Jupyter Notebooks Cheat Sheet

Updated 2026-04-26

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Jupyter Notebooks are interactive, web-based computing environments that combine live code, equations, visualizations, and narrative text in a single document. They support over 100 programming languages through kernels and are widely used for data analysis, machine learning, scientific computing, and education. As of 2026, the ecosystem includes two actively developed frontends — Jupyter Notebook 7 (built on JupyterLab 4, released 2023) and JupyterLab 4.x — plus AI-powered tools like Jupyter AI v3, automation tools like Papermill and nbmake, and serverless browser execution via JupyterLite. This cheat sheet covers installation, cell operations, shortcuts, magic commands, kernels, extensions, debugging, export, automation pipelines, and AI integrations.

What This Cheat Sheet Covers

This topic spans 16 focused tables and 228 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Installation & SetupTable 2: Cell OperationsTable 3: Keyboard ShortcutsTable 4: Markdown FormattingTable 5: LaTeX Math NotationTable 6: Magic CommandsTable 7: Kernels & LanguagesTable 8: Extensions & WidgetsTable 9: JupyterLab vs Jupyter NotebookTable 10: DebuggingTable 11: File Operations & ExportTable 12: Best PracticesTable 13: Sharing & CollaborationTable 14: Performance & OptimizationTable 15: Notebook Automation & PipelinesTable 16: Jupyter AI & AI Integrations

Table 1: Installation & Setup

Method	Example	Description
pip	`pip install notebook` `pip install jupyterlab`	• Installs Jupyter Notebook or JupyterLab via Python's package manager • recommended for existing Python installations
conda	`conda install -c conda-forge jupyterlab` `conda install -c conda-forge notebook`	• Installs Jupyter via Anaconda/Miniconda • preferred for data science environments with automatic dependency management
Anaconda	Download installer from anaconda.com → Launch from Anaconda Navigator	• Complete Python distribution including Jupyter, scientific libraries, and a GUI launcher • easiest entry point for beginners
jupyter notebook	`jupyter notebook` `jupyter lab`	• Starts a local Jupyter server and opens the browser • `notebook` opens the classic/v7 UI, `lab` opens JupyterLab
VS Code	Install Jupyter extension in VS Code → open any `.ipynb` file	Runs notebooks natively in VS Code with IntelliSense, integrated debugging, and version control
Google Colab	Navigate to colab.research.google.com → New Notebook	• Free cloud notebook with GPU/TPU access • no installation required, integrates with Google Drive

Table 1: Installation & Setup

Method	Example	Description
pip	`pip install notebook` `pip install jupyterlab`	• Installs Jupyter Notebook or JupyterLab via Python's package manager • recommended for existing Python installations
conda	`conda install -c conda-forge jupyterlab` `conda install -c conda-forge notebook`	• Installs Jupyter via Anaconda/Miniconda • preferred for data science environments with automatic dependency management
Anaconda	Download installer from anaconda.com → Launch from Anaconda Navigator	• Complete Python distribution including Jupyter, scientific libraries, and a GUI launcher • easiest entry point for beginners
jupyter notebook	`jupyter notebook` `jupyter lab`	• Starts a local Jupyter server and opens the browser • `notebook` opens the classic/v7 UI, `lab` opens JupyterLab
VS Code	Install Jupyter extension in VS Code → open any `.ipynb` file	Runs notebooks natively in VS Code with IntelliSense, integrated debugging, and version control
Google Colab	Navigate to colab.research.google.com → New Notebook	• Free cloud notebook with GPU/TPU access • no installation required, integrates with Google Drive