VS Code Open Jupyter Notebook: How-To

Yes, VS Code can open and run Jupyter Notebooks (.ipynb files) natively. You just open the .ipynb file as you would any other file, and VS Code renders it as an interactive notebook where you can run cells, see outputs, and manage kernels. This article explains exactly how to do it, what to install, and how to fix common issues.

What You Need Before Opening a Notebook

VS Code includes built-in Jupyter notebook support, but it needs a few things to actually execute code:

• VS Code itself – any recent version from the official website.
• Python extension – Microsoft’s official Python extension (ms-python.python) is required for Python kernels.
• A Python interpreter – installed on your system, with the ipykernel package. VS Code will prompt you to install it if missing.
• Jupyter extension (optional) – the ms-toolsai.jupyter extension adds extra features like variable explorer and data viewer, but is not strictly required for basic use.

If you already have Jupyter installed (from Anaconda or pip), VS Code can use that same environment. If not, the Python extension will help you create a virtual environment and install ipykernel.

How to Open a Jupyter Notebook in VS Code

There are several ways to open a notebook, all of them straightforward:

1. File Explorer – In VS Code, press Ctrl+O (Windows/Linux) or Cmd+O (Mac), select the .ipynb file, and click Open. The notebook opens in a dedicated editor.
2. Command Palette – Press Ctrl+Shift+P (or Cmd+Shift+P), type “Jupyter: Open Notebook”, and select the file.
3. Drag and drop – Drag the .ipynb file from your OS file manager into the VS Code window.
4. From the integrated terminal – Type code notebook.ipynb in your terminal (if the code command is in PATH).

Once open, you’ll see cells with an “Run” button (play icon) on each. Click it to execute the cell and see output below it. The top-right corner shows the current kernel – click it to switch to a different Python environment.

Choosing the Right Kernel

The kernel is the Python environment that executes your code. If you have multiple Python installations (e.g., Anaconda, system Python, virtual environments), you must select the one that has the packages you need.

• Click the kernel name in the top-right of the notebook editor.
• A dropdown appears with all Python interpreters VS Code can find.
• Select one, and VS Code will check if ipykernel is installed; if not, it offers to install it.

If your desired environment isn’t listed, you can “Enter interpreter path” manually. For Conda environments, VS Code usually detects them automatically if the Python extension is installed.

Creating a New Notebook in VS Code

You can also create a new notebook from scratch:

1. Open the Command Palette (Ctrl+Shift+P / Cmd+Shift+P).
2. Type “Jupyter: Create New Blank Notebook”.
3. Choose a kernel when prompted.
4. Start adding cells using the “+” buttons or the “Add Cell” option in the toolbar.

Alternatively, you can create a new file and save it with the .ipynb extension – VS Code will ask if you want to open it as a notebook.

Common Problems and Fixes

Here are the most frequent issues people hit and how to resolve them:

• “No kernel” or “Kernel not found” – Make sure you have a Python interpreter selected. Open the Command Palette, run “Python: Select Interpreter”, and pick one. If you don’t have Python, install it from python.org and check “Add to PATH” during installation.
• “ModuleNotFoundError: No module named ‘ipykernel’” – VS Code usually prompts to install it. If not, open a terminal in VS Code and run pip install ipykernel (or python -m pip install ipykernel).
• Notebook opens as plain text – This means VS Code didn’t recognize the file as a notebook. Right-click the file in the Explorer, choose “Open With…”, and select “Jupyter Notebook”. If that option isn’t there, ensure the Jupyter extension is installed.
• Kernel crashes on start – Often due to a corrupted environment. Try creating a fresh virtual environment: python -m venv .venv, then select that interpreter and install ipykernel in it.
• Output not showing – Check that the cell is not set to “Markdown” (use “Code” for executable cells). Also, if you’re using a remote environment (SSH, WSL), ensure the kernel is installed on that remote machine.

VS Code vs. Jupyter Notebook in the Browser

Both tools run the same .ipynb files, but they have different strengths:

• VS Code – Better for projects that mix notebooks with regular Python scripts, offers a unified interface, git integration, and a powerful debugger. You can also use Jupyter notebooks alongside other file types in the same window.
• Jupyter Notebook (browser) – Simpler, often pre-installed with Anaconda, and great for quick exploratory work. It also supports extensions like nbextensions that VS Code doesn’t have.
• JupyterLab – The modern browser-based interface, more flexible than classic Notebook but still separate from a full IDE.

If you already use VS Code for coding, it’s convenient to keep notebooks there. If you prefer a minimal, distraction-free environment, the browser version may be better.

Frequently Asked Questions

Do I need to install Jupyter separately?

No. VS Code has built-in Jupyter support. You only need a Python environment with the ipykernel package. The Python extension will help you set that up automatically.

Can I open a .ipynb file without installing the Jupyter extension?

Yes, basic editing and running works with just the Python extension. The Jupyter extension (ms-toolsai.jupyter) adds extra features like the variable explorer and data viewer, but it is not required for core functionality.

How do I convert a notebook to a Python script?

Open the notebook, then use the Command Palette and run “Jupyter: Export to Python Script”. You can also use the jupyter nbconvert --to script command in a terminal.

Why is my notebook running slow?

Slow execution is usually due to the kernel environment, not VS Code. Check if you’re using a remote interpreter (e.g., over SSH) or a heavy environment. Also, large outputs (like big dataframes) can slow down rendering. Try clearing outputs or using the “Restart Kernel” option.

Can I use R or Julia kernels in VS Code?

Yes, VS Code supports any Jupyter kernel. Install the corresponding language extension (e.g., R extension for R, Julia extension for Julia) and then select the kernel from the kernel picker.

Does VS Code support interactive plots in notebooks?

Yes, if you use libraries like matplotlib with the %matplotlib inline magic, plots appear inline. For more advanced interactivity, you can use ipywidgets, which work in VS Code as well.

Final Tips

To get the most out of Jupyter notebooks in VS Code, keep your Python environment clean – use virtual environments per project. Also, learn the keyboard shortcuts: Shift+Enter runs a cell and moves to the next, Ctrl+Enter runs the current cell without moving. If you run into a problem, the VS Code documentation and the Jupyter extension’s issue tracker are good places to search.

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