Jupyter Notebook

Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary

An open-source web application for creating documents that combine runnable code, its output, and narrative text; the standard format for data exploration.

What is Jupyter Notebook?

Jupyter Notebook is an open-source web application for creating documents that mix runnable code, its output, and explanatory text in a single file. Run a cell of code and its result – a table, a chart, an error – appears directly beneath it.

That format made it the default workspace for data exploration in Python. The analysis, its results, and the reasoning behind them live in one shareable document instead of being scattered across scripts and reports.

How Jupyter Notebook Works

A notebook is a sequence of cells. Code cells run in a kernel, a live language process that executes them and returns results; markdown cells hold formatted text, headings, and equations. The kernel keeps state between cells, so an analysis builds step by step: load data in one cell, inspect it in the next, plot it in a third.

Notebooks save as .ipynb files with code and outputs together. Python is the most used kernel, but kernels exist for R, Julia, and many other languages.

Jupyter Notebook vs JupyterLab

Jupyter Notebook is the classic single-document interface: one notebook per browser tab. JupyterLab is the same project’s IDE-style successor; it arranges notebooks, terminals, text editors, and a file browser side by side in one window.

Both run the same kernels and open the same .ipynb files, so the choice is about interface, not capability.

CriterionJupyter NotebookJupyterLab
InterfaceOne document per browser tabMulti-panel workspace with tabs and split views
ScopeThe notebook itselfNotebooks plus terminals, editors, and a file browser
Best forFocused, linear analyses and teachingLarger projects mixing notebooks, scripts, and data files
File formatSame .ipynb; opens in eitherSame .ipynb; opens in either

Example of a Jupyter Notebook

An analyst investigating a spike in customer refunds opens a fresh notebook. The first cell loads the refunds table with pandas, the second prints summary statistics, and the third plots refunds per week. The spike shows up in the chart, right below the code that produced it.

She narrows down by product in two more cells and writes the finding in a markdown cell: the spike traces to a single product after a firmware update. The finished notebook, with its code, charts, and written conclusion, is also the report she shares with the team.

Related AI terms: JupyterLab · Pandas · NumPy · Exploratory Data Analysis · Data Science

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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About

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