Using Jupyter Notebooks in VS Code

Jupyter Notebooks have become an essential tool for data scientists, analysts, and anyone involved in exploratory data analysis or machine learning. While there’s a standalone web-based application for running Jupyter Notebooks, integrating this functionality directly into Visual Studio Code (VS Code) offers many additional benefits, such as an improved code editor, integration with other languages, and a unified development environment. In this blog post, I am excited to share my journey of setting up Jupyter Notebooks in VS Code.

Step 1: Install the Required Extensions

First, you need to install two key extensions for VS Code:

1. Python Extension: This is essential for Python development in VS Code. It provides features such as IntelliSense, linting, debugging, and more.

   • Install Python Extension

2. Jupyter Extension: This extension enables you to run Jupyter Notebooks inside VS Code. It allows you to create, view, and manage notebook files, and execute code cells just like in a standalone Jupyter environment.

   • Install Jupyter Extension

Step 2: Install and Set Up UV

UV is a modern, fast Python package and project manager that simplifies dependency management and Python version handling. You can install UV using Homebrew:

brew install uv

Once UV is installed, navigate to your project directory and initialize a new UV project:

uv init

This command creates a pyproject.toml file that will manage your project’s dependencies and configuration. UV automatically handles virtual environment creation and management behind the scenes, so you don’t need to manually create or activate environments.

Optional: Set a Specific Python Version

If you want to use a specific Python version for your notebooks, create a .python-version file:

echo "3.13" > .python-version

UV will automatically download and use this Python version when you run your project. You can change this to any version you need (e.g., 3.11, 3.12, etc.).

Step 3: Install Jupyter and Dependencies

Jupyter relies on kernels to execute the code contained within a notebook. If you try to run the code in Jupyter notebook without it, you might see an error like below.

With UV initialized, you can now add Jupyter and its dependencies to your project:

uv add jupyter ipykernel notebook

This command updates your pyproject.toml file with the required dependencies and installs them automatically. You can also add other packages you might need for data analysis:

uv add matplotlib pandas numpy

After adding dependencies, sync your environment:

uv sync

Selecting the Kernel in VS Code

Once the dependencies are installed, you can select the kernel in your Jupyter Notebook by clicking on the kernel picker in the top right corner of the notebook interface. VS Code should automatically detect the UV-managed Python environment.

Tip: If you don’t see your UV environment in the kernel picker, try reloading VS Code or clicking the refresh button in the kernel selector.

Step 4: Running Jupyter Notebooks

Now that you’ve installed the necessary extensions and tools, you are ready to start working with Jupyter Notebooks in VS Code.

Working with Notebooks Directly in VS Code

The beauty of the Jupyter extension for VS Code is that you don’t need to launch a separate Jupyter server or web interface. Everything runs natively inside VS Code!

• Open or Create a Notebook: You can create a new Jupyter Notebook from the Command Palette (Ctrl+Shift+P or Cmd+Shift+P on macOS) by typing ‘Jupyter: Create New Blank Notebook’.
• Load Existing Notebooks: Simply drag and drop your existing .ipynb files into the VS Code editor or open them using the File Explorer.
• Run Cells: Once your notebook is open, execute the code within by navigating to a cell and clicking the ‘Run’ button or using the shortcut (Shift+Enter).
• Interactive Widgets and Outputs: The Jupyter extension supports interactive widgets and rich outputs, providing a dynamic and engaging environment for data exploration.

Working with Different Python Versions

One of UV’s powerful features is easy Python version management. If you have a project that requires a different Python version, simply update your .python-version file:

# Switch to Python 3.11
echo "3.11" > .python-version
uv sync

# Or switch to Python 3.12
echo "3.12" > .python-version
uv sync

After changing the Python version, reload VS Code and select the new kernel in your notebook. UV will automatically download the specified Python version if it’s not already available.

Optional: Launching Jupyter Web Interface

While VS Code’s native Jupyter support is recommended for most workflows, you can still launch the traditional Jupyter web interface if needed:

uv run jupyter notebook
# or
uv run jupyter lab

Conclusion

Integrating Jupyter Notebooks with VS Code revolutionizes the way you can manage and interact with your data science projects. With UV’s modern project management capabilities, you get automatic Python version handling, seamless dependency management, and a streamlined workflow. Combined with VS Code’s professional-grade editor and the rich features of Jupyter, you can enhance productivity, streamline workflows, and consolidate various tasks within a single application.

Quick Reference

Here’s a summary of the key commands:

# Initial setup
brew install uv
uv init
echo "3.13" > .python-version

# Add dependencies
uv add jupyter ipykernel notebook
uv add matplotlib pandas numpy
uv sync

# Change Python version
echo "3.11" > .python-version
uv sync

# Optional: Launch web interface
uv run jupyter notebook
uv run jupyter lab

Happy coding!