What is the difference between Jupyter Notebook and JupyterLab?
Jupyter Notebook, as the name implies, is a notebook-like environment that blends code and documentation seamlessly—perfect for beginners and straightforward projects in Python and several other languages.
On the other hand, JupyterLab is like an advanced, web-based development environment that is fantastic for large-scale projects. It provides you with multiple tabs, panels, and extension features for a modular user interface.
Key differences summarised:
- Jupyter Notebook: A single-document environment. Easy-peasy, perfect for linear workflows.
- JupyterLab: A multi-document user interface. Supports complexity and customization galore.
Installing and starting JupyterLab is as easy as:
JupyterLab allows you to organize cells using drag-and-drop, use integrated data viewers, and also a marketplace for extensions, simplifying the data science workflow significantly.
Switching Made Easy
Transitioning from Jupyter Notebook to JupyterLab is as smooth as butter because of the well-thought-out compatibility between the two. Fear not, as there's no learning cliff here. If you are a Jupyter Notebook user, you will feel at home in JupyterLab. Besides Python, both interfaces support a host of different programming languages, making these tools the best buddies of data scientists.
Enhanced User Experience
Feel free to set your development ambiance in JupyterLab with your preferred themes and font sizes. A visual debugger and keyboard shortcuts are there to elevate your coding experience to another level.
Workspace and Extension Features
In JupyterLab, you can customize your workspace to match your workflow by choosing horizontal or vertical buffer splits. JupyterLab is not static—it grows with you. Dive into the GitHub-hosted extensions to introduce tons of additional utilities and customize JupyterLab to your heart’s content.
Tools for Power Users
JupyterLab isn't merely about handling a bunch of documents—far from it. Here's what it empowers you with:
- Data Visualization: Unleashes powerful tools for advanced analysis and Dynamic Dash apps.
- Versatile Language Support: Not just Python—say hello to support for R, Julia, and more.
- Special Code Console: Execute freestanding code snippets side-by-side with your notebooks.
- Terminal: Run command-line operations within the same environment.
- File Editing facilities: Includes built-in text and markdown editors for all-inclusive project management.
Choose Wisely
When to pick which:
- Jupyter Notebook: Perfect for learning, giving demonstrations, and performing quick data exploration.
- JupyterLab: Tailored for large-scale projects, collaborative initiatives, and in-depth data analysis.
Problem-solving with JupyterLab
JupyterLab shines when:
- Dealing with Multiple Files: Open, edit, and manage several file types in one place.
- As an Integrated Development Environment (IDE): The complete system-level control it provides makes it ideal for full-scale software development projects.
- Creating Custom Layouts: Freely adjust your workspace to suit your workflow.
- In the Open Source Community: Easy to contribute and improve, making JupyterLab beneficial for all users.
References
- Project Jupyter | Home — the official homepage with comprehensive information about Project Jupyter.
- JupyterLab Documentation — the definitive guide to understanding JupyterLab features and usage.
- The JupyterLab Interface — depicts the detail of JupyterLab's user interface and components.
- GitHub - jupyterlab/jupyterlab — source code and contribution hub for JupyterLab development.
- Extensions — JupyterLab Documentation — how to give a personal touch to your JupyterLab workspace with extensions.
- Migrating from Jupyter Notebook to JupyterLab — practical insights into the transition from Jupyter Notebook to JupyterLab.
- Medium — a well-crafted article discussing what sets JupyterLab apart from Jupyter Notebooks.
Was this article helpful?