Within the Python Interactive window, it's possible to view, inspect, and filter the variables within your current Jupyter session. Note: The Python Interactive window supports rendering plots created with matplotlib and Altair. Within the Python Interactive window, double-click any plot to open it in the viewer, or select the expand button on the upper left corner of the plot. You can also export plots to PDF, SVG, and PNG formats. In the viewer you can pan, zoom, and navigate plots in the current session. The Plot Viewer gives you the ability to work more deeply with your plots.
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You can be just as productive typing in the Python Interactive window as you are in the code editor.
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The Python Interactive window has full IntelliSense – code completions, member lists, quick info for methods, and parameter hints. To use the window with a file, use the Jupyter: Run Current File in 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 as a console, open it with the Jupyter: Create Interactive Window command from the Command Palette. The Python Interactive window, mentioned in the previous section, can be used as a standalone console with arbitrary code (with or without code cells). The following table lists additional commands and keyboard shortcuts supported when working with code cells. Additional commands and keyboard shortcuts The debugger stops execution at breakpoints and allows you to step through code one line at a time and inspect variables (see Debugging for details). Then you can use Debug Cell to start a debugging session for that code cell. You can also click in the margin to the left of line numbers to set breakpoints. 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. After using this command, the Python extension automatically moves the cursor to the next cell. You can also run code cells using ( Ctrl+Enter) or the Python: Run Selection/Line in Python Terminal command ( Shift+Enter). Selecting a command starts Jupyter (if necessary, which might take a minute), then runs the appropriate cell(s) in the Python Interactive window: You would use Run Above, for example, to initialize the state of the runtime environment before running that specific cell. Run Above applies to all the code cells up to, but not including, the cell with the adornment. Run Below, which appears on the first cell, runs all the code in the file. Run Cell applies to only the one code cell. If you want to step into non-user code, you need to uncheck Data Science: Debug Just My Code in the Python extension settings ( ⌘, (Windows, Linux Ctrl+,)). Note: By default, Debug Cell just steps into user code. The first cell also includes Run Below and all subsequent cells include Run Above: When the Python extension detects a code cell, it adds Run Cell and Debug Cell CodeLens adornments. Note: Make sure to save the code shown above in a file with a. You define Jupyter-like code cells within Python code using a # %% comment: # %% msg = "Hello World" print (msg) # %% msg = "Hello again" print (msg) 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, and export Python files as Jupyter notebooks. To select an environment, use the Python: Select Interpreter command from the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P)). 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. View, inspect, and filter variables using the Variables Explorer and Data Viewer.Run code in the Python Interactive Window.This topic covers the support offered through Python code files and demonstrates how to: Visual Studio Code supports working with Jupyter Notebooks natively, as well as through Python code files. Jupyter (formerly IPython Notebook) is an open-source project that lets you easily combine Markdown text and executable Python source code on one canvas called a notebook. Configure IntelliSense for cross-compiling.
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