent for reproducibility and communication.
- Integrates well with the Python data ecosystem.