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You can change the cell type of any cell in Jupyter Notebook using the Toolbar. The default cell type is Code. To use the Keyboard Shortcuts, hit the esc key. After that, you can change a cell to Markdown by hitting the m key, or you can change a cell to Code by hitting the y key. A markdown cell can display header text of 6 sizes, similar to HTML headers. Start the text in markdown cell by # symbol. Use as many # symbols corresponding to level of header you want. It means single # will render biggest header line, and six # symbols renders header of smallest font size. The rendering will take place when you run the cell either from cell menu or run button of toolbar. Following screenshot sho. Markdown is a way to format text. By default, Jupyter notebooks cells are formatted to display code, so without markdown, there is no easy way to include explanatory text in your notebooks. Of course, you can always include code comments, but those cannot be formatted. And what about links or images?

It is possible to store Jupyter notebooks in plain Markdown. This allows youto define a notebook structure entirely using MyST Markdown. For more informationabout MyST Markdown, see MyST Markdown overview.

Notebooks with Markdown can be read in, executed, and cached by Jupyter Book (see Execute and cache your pages for information on how to cache pages).This allows you to store all of your notebook content in a text format that is much nicer for version control software, while still having all the functionality of a Jupyter notebook.

Note

MyST notebooks uses [MyST-NB to convert between ipynb and text files][myst-nb:index].See its documentation for more information.

To see an example of a MyST notebook, you can look atmany of the pages of this documentation.For example, see ./interactive/hiding.md and ./content/layout.md.

Create a MyST notebook with Jupytext¶

The easiest way to create a MyST notebook is to use Jupytext, a toolthat allows for two-way conversion between .ipynb and a variety of text files.

You can convert an .ipynb file to a MyST notebook with the following command:

A resulting mynotebook.md file will be created.This can then be used as a page in your book.

Important

For full compatibility with myst-parser, it is necessary to use jupytext>=1.6.0.

Jupytext can also automatically synchronize an .ipynb file with your Markdown.To do so, use a Jupyter interface such as Jupyter Lab or the classic notebook interfaceand follow the Jupytext instructions for paired notebooks.

Convert a Markdown file into Jupytext MyST Markdown¶

Jupyter Book has a small CLI to provide common functionality for manipulating andcreating MyST Markdown files that synchronize with Jupytext. To add Jupytext syntaxto a Markdown file (that will tell Jupytext it is a MyST Markdown file), run thefollowing command:

If you do not specify --kernel, then the default kernel will be used if there isonly one available. If there are multiple kernels available, you must specify onemanually.

Structure of MyST notebooks¶

Let’s take a look at the structure that Jupytext creates, which you may also useto create a MyST notebook from scratch. First, let’s take a look at a simple MyST notebook:

There are three main sections to notice:

Frontmatter YAML¶

MyST notebooks need special frontmatter YAML to tell Jupytext that theycan be converted to .ipynb files. The frontmatter YAML block

tells Jupytext that the file is in myst format, and that its code shouldbe run with a Python 3 kernel.

Code cells¶

Code blocks in MyST notebooks are defined with the following MyST directive:

You can optionally add extra metadata to the code cell, which will be convertedinto cell metadata in the .ipynb file. For example, you can add tags to your codecell like so:

You may also explicitly pass the kernel name after {code-cell} to make it clear whichkernel you are running. For example:

However, remember that there is only one kernel allowed per page.

Markdown content¶

Everything in-between your code cells is parsed as Markdown content using theMyST Markdown parser. See MyST Markdown overview formore information about MyST Markdown.

To explicitly split up Markdown content into two Markdown cells, use the followingpattern:

You may also attach metadata to the cell by adding a Python dictionary after the +++.For example, to add tags to the second cell above: Air computers srl driver download for windows.

Warning

Please note that cell breaks and metadata specified in MyST files via the +++ syntaxonly propagate to their .ipynb counterpart. When generating the book’s HTML, Markdowncell information is discarded to avoid conflicting hierarchies in the structure of thedocument. In other words, only code cell tags have an effect on the generated HTML.

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. Visual Studio Code supports working with Jupyter Notebooks natively, as well as through Python code files. This topic covers the support offered through Python code files and demonstrates how to:

• Work with Jupyter-like code cells
• Run code in the Python Interactive Window
• View, inspect, and filter variables using the Variable explorer and data viewer
• Connect to a remote Jupyter server
• Debug a Jupyter notebook
• Export a Jupyter 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, and export Python files as Jupyter notebooks.

Jupyter code cells

You define Jupyter-like code cells within Python code using a # %% comment:

Note: Make sure to save the code shown above in a file with a .py extension.

Jupyter Enable Markdown

When the Python extension detects a code cell, it adds Run Cell and Debug Cell CodeLens adornments. The first cell also includes Run Below and all subsequent cells include Run Above:

Note: By default, Debug Cell just steps into user code. 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+,)).

Run Cell applies to only the one code cell. Run Below, which appears on the first cell, runs all the code in the file. Run Above applies to all the code cells up to, but not including, the cell with the adornment. You would use Run Above, for example, to initialize the state of the runtime environment before running that specific cell.

Selecting a command starts Jupyter (if necessary, which might take a minute), then runs the appropriate cell(s) in the Python Interactive window:

You can also run code cells using (Ctrl+Enter) or 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.

You can also click in the margin to the left of line numbers to set breakpoints. Then you can use Debug Cell to start a debugging session for that code cell. 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).

Additional commands and keyboard shortcuts

The following table lists additional commands and keyboard shortcuts supported when working with code cells.

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 Jupyter: Create 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 Jupyter: Run Current File in Python Interactive Window command from the Command Palette.

IntelliSense

The Python Interactive window has full IntelliSense – code completions, member lists, quick info for methods, and parameter hints. You can be just as productive typing in the Python Interactive window as you are in the code editor.

Plot Viewer

The Plot Viewer gives you the ability to work more deeply with your plots. In the viewer you can pan, zoom, and navigate plots in the current session. You can also export plots to PDF, SVG, and PNG formats.

Avid driver download for windows. 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.

Note: The Python Interactive window supports rendering plots created with matplotlib and Altair.

Live Share for Python Interactive

The Python Interactive window also supports Visual Studio Live Share for real-time collaboration. Live Share lets you co-edit and co-debug while sharing audio, servers, terminals, diffs, comments, and more.

This feature requires the Live Share extensions to be installed on both host and guest machines.

Jupyter Python In Markdown Cell

Variable explorer and data viewer

Within the Python Interactive window, it's possible to view, inspect, and filter the variables within your current Jupyter session. By expanding the Variables section after running code and cells, you'll see a list of the current variables, which will automatically update as variables are used in code.

For additional information about your variables, you can also double-click on a row or use the Show variable in data viewer button to see a more detailed view of a variable in the Data Viewer. Once open, you can filter the values by searching over the rows.

Note: Variable explorer is enabled by default, but can be turned off in settings (Python > Data Science: Show Jupyter Variable Explorer).

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:

1. Run the Jupyter: Specify local or remote Jupyter server for connections command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

2. Select how you would like to connect to a Jupyter server.

3. If working remotely, provide the server's URI (hostname) with the authentication token included with a ?token= URL parameter when prompted. (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.) Alternatively, you can specify a username and password after providing the URI.

4. The Python Interactive window indicates where code is run by displaying the URI (which is blurred out in the image below):

Note: For added security, Microsoft recommends configuring your Jupyter server with security precautions such as SSL and token support. This helps ensure that requests sent to the Jupyter server are authenticated and connections to the remoter server are encrypted. For guidance about securing a notebook server, see the Jupyter docs.

Convert Jupyter notebooks to Python code file

When you've activated an environment with Jupyter installed, you can open a Jupyter notebook file (.ipynb) in VS Code and then convert it to Python code. Once you've converted 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.

Jupyter Notebook Markdown Space

When you open a notebook file, Visual Studio Code will open it in the Notebook Editor automatically. Use the convert icon on the toolbar to convert the Notebook (.ipynb) file to a Python file (.py).

Select the convert icon followed by 'Python Script', 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:

Note: The first time you run code cells in a Python 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.

Markdown Cell In Jupyter Notebook

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.

1. In VS Code, activate a Python environment in which Jupyter is installed, as described at the beginning of this article.

2. Import the notebook's .ipynb file 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.)

3. To start the debugger, use one of the following options:

   • For the whole notebook, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and run the Jupyter: Debug Current File in Python Interactive Window command.
   • For an individual cell, use the Debug Cell adornment that appears above the cell. The debugger specifically starts on the code in that cell. By default, Debug Cell just steps into user code. 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+,)).

4. To familiarize yourself with the general debugging features of VS Code, such as inspecting variables, setting breakpoints, and other activities, review VS Code debugging.

5. As you find issues, stop the debugger, correct your code, save the file, and start the debugger again.

6. 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

Jupyter Notebook Markdown Font Size

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 a Python file in VS Code to a Jupyter notebook (with the .ipynb extension).

Code In Markdown Cell Jupyter

• Jupyter: Export Current Python File as Jupyter Notebook: creates a Jupyter notebook from the contents of the current file, using the # %% and # %% [markdown] delimiters to specify their respective cell types.
• Jupyter: 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.
• Jupyter: Export Interactive Window as Jupyter Notebook: creates a Jupyter notebook from the contents of the Python Interactive window.

Markdown Cell Jupyter Bold

After exporting the contents, VS Code displays a prompt through which you can open the notebook in a browser.