If you work with data science, machine learning, or Python programming, you’ve likely encountered Jupyter Notebook and JupyterLab. These tools are widely used for interactive coding, but many users wonder: Jupyter Notebook vs JupyterLab—what’s the difference?
Both platforms are part of the Project Jupyter ecosystem, but they offer distinct features and workflows. In this article, we’ll compare Jupyter Notebook and JupyterLab, discuss their key differences, and help you decide which one is best for your needs.
What Is Jupyter Notebook?
Jupyter Notebook is an open-source, web-based interactive computing environment that allows users to create and share documents containing:
- Live code (Python, R, Julia, etc.)
- Visualizations
- Markdown text
- Mathematical equations
- Interactive widgets
It was designed to make data science and computational research more interactive, and it has been widely adopted in academia and industry.
Key Features of Jupyter Notebook
✔ Simple and lightweight – Easy to install and use. ✔ Supports multiple programming languages – Primarily Python, but also R, Julia, and more. ✔ Markdown support – Enables writing explanatory text within notebooks. ✔ Cell-based execution – Run code in small sections rather than all at once. ✔ Rich visualization – Integrates well with libraries like Matplotlib, Seaborn, and Plotly.
However, Jupyter Notebook has some limitations, such as a basic interface, lack of tabbed views, and limited workspace organization.
What Is JupyterLab?
JupyterLab is the next-generation interactive development environment (IDE) for Jupyter, designed to be more flexible, modular, and user-friendly than Jupyter Notebook.
It provides a more advanced interface with multiple panes, drag-and-drop functionality, and better workspace organization for handling large-scale projects.
Key Features of JupyterLab
✔ Multi-document interface – Work with multiple notebooks, text files, and terminals in a tabbed layout. ✔ Integrated file browser – Navigate your project’s files directly in the UI. ✔ Drag-and-drop functionality – Move cells, files, and outputs easily. ✔ Customizable workspaces – Arrange panels to match your workflow. ✔ Supports extensions and plugins – Enhance functionality with third-party add-ons. ✔ Terminal and text editor included – No need for separate software.
While JupyterLab offers more features and flexibility, it is slightly more resource-intensive and may be overwhelming for new users.
Jupyter Notebook vs JupyterLab: Side-by-Side Comparison
| Feature | Jupyter Notebook | JupyterLab |
|---|---|---|
| Interface | Single-window notebook view | Multi-tabbed, flexible layout that allows multiple files and documents to be opened side-by-side. |
| File Management | Limited, basic interface with manual file opening and saving | Full file browser with drag-and-drop support, making it easier to navigate large projects. |
| Extensions & Plugins | Limited support for customization | Extensive plugin ecosystem that allows adding new functionalities like real-time collaboration, debugging tools, and Git integration. |
| Workspace Organization | One notebook at a time in a single tab | Multiple documents, terminals, and notebooks can be opened in a split view for better multitasking. |
| Performance | Lightweight and fast, optimized for smaller-scale projects | Slightly heavier but more powerful, better suited for large-scale projects and enterprise use. |
| Coding & Execution | Supports inline execution but lacks an integrated debugging environment | Provides better debugging tools and supports real-time collaboration for teams. |
| Ideal for | Beginners, quick scripting, small-scale projects | Large-scale projects, advanced users, and team collaboration. |
When to Use Jupyter Notebook vs JupyterLab
Use Jupyter Notebook if:
✅ You prefer a simple, minimal interface that is easy to use. ✅ You are new to Python or Jupyter and want a beginner-friendly environment. ✅ You are working on small-scale projects, scripts, or quick data analysis. ✅ You don’t require advanced file management or multiple open documents. ✅ You prefer a lightweight, fast-loading tool that doesn’t consume many system resources. ✅ You need quick prototyping or teaching environments where simplicity is key.
Use JupyterLab if:
✅ You want a more powerful, feature-rich development environment with improved flexibility. ✅ You frequently work with multiple notebooks, scripts, and documents at the same time. ✅ You require better file organization, an integrated terminal, and a built-in file manager. ✅ You want to customize your workspace with extensions, themes, and plugins. ✅ You need support for collaborative projects, real-time debugging, or Git integration. ✅ You work with large datasets, complex workflows, or long-term projects that require better organization. ✅ You want a modern IDE-like interface similar to VS Code but optimized for Jupyter workflows.
Frequently Asked Questions (FAQs)
1. Is JupyterLab a replacement for Jupyter Notebook?
Yes, JupyterLab is the official successor to Jupyter Notebook, but Jupyter Notebook is still maintained and widely used.
2. Does JupyterLab support Jupyter Notebook files (.ipynb)?
Yes, JupyterLab fully supports .ipynb files and can open, edit, and run them just like Jupyter Notebook.
3. Which one is better for machine learning and data science?
- Jupyter Notebook is great for simple tasks, quick data exploration, and teaching.
- JupyterLab is better for handling large datasets, multi-file projects, and integrating various tools.
4. Can I install both Jupyter Notebook and JupyterLab?
Yes, you can install and use both on the same machine without conflicts.
5. How do I switch from Jupyter Notebook to JupyterLab?
If JupyterLab is installed, simply run:
jupyter lab
instead of:
jupyter notebook
Conclusion
Jupyter Notebook vs JupyterLab—Which one should you use?
- If you need a simple and lightweight tool for quick data analysis, Jupyter Notebook is the better choice.
- If you need a more advanced, flexible, and feature-rich environment, JupyterLab is the way to go.
Both tools are valuable in the data science and machine learning ecosystem, and choosing the right one depends on your workflow and project complexity.