Interactive computing has revolutionized data science, machine learning, and academic research. With the advent of tools such as Jupyter Notebook and JupyterLab, users can write code, visualize data, and document their process all in one place. But with both tools now available and serving similar purposes, many users find themselves asking: Which is better—Jupyter Notebook or JupyterLab?
TLDR: Which Should You Choose?
If you’re looking for a familiar, lightweight, and streamlined environment, Jupyter Notebook is ideal for you. For more advanced features, greater customization, and an integrated workspace, JupyterLab is the better option. Essentially, JupyterLab can be viewed as the next-generation interface, improving upon the traditional notebook without losing its essence. So, if you’re committed to long-term projects or collaborative work, JupyterLab is likely to serve you better.
Background: What Are Jupyter Notebook and JupyterLab?
Both Jupyter Notebook and JupyterLab are part of the Project Jupyter ecosystem. They allow users to create and share documents that contain live code, equations, visualizations, and narrative text. However, there are significant differences in their architecture and capabilities.
- Jupyter Notebook was initially developed as a successor to IPython Notebook, offering a simple, yet powerful UI for mixing code and text in a linear document.
- JupyterLab, released later, provides a more sophisticated interface with full IDE-like features, enabling users to open multiple documents, terminals, and code consoles in a single tabbed workspace.
User Interface and Experience
One of the fundamental ways these two platforms differ is in their user interface and overall user experience. If you’re used to working in Jupyter Notebook, its stripped-down, document-focused interface may feel intuitive and distraction-free. Here’s how the user experience compares:
- Jupyter Notebook: Designed with simplicity in mind. You open one notebook per tab and proceed linearly—great for simple tasks and new learners.
- JupyterLab: Highly modular and flexible with support for drag and drop, tabbed views, and multiple file types. You can open multiple notebooks, terminals, and consoles simultaneously, making it perfect for multitasking.
Feature Comparison
Let’s take a closer look at how the two stack up when it comes to features:
| Feature | Jupyter Notebook | JupyterLab |
|---|---|---|
| Multi-Notebook Workspace | No | Yes |
| Side-by-Side Views | No | Yes |
| Extensibility via Plugins | Limited | Highly Extensible |
| Integrated Terminals | Yes, separate tab | Yes, integrated |
| Real-time Preview | No | Yes |
| Git Integration | Via extension | Built-in or more seamless via extensions |
Customization and Extensions
JupyterLab truly shines when it comes to customization. It features a powerful extension system that allows users to install plugins to add new functionality. Want to enable real-time collaboration, version control with Git, or visual debugging tools? JupyterLab’s got you covered.
In contrast, while you can extend Jupyter Notebook via nbextensions, the process is more manual and less cohesive than what JupyterLab offers.
Popular Extensions for JupyterLab:
- JupyterLab Git: Integrated Git version control directly into the interface.
- JupyterLab Variable Inspector: View current variables and their values in a side panel.
- Interactive Widgets: Create sliders, dropdowns, and other UI elements using ipywidgets.
Performance and Reliability
Performance-wise, both platforms rely on the same Jupyter kernel infrastructure, so there’s generally no major difference in computation speed or resource usage. However, JupyterLab can feel heavier, especially when many plugins are installed or when multiple documents are open simultaneously.
In terms of reliability and stability, both tools are mature and widely used. However, JupyterLab, being the more actively developed and future-facing tool, receives performance improvements and security updates more frequently.
Use Cases: When to Use What?
Choosing between Jupyter Notebook and JupyterLab often comes down to your specific use case. Here are some scenarios to guide you:
- Educational Purposes: Jupyter Notebook is easier for beginners to pick up. It’s clean and less intimidating, making it ideal for teaching and workshops.
- Data Science Projects: JupyterLab is more suitable due to its multitasking capability and integrated tools for version control, terminals, and visualizations.
- Production Workflows: JupyterLab supports better collaboration, extensibility, and modular views, making it ideal for production environments and team projects.
Community and Support
Another important factor is the community and available support. Since both tools come from the same ecosystem, you’ll find robust documentation, an active developer community, and extensions for each. However, JupyterLab being the newer and actively promoted interface, is now the primary focus of new development efforts in the Jupyter project.
That means new extensions, features, and community contributions are increasingly geared toward enhancing JupyterLab instead of the classic notebook interface.
Compatibility and Transition
If you’re already using Jupyter Notebook and concerned about transitioning to JupyterLab, worry not. Both platforms use the same underlying file format (.ipynb), so you can open the same notebooks in either environment without compatibility issues.
In fact, JupyterLab is designed to be backward-compatible with most of the classic Jupyter Notebook use cases. The transition is usually smooth and doesn’t require re-training your team significantly.
Final Verdict: Jupyter Notebook vs. JupyterLab
There isn’t a one-size-fits-all answer, but here’s how you might think about it:
- Choose Jupyter Notebook if you value simplicity, are teaching or learning, or are working on quick prototypes or one-off analyses.
- Choose JupyterLab if you need a modern, powerful, and expandable interface for complex workflows, collaboration, or production-level data science.
To sum it up, JupyterLab is not just a replacement for Jupyter Notebook—it’s an evolution. While the notebook interface is here to stay and still widely used, JupyterLab represents the project’s vision for the future.
So, as your projects scale and your needs grow more advanced, it might be time to switch to JupyterLab and experience a new level of productivity and integration.
