Skip to content
JupyterLab vs Notebook: A Comprehensive Comparison

JupyterLab vs Notebook: A Comprehensive Comparison

When it comes to interactive computing environments, JupyterLab and Jupyter Notebook are two of the most popular choices. Though Jupyter Notebook is the more established option with a simpler interface, JupyterLab is the newer, more versatile interface with real-time collaboration, support for multiple file formats, and built-in terminals and code consoles. In this comprehensive comparison, we dive into the key differences, features, and considerations to help you choose the best option for your needs.

Want to quickly create Data Visualization from Python Pandas Dataframe with No code?

PyGWalker is a Python library for Exploratory Data Analysis with Visualization. PyGWalker (opens in a new tab) can simplify your Jupyter Notebook data analysis and data visualization workflow, by turning your pandas dataframe (and polars dataframe) into a Tableau-style User Interface for visual exploration.

PyGWalker for Data visualization (opens in a new tab)

What is JupyterLab?

JupyterLab is an interactive development environment for working with notebooks, code and data. It is the next-generation user interface for Project Jupyter offering all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. JupyterLab will eventually replace the classic Jupyter Notebook.

JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. You can arrange multiple notebooks, text files, terminals, output areas, and more on a single layout with multiple panels, tabs, splitters, and collapsible sidebars with a file browser, command palette, and task-focused workspaces.

What is Jupyter Notebook?

Jupyter Notebook, on the other hand, is a web-based interactive computational environment for creating Jupyter notebook documents. It supports several languages like Python (IPython), Julia, R, etc. and is mostly used for data analysis, data visualization, and other interactive, exploratory computing.

The Jupyter Notebook combines three components. The first is the notebook app, a web-based application for writing and running code interactively and authoring notebook documents. The second component, kernels, are separate processes started by the notebook app that runs users' code in a given language and returns output back to the notebook web application. The third component is notebook documents, a representation of all content visible in the notebook web application, including inputs and outputs of the computations, narrative text, equations, images, and rich media representations of objects.

Differences between JupyterLab and Jupyter Notebook

While both JupyterLab and Jupyter Notebook serve as interactive computing environments, they differ in several ways, primarily in their user interface, functionality, and flexibility.

User Interface

Jupyter Notebook has a simpler, more lightweight interface. It is primarily a single-document interface, presenting the user with a single, straightforward document that is easy to navigate and understand. This simplicity makes it an excellent choice for beginners or those who prefer a more straightforward approach to their coding environment.

JupyterLab, however, offers a more versatile and feature-rich interface. It is a multi-document, multi-tasking interface that allows users to work with several notebooks or files simultaneously, view their data in a variety of ways, and even integrate their work with third-party extensions. This makes JupyterLab a more powerful tool for more complex or larger-scale projects.

Functionality and Flexibility

Jupyter Notebook offers basic functionality for interactive computing. You can write and run

code, view the results, and write explanations or narratives alongside your code. It also supports the integration of rich media, such as images and videos, and the use of various programming languages like Python, R, and Julia.

JupyterLab takes this functionality to the next level. In addition to all the features offered by Jupyter Notebook, JupyterLab allows you to open multiple notebooks or files side-by-side in the work area, organize your workspace with drag-and-drop functionality, and use tools like a file browser, command palette, markdown preview, and more. It also supports real-time collaboration, making it a great tool for team projects.

File Formats

Both JupyterLab and Jupyter Notebook support a wide range of file formats. However, JupyterLab has a slight edge in this regard. In addition to the notebook file format (.ipynb), JupyterLab also supports other file formats like markdown (.md), JavaScript (.js), JSON (.json), HTML (.html), CSS (.css), and more. This makes JupyterLab a more versatile tool for working with different types of files and projects.

JupyterLab vs Jupyter Notebook for Data Science and Machine Learning

When it comes to data science and machine learning, both JupyterLab and Jupyter Notebook offer robust capabilities. However, the multi-document interface of JupyterLab can be particularly beneficial in these fields. The ability to view multiple notebooks side-by-side allows for more efficient comparison of data, models, and results. Additionally, the integrated terminal in JupyterLab can be a powerful tool for running shell commands, which can be useful for tasks like data preprocessing or installing libraries.

Jupyter Notebook, while simpler, still offers all the necessary features for data science and machine learning. The ability to write code, run it, and see the results in one place, along with explanatory text, makes it a great tool for these tasks. The choice between the two often comes down to personal preference and the specific requirements of the project.

JupyterLab vs Jupyter Notebook for Beginners

For beginners, Jupyter Notebook's simpler interface can be more approachable. Its single-document interface is straightforward and easy to navigate, making it a great choice for those new to coding or data science.

JupyterLab, while more complex, also has features that can benefit beginners. The ability to view multiple documents at once can be helpful for learning and comparing code. Additionally, its integrated terminal and command palette can be powerful tools as one's coding skills advance.

In conclusion, both JupyterLab and Jupyter Notebook have their strengths and can be effectively used for interactive computing, data science, and machine learning. Your choice between the two will depend on your specific needs, preferences, and the nature of your project.

Frequently Asked Questions

What are the differences between JupyterLab and Jupyter Notebook?

JupyterLab and Jupyter Notebook differ primarily in their user interface, functionality, and flexibility. Jupyter Notebook has a simpler, more lightweight interface, while JupyterLab offers a more versatile and feature-rich interface. JupyterLab also provides more functionality and flexibility, with features like a multi-document interface, integrated terminal, and real-time collaboration.

Which one is better, JupyterLab or Jupyter Notebook?

Neither JupyterLab nor Jupyter Notebook is inherently better than the other. The choice between the two depends on your specific needs, preferences, and the nature of your project. Jupyter Notebook's simpler interface may be more approachable for beginners, while JupyterLab's advanced features and flexibility make it a powerful tool for more complex or larger-scale projects.

Can I install both Jupyter Notebook and JupyterLab?

Yes, you can have both Jupyter Notebook and JupyterLab installed and use them interchangeably. They are part of the same Project Jupyter and are designed to work together seamlessly.