Learn JUPYTER-NOTEBOOK with Real Code Examples

Web-based front-end (browser interface)

Backend kernel executes code in selected language

JSON-based notebook format (.ipynb)

Supports extensions and plugins for additional features

Optional integration with cloud platforms (e.g., JupyterHub, Colab)

Browser-based notebook interface

Cells rendered as code or markdown

Inline output for plots, tables, and widgets

Kernel executes code and manages state

Exportable to multiple formats for sharing

Client-server model: front-end (browser) and kernel (back-end)

Notebook document JSON structure (.ipynb)

Kernel-per-language execution system

Extension and plugin support for customization

Integration with cloud services and version control

Data science experiments

Machine learning research

Scientific reproducible research

Interactive teaching and tutorials

Collaborative cloud-based projects

Interactive and exploratory programming

Combine code, text, and visualizations

Reproducibility and shareability

Language-agnostic via kernels

Extendable with plugins and cloud integration

Small: single notebook analysis

Medium: multi-notebook projects

Large: JupyterHub or cloud clusters for teams

Enterprise: integrate with cloud computing resources

Global: share notebooks via GitHub or nbviewer

Move notebooks via GitHub or file export

Install required packages in new environment

Check kernel compatibility

Re-run all cells to ensure reproducibility

Update paths for local datasets if needed