What is notebook?

A notebook in the tech world is an interactive document that lets you write and run code, add text, images, and visualizations all in one place. The most common type is a Jupyter Notebook, which runs in a web browser and supports languages like Python, R, and Julia.

Let's break it down

  • Cells: The notebook is made up of small blocks called cells. There are two main types: code cells (where you write program code) and markdown cells (where you write formatted text, headings, lists, and embed images).
  • Kernel: This is the engine that executes the code you write. When you run a code cell, the kernel processes it and returns the result right below the cell.
  • Interface: You interact with the notebook through a web page that shows the cells, lets you add, delete, move, and run them, and displays outputs like numbers, tables, plots, or even interactive widgets.
  • File format: Notebooks are saved as .ipynb files, which are just JSON data that store the cells, their content, and metadata.

Why does it matter?

Notebooks make it easy to combine code, results, and explanations in a single, shareable document. This helps you explore data, test ideas quickly, and create tutorials or reports that others can run and understand without needing a separate script and separate documentation.

Where is it used?

  • Data science and analytics: Exploring datasets, cleaning data, building models, and visualizing results.
  • Education: Teaching programming, statistics, and machine learning with live examples.
  • Research: Documenting experiments, sharing reproducible code, and publishing interactive papers.
  • Prototyping: Quickly testing snippets of code before turning them into full applications.
  • Business reporting: Creating dashboards and reports that combine narrative with live data.

Good things about it

  • Interactive: See results instantly after running a cell.
  • Combines code and documentation: Makes projects easier to understand and share.
  • Supports many languages via different kernels.
  • Great for visualizations and interactive widgets.
  • Easy to export to HTML, PDF, or slides for presentation.
  • Large community and many extensions (e.g., JupyterLab, nbextensions).

Not-so-good things

  • Not ideal for large, production‑level codebases; version control can be messy.
  • Heavy memory usage if many cells keep large data in the kernel.
  • Can become disorganized if cells are not structured well.
  • Dependency on a running kernel; if it crashes, you lose the current state.
  • Exporting to plain scripts may lose some of the rich formatting.