A Comprehensive Guide to JupyterLab

JupyterLab is a powerful web-based environment for working with Jupyter notebooks, code, and data. It streamlines the data science workflow, enabling users to develop, document, and share complex projects within a single, customizable interface. It integrates tools like notebooks, text editors, and terminals in one unified workspace.

By Saturn Cloud | Tuesday, April 11, 2023 | Data Science & ML

Introduction to JupyterLab

How Does JupyterLab Work?

JupyterLab runs as a web application in your browser similar to Jupyter notebook, connecting to a server that executes your code locally or remotely. It supports multiple programming languages and allows seamless integration of code, text, and rich media.

JupyterLab vs. Jupyter Notebook: Understanding the differences

By understanding the distinctions between these two environments, making an informed decision on which tool best suits your needs and preferences will be seamless. Here are some key difference between Jupyterhub and Jupyter notebook;

User Interface: JupyterLab offers a more advanced and modern user interface than Jupyter Notebook. It has a modular structure, allowing users to open several notebooks or files (e.g. HTML, Text, Markdown, python, SQL, R) as tabs in the same window.
File Management: JupyterLab offers an improved file management system with a built-in file browser, making it easier to navigate and manage your files and folders within the application. Jupyter Notebook, on the other hand, relies on the operating system’s file explorer for file management.
Terminal Access: JupyterLab integrates a command-line terminal, enabling users to execute shell commands directly within the application. This can be particularly useful for tasks like package installations, managing virtual environments, or running command-line tools. While on the other hand Jupyter notebook require you to run a command on your system terminal.
Markdown Preview: JupyterLab offers a live preview of Markdown cells, enabling users to view their formatted text side-by-side with the raw Markdown code. This feature helps ensure that the final output appears as intended, without the need to repeatedly execute cells to check formatting. Jupyter Notebook lacks a live Markdown preview, necessitating the execution of cells to visualize the formatted text.

Now, that we understand the key difference between Jupyterlab and Jupyter notebook, let’s move into installing and setting up Jupyterlab on your local environment.

Installing and setting up JupyterLab

To install JupyterLab, you can use the package manager pip, conda, or anaconda. For pip, run:

$ pip install jupyterlab

For conda, run:

$ conda install -c conda-forge jupyterlab

For Anaconda, install the anaconda application, and launch JupyterLab.

Once installed, start JupyterLab by running:

$ jupyter lab

This will launch JupyterLab in your default web browser.

JupyterLab Keyboard Shortcut

JupyterLab offers many keyboard shortcuts to enhance your productivity. Some common shortcuts include:

Shift+Enter → To run the selected cell
Ctrl+Shift+- → To split the selected cell at the cursor
Press A → To insert a new cell above the selected cell
Press B → To insert a new cell below the selected cell
Double tap D → To delete the selected cell
Press Z → To undo the last cell operation

You can create custom keyboard shortcuts or modify existing ones to match your preferences. To do so, go to the Settings menu and click on Advanced Settings Editor. Then, select Keyboard Shortcuts from the left pane. In the User Preferences tab, add or modify the desired shortcuts following the structure provided in the System Defaults tab. Make sure to save your changes before closing the editor.

Jupyterlab Extension

Extensions are add-ons that enhance JupyterLab’s functionality or appearance. You can browse available extensions using the JupyterLab Extension Manager, which can be enabled in the “Settings” menu under “Enable Extension Manager.” Once enabled, click the puzzle piece icon in the left sidebar to browse and install extensions. You can also use the command line to install extensions with the following syntax:

$ jupyter labextension install <extension-name>

Jupyterlab Advanced Features

Jupyter notebooks are a great way to share your work with others. To share a notebook, you can:

Save the notebook as an HTML file using the “File” menu, then “Export Notebook As” > “Export Notebook to HTML”. This creates a standalone HTML file that can be viewed in any web browser.
Share the notebook file (.ipynb) directly with your collaborators, allowing them to open and interact with it in their own JupyterLab environment.

2. Git integration: Tracking changes and collaborating with others

JupyterLab offers Git integration, which allows you to track changes, collaborate with others, and manage your projects efficiently. To use Git in JupyterLab, you’ll need to:

Install the Git extension for JupyterLab using the Extension Manager or by running the following command in your terminal:

$ jupyter labextension install @jupyterlab/git

Configure Git in your JupyterLab environment by setting your username and email:

$ git config --global user.name "Your Name"
$ git config --global user.email "you@example.com"

Use the Git interface in JupyterLab to clone repositories, stage changes, commit, and push or pull updates.

3. Real-time collaboration: Working together on JupyterLab

Real-time collaboration is possible in JupyterLab with the help of JupyterLab’s collaboration features. By using JupyterHub and the JupyterLab Real Time Collaboration (RTC) extension, you can work together with others on the same notebook simultaneously.

To set up real-time collaboration, follow these steps:

Install and configure JupyterHub for your team or organization.
Install the JupyterLab RTC extension using the Extension Manager or by running the following command in your terminal:

$ jupyter labextension install @jupyterlab/rtc

Share the JupyterHub link with your collaborators, allowing them to access the shared workspace and work together in real-time on notebooks and other files.

4. Connecting to remote kernels: Leveraging powerful computing resources

JupyterLab allows you to connect to remote kernels, enabling you to run your code on powerful computing resources, such as high-performance computing clusters or cloud-based services. To connect to a remote kernel:

Install and configure the necessary remote kernel software, such as Jupyter Enterprise Gateway or Jupyter Kernel Gateway, on the remote machine.
Set up the appropriate connection settings and authentication credentials in your local JupyterLab environment.
Select the remote kernel when creating a new notebook or change the kernel of an existing notebook through the Kernel menu.

5. JupyterLab magic commands: Streamlining your workflow

Magic commands are special commands in JupyterLab that simplify common tasks and enhance your workflow. They are prefixed with a percentage sign (%) for line magics or two percentage signs (%%) for cell magics. Some useful magic commands include:

%run: Runs a Python script as a program, with command-line arguments passed as arguments.
%load: Loads the contents of a file into a code cell.
%time: Measures the execution time of a single statement.
%timeit: Measures the average execution time of a statement over multiple runs.
%who: Lists all variables in the namespace.

6. Debugging in JupyterLab

JupyterLab features a built-in debugger that helps you identify and fix issues in your code. To use the debugger:

Install the required dependencies:

$ pip install xeus-python

Start JupyterLab with the Xeus-Python kernel:

$ jupyter lab --kernel=python3-xeus

Open a notebook using the Xeus-Python kernel, which can be selected in the kernel selection menu.
Enable the debugger by clicking the bug icon in the toolbar.
Set breakpoints in your code by clicking next to the line numbers in the code cells.
Run the notebook and use the debugger interface to step through the code, inspect variables, and interactively debug your code.

Tips and Tricks for JupyterLab Power Users

1. Optimizing your JupyterLab workflow

To make the most of JupyterLab, consider these tips for optimizing your workflow:

Use keyboard shortcuts to speed up common tasks, and customize them to match your preferences.
Utilize the split view feature to work on multiple notebooks or files side-by-side.
Take advantage of magic commands to streamline your coding process.
Regularly save and checkpoint your work to prevent data loss.
Use cell tags to label and organize your notebook content.

2. Troubleshooting common JupyterLab issues

If you encounter issues in JupyterLab, try these troubleshooting steps:

Clear your browser cache and restart the JupyterLab server.
Reset JupyterLab settings to default by deleting the settings folder located in your user directory under ~/.jupyter/lab/user-settings/@jupyterlab/.
Update JupyterLab and its dependencies to the latest version.
Check the JupyterLab logs for error messages or seek help on the JupyterLab GitHub repository and community forums.

JupyterLab is a powerful tool that enhances the data science workflow by providing an interactive, customizable, and feature-rich environment for working with notebooks, code, and data. By leveraging its advanced features, extensions, and collaboration capabilities, you can streamline your work, increase productivity, and create more impactful data-driven insights. As you continue to explore JupyterLab, you’ll discover new ways to optimize your workflow and make the most of this versatile platform for data science and beyond.