.png){kind=logo}
Jupyter Notebook is simple and ideal for learning, teaching, and small coding tasks.
JupyterLab provides advanced tools and flexible layouts for professional data work.
Both share the same ecosystem, allowing easy transition as project needs grow.
Data science, coding, and research increasingly rely on interactive tools that speed up work and make it more visual. Popular choices include Jupyter Notebook and JupyterLab. They both come from the same Jupyter ecosystem, use the same file format, but have different interfaces and capabilities. Knowing what each can do is useful when deciding which to use for specific tasks.
Jupyter Notebook began as a small product that allowed you to write and view code, text, and charts all together. Each notebook consisted of cells that either ran code or displayed text descriptions. It is lightweight, very easy to use, and handles small projects or classroom work well.
JupyterLab was created later as an advanced version of Jupyter Notebook. This tool has the same base but provides a more organized, flexible workspace. JupyterLab allows users to open multiple files, terminals, and notebooks in a single window. The layout feels like coding software, where users can move panels, compare results, and manage multiple files.
Also Read: Google Colab vs Jupyter Notebook: Which One is Better?
Jupyter Notebook has a simple design. It runs in a browser, and each notebook opens in a new tab. Work is done in a straight line from top to bottom.
JupyterLab allows multiple tabs and panels in one window. A notebook, a terminal, and even a text file can be open side by side. The experience is more organized and easier to switch between tasks.
In Jupyter Notebook, file management is basic. Files simply appear in a list, and each notebook opens in a different window.
In JupyterLab, files can be managed like an IDE. There is a file browser, and you can drag and drop files. You can work on multiple files at once and view them together.
Jupyter Notebook has a few add-ons, but they are constrained.
JupyterLab has more built-in tools. It supports terminals, code editors, and various extensions that improve workflow.
Jupyter Notebook suits teachers, students, and those doing small experiments. It is perfect for learning, writing tutorials, or testing code.
JupyterLab fits professionals working on big projects. It helps manage data, scripts, and visualizations across multiple files.
Even though JupyterLab can be a powerful tool, its design can make it look confusing. While JupyterLab has a far richer interface, the simpler interface of Jupyter Notebook might be better for beginners and easier for some users.
If you are teaching and working on small or quick data exploration, the benefits of using Jupyter Notebook prevail. Jupyter Notebook boots quickly, is visually simple, and lets users focus on their content rather than the setup.
If you are working on a larger data journalism project or a multi-file analysis, JupyterLab is likely the most effective solution. JupyterLab provides a vast set of tools and minimizes interruptions, offering an increasingly seamless development experience for advanced coding and visualizations.
Both use the same file format, so switching between them is simple. If a project needs many extensions or tools like Git and JupyterLab, it's more future-ready. But for light work or computers with limited memory, Jupyter Notebook is easier to run.
Installation is also simple for both pip and conda. However, JupyterLab might need some time to understand its layout and features. Once familiar, it helps save time in complex projects.
Neither option is strictly better than the other. Jupyter Notebook works effectively for quick experiments, classroom activities, and simple data analysis. JupyterLab is more appropriate for larger projects that involve more files, more tools, and greater flexibility in working. Many users will begin with Jupyter Notebook and later transition to JupyterLab as needed.
Jupyter Notebook is an excellent way for students with a science background to get started with data-driven storytelling. However, JupyterLab will become more useful when handling multi-layered or complex projects. Both environments have offered the possibility of a more interactive data exploration experience and a more engaging way to learn.
Q1. What is the main difference between Jupyter Notebook and JupyterLab?
Jupyter Notebook is linear and straightforward, while JupyterLab offers a flexible, multi-panel layout for handling bigger projects.
Q2. Who should use Jupyter Notebook and who should use JupyterLab?
Students and beginners benefit from Notebook’s simplicity, while professionals prefer JupyterLab for large data projects.
Q3. Can files created in Jupyter Notebook be opened in JupyterLab?
Yes, both use the same .ipynb format, allowing seamless switching without losing any code, text, or visualizations.
Q4. Is JupyterLab more difficult to use than Jupyter Notebook?
JupyterLab can feel complex initially, but once users learn its layout, it becomes more efficient for multitasking and analysis.
Q5. Which tool is better for data journalism or research projects?
JupyterLab is better for large data journalism or research projects needing multiple files, extensions, and visual tools.
