In the field of data science and Python programming, Jupyter Notebook and JupyterLab are two widely used tools for writing and executing interactive code. Although both belong to the Jupyter ecosystem, each platform offers its own strengths in terms of interface, features, and extensibility. So, which one is the right choice for your needs? In this article, FPT AI Factory will provide a detailed comparison and practical recommendations to help you choose the most suitable tool.
1. What is a Jupyter Notebook?
Jupyter Notebook is an open-source, web-based programming environment that allows users to create and share documents containing code, text (Markdown), images, and visualizations in one place.
It is designed to support interactive computing, meaning you can write and execute code in small units called cells and instantly view the output below. This makes learning, experimentation, and data analysis more intuitive and efficient.
Key features of Jupyter Notebook
- Cell-based execution: Run code step by step for better control and debugging
- Inline output display: View data tables, charts, and images directly
- Multi-language support: Primarily Python, with support for R, Julia, and more
- Code and documentation integration: Easily add notes and explanations within the notebook
- Beginner-friendly: Simple interface, ideal for those new to programming
Jupyter Notebook is designed to support interactive computing
2. What is JupyterLab?
JupyterLab is the next-generation interactive development environment from Project Jupyter, built as an extension and upgrade of Jupyter Notebook. It is a powerful web-based platform that allows you to work with multiple files, notebooks, and tools within a single interface.
Unlike the traditional Jupyter Notebook, JupyterLab provides an experience similar to an Integrated Development Environment (IDE), offering greater flexibility in file management and customization.
Key features of JupyterLab
- Tab-based interface: Open and work with multiple notebooks, code files, and terminals simultaneously
- Advanced file management: Easily drag, drop, organize, and access various file types
- Integrated terminal: Run system commands directly within JupyterLab
- Extensible with plugins: Customize features and interface based on your needs
- Enhanced code editor: Supports auto-completion and syntax highlighting
JupyterLab is the next-generation interactive development environment from Project Jupyter
3. Comparison with Jupyter Notebook vs JupyterLab
To help you better understand the differences between Jupyter Notebook and JupyterLab, the table below provides a detailed comparison based on key criteria such as features, interface, file management, and suitability for different use cases.
| Criteria | Jupyter Notebook | JupyterLab |
| Features | Basic, focused on single notebooks | Advanced, integrates multiple tools in one environment |
| Interface | Simple, user-friendly, single-page layout | Modern, multi-tab interface similar to an IDE |
| File Management | Limited, not ideal for handling multiple files | Powerful, supports drag-and-drop and multi-file management |
| Customization | Limited customization options | Highly customizable with extensions/plugins |
| Collaboration | Basic, mainly through file sharing | Better with collaboration-supporting tools |
| Flexible Access | Works with one notebook at a time | Enables working with multiple notebooks, terminals, and files simultaneously |
| Debugging | Limited, mainly using print/log | More advanced with debugger support |
| Resource Usage | Lightweight, suitable for low-spec machines | Heavier, requires more system resources |
| Ideal Use Cases | Learning, quick experiments, small projects | Large projects, professional workflows, multitasking |
4. When Should You Use Jupyter Notebook vs JupyterLab?
4.1. Use cases for Jupyter Notebook
Jupyter Notebook is well-suited for roles such as students, data analysts, junior data scientists, and educators who need a simple environment for learning, basic data analysis, and quick experimentation. It is especially effective for presenting ideas, creating demos, and building step-by-step prototypes.
4.2. Use cases for JupyterLab
JupyterLab phù hợp hơn với các vai trò như data scientist, machine learning engineer, AI engineer hoặc researcher – những vị trí thường xuyên làm việc với dự án phức tạp và nhiều tệp cùng lúc. Đây là lựa chọn lý tưởng trong môi trường chuyên nghiệp, khi bạn cần tối ưu quy trình làm việc (workflow) và dễ dàng mở rộng để phát triển các hệ thống lớn.
4.3. Recommendation by user level
For new users, the best starting point is usually Jupyter Notebook because it offers a simpler and more focused experience. As users become more familiar with notebook-based workflows, they can move to JupyterLab for more advanced project needs.
- Start with Jupyter Notebook if you are learning how to write, run, and debug interactive code for the first time.
- Move to JupyterLab once you need a more flexible workspace with multi-file support, side-by-side panels, and better project organization.
- Choose based on workflow complexity: Jupyter Notebook is better for simple learning and quick experiments, while JupyterLab is better for larger and more structured projects.
FPT AI Notebook will helps you get started with AI and Data Science faster (Source: FPT AI Factory)
In addition to these tools, AI Notebook is a practical option for users who want to get started with AI and data science more quickly. Available on FPT AI Factory, it provides a pre configured environment with CPU and GPU resources ready to use, allowing you to focus on coding, experimentation, and building projects instead of setup.
To support new users, the Starter Plan includes 100 dollars in free credits valid for 30 days, along with access to AI Notebook, GPU Container, GPU Virtual Machine, and other AI services. It also supports more than 20 AI models, including Llama 3.3, and provides up to 5 million tokens for learning and real world experimentation. These credits are granted immediately after registration, so you can log in and start using the platform right away.
For businesses or organizations that require customized solutions or plan to deploy at scale, please contact FPT AI Factory through the contact form to receive tailored consultation and support for your specific needs.
Contact Information:
Hotline: 1900 638 399
Email: support@fptcloud.com
