Jupyter Notebook and its successor JupyterLab are interactive computing environments that combine live code, visualizations, and narrative text in a single document, widely adopted across data science, scientific research, and machine learning workflows. The Jupyter ecosystem extends beyond basic notebooks through IPython's magic commands, kernel management, rich display objects, interactive widgets, and extensibility via nbformat, papermill, nbconvert, jupytext, and Voilà. Understanding Jupyter means mastering two distinct interaction modes (command and edit), cell execution semantics (order matters more than position), and the distinction between the notebook interface and the underlying IPython kernel that executes code and maintains state across cells.
What This Cheat Sheet Covers
This topic spans 20 focused tables and 174 indexed concepts, 77 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
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Table 1: Cell Types
Every notebook is built from three kinds of cells, and knowing which to reach for shapes how the document reads and runs. Code cells execute and carry state; Markdown cells hold the narrative and math; raw cells pass straight through to whatever export format you target.
| Type | Example | Description | |
|---|---|---|---|
x = 42print(x) | • Executable Python (or other kernel language) that produces numbered outputs • execution order tracked by In [N] label• state persists across cells | ||
# Heading**bold** text$E=mc^2$ | • Rich text with headers, lists, LaTeX math using $ $ for inline or $$ $$ for display equations, images, and HTML• renders when executed | ||
\begin{equation}x = y\end{equation} | • Unmodified pass-through content for nbconvert output formats like LaTeX or reStructuredText • bypasses rendering entirely |