Working with Jupyter Notebooks in Visual Studio Code
Jupyter (formerly IPython) is an open-source project that lets you easily combine Markdown text and executable Python source code on one canvas called a notebook.
To work with Jupyter notebooks, you must activate an Anaconda environment in VS Code, or another Python environment in which you've installed the Jupyter package. To select an environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).
Once the appropriate environment is activated, you can create and run Jupyter-like code cells, connect to a remote Jupyter server for running code cells, open a Jupyter notebook directly, and export Python files as Jupyter notebooks.
Jupyter code cells
You define Jupyter-like code cells within Python code using a
#%% msg = "Hello World" print(msg)
When the Python extension detects a code cell, it adds a Run Cell or Run All Cells CodeLens above the comment:
Selecting either command starts Jupyter (if necessary, which might take a minute), then runs the cell(s) in the Python interactive window.
You can also run code cells using the Python: Run Selection/Line in Python Terminal command (Shift+Enter). After using this command, the Python extension automatically moves the cursor to the next cell. If you're in the last cell in the file, the extension automatically inserts another
#%% delimiter for a new cell, mimicking the behavior of a Jupyter notebook.
Python interactive window
The Python interactive window, mentioned in the previous section, can be used as a standalone console with arbitrary code (with or without code cells).
To use the window as a console, open it with the Python: Show Python Interactive window command from the Command Palette. You can then type in code, using Enter to go to a new line and Shift+Enter to run the code.
To use the window with a file, use the Run Current File in Python Interactive window command from the Command Palette.
Connect to a remote Jupyter server
You can offload intensive computation in a Jupyter notebook to other computers by connecting to a remote Jupyter server. Once connected, code cells run on the remote server rather than the local computer.
To connect to a remote Jupyter server:
Run the Python: Specify Jupyter server URI command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).
When prompted, provide the server's URI (hostname) with the authentication token included with a
?token=URL parameter. (If you start the server in the VS Code terminal with an authentication token enabled, the URL with the token typically appears in the terminal output from where you can copy it.)
The Python interactive window indicates where code is run by displaying the URI (which is blurred out in the image below):
Open Jupyter notebooks
When you've activated an environment with Jupyter installed, you can import a Jupyter notebook file (
.ipynb) in VS Code as Python code. Once you've imported the file, you can run the code as you would with any other Python file and also use the VS Code debugger. Opening and debugging notebooks in VS Code is a convenient way to find and resolve code bugs, which is difficult to do directly in a Jupyter notebook.
When you open a notebook file, the Python extension prompts you to import the notebook as a Python code file:
Choose Import, wait a few seconds, and then VS Code opens the converted notebook in an untitled file. The notebook's cells are delimited in the Python file with
#%% comments; Markdown cells are converted wholly to comments preceded with
#%% [markdown], and render as HTML in the interactive window alongside code and output such as graphs:
If you open the file without importing, it appears as plain text.
Note: The first time you run code in a notebook file, the Python extension starts a Jupyter server. It may take some time for the server to start up and for the Python Interactive window to appear with the results of the code.
Debug a Jupyter notebook
The Visual Studio Code debugger lets you step through your code, set breakpoints, examine state, and analyze problems. Using the debugger is a helpful way to find and correct issues in notebook code.
In VS Code, activate a Python environment in which Jupyter is installed, as described at the beginning of this article.
Import the notebook's
.ipynbfile into VS Code as described in the previous section. (Download the file first if you're using a cloud-based Jupyter environment such as Azure Notebooks.)
Follow the instructions to configure and run the debugger as described on Tutorial - Configure and run the debugger, using your imported
.ipynbfile, of course, and setting a breakpoint in an appropriate location in your notebook code.
To familiarize yourself with the general debugging features of VS Code, such as inspecting variables, setting breakpoints, and other activities, review VS Code debugging.
As you find issues, stop the debugger, correct your code, save the file, and run the debugger again.
When you're satisfied that all your code is correct. Save the file, then export the notebook as described in the following section. You can then upload the notebook to your normal Jupyter environment.
Export a Jupyter notebook
In addition to opening a Jupyter notebook, you can also use one of the following commands from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) to export content from VS Code to a Jupyter notebook (with the
- Python: Export Current Python File as Jupyter Notebook: creates a Jupyter notebook from the contents of the current file, using the
#%% [markdown]delimiters to specify their respective cell types.
- Python: Export Current Python File and Output as Jupyter Notebook: creates a Jupyter notebook from the contents of the current file and includes output from code cells.
- Python: Export Python Interactive window as Jupyter Notebook: creates a Jupyter notebook from the contents of the Python interactive window.
After exporting the contents, VS Code displays a prompt through which you can open the notebook in a browser.