Jupyter Lab vs Notebook: Which One Should You Be Using in 2025?

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If you’re diving into data science, machine learning, or Python-based development, you’ve probably heard of Jupyter Notebook and Jupyter Lab. Both are popular tools in for Python, but understanding their differences and choosing the right one for your circumstances can really improve your workflow. Below we will break down what Jupyter Notebook and Jupyter Lab are, compare their features, and help you decide which one suits your needs in 2025.

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What is Jupyter Notebook?

Jupyter Notebook is an open-source web app that allows users to create and share documents containing live code, equations, visualizations, and narrative text. It’s used frequently for data analysis, exploration, and visualization. Jupyter Notebook’s interface is simple, intuitive, and great for beginners who want to quickly test Python code or create data-driven reports.

Key Features of Jupyter Notebook:

1. Code and Text Integration:
   • Combines live code, Markdown, and rich text in a single document.
   • Great for creating tutorials, reports, and quick prototypes.
   • Code and text blocks can be run independently, allowing users to iteratively develop projects.
2. Interactive Outputs:
   • Allows embedding of visualizations, tables, and widgets.
   • Supports libraries like Matplotlib, Seaborn, and Plotly to generate rich, interactive charts.
   • Outputs can be viewed inline, creating a seamless experience for presenting results.
3. Ease of Use:
   • Minimal setup with an intuitive interface that works directly in a web browser.
   • Easy for new users to grasp the functionality without a lot of training or prior knowledge.
4. Export Options:
   • Export notebooks to formats like HTML, PDF, or slides for easy sharing.
   • Great for creating sharable reports and presentations.
5. Broad Adoption:
   • Supported by a large community of developers and data scientists.
   • Frequent updates and tons of documentation make it beginner-friendly.

Jupyter Notebook is a great choice for those who need simplicity and don’t require advanced project management or multitasking features.

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What is Jupyter Lab?

Jupyter Lab is the next-generation interface for Jupyter, offering an integrated development environment (IDE) experience. It builds on the foundation of Jupyter Notebook but adds a modular and flexible interface. Released in 2018, Jupyter Lab has become the go-to choice for users who need a more powerful and versatile tool.

Key Features of Jupyter Lab:

1. Modular Interface:
   • Multi-tab and multi-pane layout for working on multiple notebooks, terminals, or files simultaneously.
   • Drag-and-drop features to customize your workspace and rearrange panels as needed.
   • Tabs allow for efficient switching between projects and tasks without losing context.
2. Support for Multiple File Types:
   • Handles notebooks, text files, CSVs, images, and more in a unified workspace.
   • Built-in editors for Python scripts, JSON, Markdown, and other text-based file types.
   • Users can preview or edit multiple files side by side, streamlining complex workflows.
3. Enhanced Extensions:
   • Many plugins and extensions to enhance functionality (e.g., Git integration, debugging tools).
   • Extensions like “Table of Contents”, “Variable Inspector”, and “Themes” enhance the productivity and aesthetics of the environment.
4. Real-Time Collaboration:
   • Allows multiple users to work on the same notebook (with proper setup using JupyterHub or similar configurations).
   • Facilitates better teamwork and efficient sharing of results in professional or academic environments.
5. Improved Performance:
   • Optimized for handling large datasets and demanding projects, with great resource allocation.
   • Capable of integrating with high-performance computing environments, making it a good choice for advanced analytics.
6. Scalable for Complex Projects:
   • With its ability to manage multiple notebooks, scripts, and workflows, Jupyter Lab is highly suited for complex, multi-step data science projects.

Jupyter Lab is ideal for advanced users who need a powerful, flexible, and scalable environment.

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Comparing Jupyter Lab and Jupyter Notebook

1. Interface and Usability

Jupyter Notebook:

• Simple and beginner-friendly with a clean, minimalistic layout.
• Designed for single-document focus, making it great for straightforward tasks like running code or creating tutorials.
• Easy to use for quick experiments or sharing code snippets without distractions from additional features.

Jupyter Lab:

• More complex and feature-rich interface tailored to professional use.
• Allows multiple documents, files, and tools to be open simultaneously, boosting productivity.
• Highly customizable layout with drag-and-drop functionality for advanced multitasking.

Verdict: If you prefer simplicity, go with Jupyter Notebook. If multitasking and customization are important, Jupyter Lab is the better choice.

2. Collaboration

Jupyter Notebook:

• Collaboration is limited. Sharing typically involves exporting the notebook as an HTML or PDF file or using platforms like GitHub.
• Requires third-party tools (e.g., JupyterHub) for real-time collaboration, which can be challenging for beginners.

Jupyter Lab:

• Built-in support for real-time collaboration (when properly configured with tools like JupyterHub or Binder).
• Easier to share and work collaboratively on large projects with colleagues or teams.

Verdict: Jupyter Lab is more collaborative, making it suitable for team-based workflows, especially in professional settings.

3. Extensions and Plugins

Jupyter Notebook:

• Limited plugin support with some manual setup required (e.g., nbextensions).
• Basic extensions are available but doesnt’ have integration depth of Jupyter Lab.

Jupyter Lab:

• Extensive plugin and extension ecosystem integrated seamlessly into the platform.
• Plugins are easy to install and manage directly within the interface, providing tailored functionality for diverse needs.

Verdict: Jupyter Lab wins with its robust and user-friendly extension system, making it adaptable to advanced use cases.

4. Performance

Jupyter Notebook:

• Works well for smaller datasets and simpler tasks, such as data cleaning or visualization.
• May struggle with memory heavy tasks or large datasets, limiting its applicability for demanding workflows.

Jupyter Lab:

• Optimized for handling larger datasets and more resource-intensive workflows.
• Performs better in complex, multi-step projects requiring substantial computational power.

Verdict: Jupyter Lab is more suitable for performance-heavy tasks, especially in data science and machine learning projects.

5. Learning Curve

Jupyter Notebook:

• Straightforward and easy to learn, even for those new to programming.
• Perfect for casual users, educators, and anyone needing a quick prototyping tool.

Jupyter Lab:

• Somewhat steeper learning curve due to its advanced features and customizable interface.
• Requires some experience to fully leverage its capabilities, making it more suitable for intermediate to advanced users.

Verdict: Jupyter Notebook is better for beginners, while Jupyter Lab is better for experienced users looking for a sophisticated tool.

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Similarities Between Jupyter Lab and Jupyter Notebook

Despite their differences, Jupyter Notebook and Jupyter Lab share several common features:

1. Programming Languages:
   • Both support multiple languages via Jupyter kernels, including Python, R, Julia, and more.
   • Easily extensible with additional kernels to meet specific programming needs.
2. Interactive Code Execution:
   • Execute code cells interactively and view immediate outputs.
   • Both tools excel at iterative development, allowing users to test ideas in real-time.
3. Visualization Support:
   • Compatible with popular libraries like Matplotlib, Seaborn, and Plotly, ensuring rich visual outputs.
   • Both platforms support inline chart rendering for clear and concise data representation.
4. Web-Based:
   • Both tools run in your web browser, ensuring platform independence and ease of setup.
   • No need for heavy installations, making them lightweight and accessible across devices.
5. Export Capabilities:
   • Export notebooks to formats like HTML, PDF, or slides for sharing with stakeholders.
   • Makes it easy to transform projects into presentations or reports.
6. Open-Source:
   • Both are free and supported by a large community of developers.
   • Open-source nature ensures constant updates, bug fixes, and feature enhancements.

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When to Use Jupyter Notebook

Choose Jupyter Notebook if:

• You’re a beginner or casual user who needs a simple and intuitive interface.
• Your work involves small to medium datasets that don’t demand heavy computational resources.
• You need a distraction-free environment for creating tutorials, reports, or quick prototypes.
• Simplicity and ease of use are your top priorities.

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When to Use Jupyter Lab

Choose Jupyter Lab if:

• You’re an experienced user or require advanced features for your workflows.
• Your projects involve large datasets, complex analyses, or resource-intensive computations.
• You need to work with multiple files or tools simultaneously.
• Real-time collaboration and extensive customization are necessary for your tasks or workflows.
• You want to take full advantage of the latest extensions and plugins.

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Final Thoughts: Jupyter Lab vs Notebook — Which One Should You Use in 2025?

In 2025, both Jupyter Notebook and Jupyter Lab remain great tools, each catering to specific needs. Jupyter Notebook’s simplicity makes it ideal for beginners and straightforward tasks, while Jupyter Lab’s advanced features and flexibility are perfect for more advanced users and team collaborations.

For beginners or users working on small-scale projects, Jupyter Notebook is an excellent choice. Its intuitive interface ensures that even those with minimal programming experience can quickly create and share their work. Educators and students also benefit from its simplicity when creating lessons or conducting experiments.

On the other hand, professionals and teams handling larger datasets or complex projects will find Jupyter Lab to be a valuable tool. Its modular interface, real-time collaboration capabilities, and robust extension ecosystem provide unparalleled flexibility and efficiency. From machine learning workflows to data visualization dashboards, Jupyter Lab allows users to tackle sophisticated tasks with ease.

Ultimately, your choice depends on your individual needs, skill level, and project requirements. It’s worth noting that you don’t have to pick just one—you can use both tools in your workflow, allowing you to leverage their strengths as needed.

Ready to get started? Check out our detailed guides on How to Install Jupyter Notebook, How to Install Jupyter Lab, and How to Install Python to set up your environment today!