Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating Jupyter notebook documents. Markdown is a light weight and popular Markup language which is a writing standard for data scientists and analysts. Work With Python Code and Markdown Cells in Jupyter Notebook. Recall that a Jupyter Notebook file consists of a set of cells that can store text or code. Text Cells: Text cells allow you to write and render Markdown syntax. This is where you can describe and document your workflow.

• Markdown Python Jupiter Key
• Markdown In Python Jupyter Notebook
• Jupyter Markdown Url
• Jupyter Notebook Markdown
• Python Jupyter Notebook Markdown Cheat Sheet

In addition to Jupyter Notebook Markdown,Jupyter Book also supports a special flavour of Markdown called MyST (orMarkedly Structured Text).It was designed to make it easier to create publishable computational documents written with Markdown notation.It is a superset of CommonMark Markdown and draws heavy inspiration from the fantastic RMarkdown language from RStudio.

Whether you write your book’s content in Jupyter notebooks (.ipynb) or in regular Markdown files (.md),you’ll write in the same flavour of MyST Markdown. Jupyter Book will know how to parse both of them.

This page contains a few pieces of information about MyST Markdown and how it relates to Jupyter Book.You can find much more information about this flavour of Markdown atthe Myst Parser documentation.

Want to use RMarkdown directly?

See Custom notebook formats and Jupytext

Directives and roles¶

Roles and directives are two of the most powerful tools in Jupyter Book.They are kind of like functions, but written in a markup language.They both serve a similar purpose, but roles are written in one line whereas directives span many lines.They both accept different kinds of inputs, and what they do with those inputs depends on the specific role or directive being used.

Directives¶

Directives customize the look, feel, and behaviour of your book.They are kind of like functions, and come in a variety of names with different behaviour.This section covers how to structure and use them.

At its simplest, you can use directives in your book like so:

This will only work if a directive with name mydirectivename already exists (which it doesn’t).There are many pre-defined directives associated with Jupyter Book.For example, to insert a note box into your content, you can use the following directive:

This results in:

Note

Here is a note

being inserted in your built book.

For more information on using directives, see the MyST documentation.

More arguments and metadata in directives¶

Many directives allow you to control their behaviour with extra pieces ofinformation. In addition to the directive name and the directive content,directives allow two other configuration points:

directive arguments - a list of words that come just after the {directivename}.

Here’s an example usage of directive arguments:

directive keywords - a collection of flags or key/value pairsthat come just underneath {directivename}.

There are two ways to write directive keywords, either as :key:val pairs, oras key:val pairs enclosed by --- lines. They both work the same way:

Here’s an example of directive keywords using the :key:val syntax:

and here’s an example of directive keywords using the enclosing --- syntax:

Tip

Remember, specifying directive keywords with :key: or --- will make no difference.We recommend using --- if you have many keywords you wish to specify, or if some valueswill span multiple lines. Use the :key:val syntax as a shorthand for just one or twokeywords.

For examples of how this is used, see the sections below.

Roles¶

Roles are very similar to directives, but they are less complex and writtenentirely in one line. You can use a role in your book withthis syntax:

Again, roles will only work if rolename is a valid role name.For example, the doc role can be used to refer to another page in your book.You can refer directly to another page by its relative path.For example, the syntax {doc}`./intro` will result in: Books with Jupyter.

Warning

It is currently a requirement for roles to be on the same line in your source file. It willnot be parsed correctly if it spans more than one line. Progress towards supporting rolesthat span multiple lines can be tracked by this issue

For more information on using roles, see the MyST documentation.

What roles and directives are available?¶

There is currently no single list of roles / directives to use as a reference, but thissection tries to give as much as information as possible. For those who are familiarwith the Sphinx ecosystem, you may use any directive / role that is available in Sphinx.This is because Jupyter Book uses Sphinx to build your book, and MyST Markdown supportsall syntax that Sphinx supports (think of it as a Markdown version of reStructuredText).

Caution

If you search the internet (and the links below) for information about roles and directives,the documentation will generally be written with reStructuredText in mind. MyST Markdownis different from reStructuredText, but all of the functionality should be the same.See the MyST Sphinx parser documentation for more information about the differences between MyST and rST.

For a list of directives that are available to you, there are three places to check:

1. The Sphinx directives pagehas a list of directives that are available by default in Sphinx.

2. The reStructuredText directives pagehas a list of directives in the Python “docutils” module.

3. Carbon copy cloner for mac free. This documentation has several additional directives that are specific to Jupyter Book.

What if it exists in rST but not MyST?

In some unusual cases, MyST may be incompatible with a certain role or directive.In this case, you can use the special eval-rst directive, to directly parse reStructuredText:

which produces

Note Paint for macbook pro free download.

A note written in reStructuredText.

See also

Markdown Python Jupiter Key

Macbook air mac store. The MyST-Parser documentation on how directives parse content, and its use for including rST files into a Markdown file, and using sphinx.ext.autodoc in Markdown files.

Nesting content blocks in Markdown¶

If you’d like to nest content blocks inside one another in Markdown (forexample, to put a {note} inside of a {margin}), you may do so by addingextra backticks (`) to the outer-most block. This works for literalcode blocks as well.

For example, the following syntax:

yields

Thus, if you’d like to nest directives inside one another, you can take the sameapproach. For example, the following syntax:

produces:

Other MyST Markdown syntax¶

In addition to roles and directives, there are numerous other kinds of syntaxthat MyST Markdown supports.MyST supports all syntax of CommonMark Markdown (the kind of Markdown that Jupyter notebooks use), as well as an extended syntax that is used for scientific publishing.

The MyST-Parser is the tool that Jupyter Book uses to allow you to write your book content in MyST.It is also a good source of information about the MyST syntax.Here are some links you can use as a reference:

See also

For information about enabling extended MyST syntax, see MyST syntax extensions.In addition, see other examples of this extended syntax (and how to enable each) throughout this documentation.

What can I create with MyST Markdown?¶

See Special content blocks for an introduction to what you can do with MyST Markdownin Jupyter Book.In addition, the other pages in this site cover many more use-cases for how to use directives with MyST.

Tools for writing MyST Markdown¶

There is some support for MyST Markdown in tools across the community. Here we includea few prominent ones.

Jupyter interfaces¶

While MyST Markdown does not (yet) render in traditional Jupyter interfaces, mostof its syntax should “gracefully degrade”, meaning that you can still work withMyST in Jupyter, and then build your book with Jupyter Book.

Jupytext and text sync¶

For working with Jupyter notebook and Markdown files, we recommend jupytext,an open source tool for two-way conversion between .ipynb and text files.Jupytext supports the MyST Markdown format.

Note

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

See also Convert a Jupytext file into a MyST notebook.

VS Code¶

If editing the Markdown files using VS Code, theVS Code MyST Markdown extensionprovides syntax highlighting and other features.

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 native support available for Jupyter Notebooks and demonstrates how to:

• Create, open, and save Jupyter Notebooks
• Work with Jupyter code cells
• View, inspect, and filter variables using the Variable explorer and Data viewer
• Connect to a remote Jupyter server
• Debug a Jupyter notebook

Setting up your environment

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 open a Jupyter Notebook, connect to a remote Jupyter server for running code cells, and export a Jupyter Notebook as a Python file.

Note: By default, the Visual Studio Code Python extension will open a Jupyter Notebook (.ipynb) in the Notebook Editor. If you want to disable this behavior you can turn it off in settings. (Python > Data Science: Use Notebook Editor).

Create or open a Jupyter Notebook

You can create a Jupyter Notebook by running the Jupyter: Create Blank New Jupyter Notebook command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) or by creating a new .ipynb file in your workspace. When you select the file, the Notebook Editor is launched allowing you to edit and run code cells.

If you have an existing Jupyter Notebook, you can open it in the Notebook Editor by double-clicking on the file and opening with Visual Studio Code, through the Visual Studio Code, or using the Command Palette Jupyter: Open in Notebook Editor command.

Once you have a Notebook created, you can run a code cell using the green run icon above the cell and the output will appear directly below the code cell.

Trusted Notebooks

It's possible for malicious source code to be contained in a Jupyter Notebook. With that in mind, to help protect you, any Notebook that's not created with VS Code on your local machine (or explicitly set to Trusted by you) is considered Not Trusted. When a Notebook is Not Trusted, VS Code will not render Markdown cells or display the output of code cells within the Notebook. Instead, just the source of Markdown and code cells will be shown. The Notebook is essentially in read-only mode, with toolbars disabled and no ability to edit the file, until you set it as Trusted.

Note: Before setting a Notebook as Trusted, it is up to you to verify that the source code and Markdown are safe to run. VS Code does not perform any sanitizing of Markdown, it merely prevents it from being rendered until a Notebook is marked as Trusted to help protect you from malicious code.

When you first open a Notebook that's Not Trusted, the following notification prompt is displayed.

If you select Trust, the Notebook will be trusted going forward. If you opt not to trust the Notebook, then Not Trusted will be displayed in the toolbar and the Notebook will remain in a read-only state as described previously. If you select Trust all notebooks, you will be taken to settings, where you can specify that all Notebooks opened in VS Code be trusted. That means you will no longer be prompted to trust individual notebooks and harmful code could automatically run.

Markdown In Python Jupyter Notebook

You can relaunch the trust notification prompt after reviewing the Notebook by clicking on the Not Trusted status.

Save your Jupyter Notebook

You can save your Jupyter Notebook using the keyboard combo Ctrl+S or through the save icon on the Notebook Editor toolbar.

Note: At present, you must use the methods discussed above to save your Notebook. The File>Save menu does not save your Notebook, just the toolbar icon or keyboard command.

Export your Jupyter Notebook

You can export a Jupyter Notebook as a Python file (.py), a PDF, or an HTML file. To export, just click the convert icon on the main toolbar. You'll then be presented with file options from the Command Palette.

Note: For PDF export, you must have TeX installed. If you don't, you will be notified that you need to install it when you select the PDF option. Also, be aware that if you have SVG-only output in your Notebook, they will not be displayed in the PDF. To have SVG graphics in a PDF, either ensure that your output includes a non-SVG image format or else you can first export to HTML and then save as PDF using your browser.

Work with code cells in the Notebook Editor

The Notebook Editor makes it easy to create, edit, and run code cells within your Jupyter Notebook.

Create a code cell

By default, a blank Notebook will have an empty code cell for you to start with and an existing Notebook will place one at the bottom. Add your code to the empty code cell to get started.

Code cell modes

While working with code cells a cell can be in three states, unselected, command mode, and edit mode. The current state of a cell is indicated by a vertical bar to the left of a code cell. When no bar is visible, the cell is unselected.

An unselected cell isn't editable, but you can hover over it to reveal additional cell specific toolbar options. These additional toolbar options appear directly below and to the left of the cell. You'll also see when hovering over a cell that an empty vertical bar is present to the left.

When a cell is selected, it can be in two different modes. It can be in command mode or in edit mode. When the cell is in command mode, it can be operated on and accept keyboard commands. When the cell is in edit mode, the cell's contents (code or Markdown) can be modified.

When a cell is in command mode, the vertical bar to the left of the cell will be solid to indicate it's selected.

When you're in edit mode, the vertical bar will have diagonal lines.

To move from edit mode to command mode, press the ESC key. To move from command mode to edit mode, press the Enter key. You can also use the mouse to change the mode by clicking the vertical bar to the left of the cell or out of the code/Markdown region in the code cell.

Add additional code cells

Code cells can be added to a Notebook using the main toolbar, a code cell's vertical toolbar, the add code cell icon at the bottom of the Notebook, the add code cell icon at the top of the Notebook (visible with hover), and through keyboard commands.

Using the plus icon in the main toolbar will add a new cell directly below the currently selected cell. Using the add cell icons at the top and bottom of the Jupyter Notebook, will add a code cell at the top and bottom respectively. And using the add icon in the code cell's toolbar, will add a new code cell directly below it.

When a code cell is in command mode, the A key can be used to add a cell above and the B can be used to add a cell below the selected cell.

Select a code cell

The selected code cell can be changed using the mouse, the up/down arrow keys on the keyboard, and the J (down) and K (up) keys. To use the keyboard, the cell must be in command mode.

Run a single code cell

Once your code is added, you can run a cell using the green run arrow and the output will be displayed below the code cell.

You can also use key combos to run a selected code cell.

• Ctrl+Enter runs the currently selected cell
• Shift+Enter runs the currently selected cell and, if a cell is not already present, inserts a new cell immediately below (focus moves to the below cell in command mode)
• Alt+Enter runs the currently selected cell and inserts a new cell immediately below (focus moves to new cell in edit mode).

These keyboard combos can be used in both command and edit modes.

Run multiple code cells

Running multiple code cells can be accomplished in a number of ways. You can use the double arrow in the toolbar of the Notebook Editor to run all cells within the Notebook or the run icons with directional arrows to run all cells above or below the current code cell.

Run code by line

To help diagnose issues with your Notebook code, run-by-line lets you step through the code of a cell in a line-by-line fashion. While stepping through code you can view the state of variables at each step via the variable explorer or hover your mouse over variables to see data tips.

To start a session, just click the run-by-line icon to the right of the run cell icon on the cell's toolbar.

Once in a run-by-line session, you can run the currently highlighted line of code by pressing the icon again. To exit, just click the stop button next to the run-by-line icon in the cell.

Move a code cell

Moving code cells up or down within a Notebook can be accomplished using the vertical arrows beside each code cell. Hover over the code cell and then click the up arrow to move the cell up and the down arrow to move the cell down.

Delete a code cell

Deleting a code cell can be accomplished by hovering over a code cell and using the delete icon in the code cell toolbar or through the keyboard combo dd when the selected code cell is in command mode.

Undo your last change

You can use the z key to undo your previous change, for example, if you've made an accidental edit you can undo it to the previous correct state, or if you've deleted a cell accidentally you can recover it.

Switch between code and Markdown

The Notebook Editor allows you to easily change code cells between Markdown and code. By default a code cell is set for code, but just click the Markdown icon (or the code icon, if Markdown was previously set) in the code cell's toolbar to change it.

Once Markdown is set, you can enter Markdown formatted content to the code cell. Once you select another cell or toggle out of the content selection, the Markdown content is rendered in the Notebook Editor.

You can also use the keyboard to change the cell type. When a cell is selected and in command mode, the M key switches the cell type to Markdown and the Y key switches the cell type to code.

Clear output or restart/interrupt the kernel

If you'd like to clear the code cell output or restart/interrupt the kernel, you can accomplish that using the main Notebook Editor toolbar.

Enable/Disable line numbers

You can enable or disable line numbering within a code cell using the L key.

IntelliSense support in the Jupyter Notebook Editor

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

Variable explorer and data viewer

Within the Python Notebook Editor, it's possible to view, inspect, and filter the variables within your current Jupyter session. By clicking the Variables icon in the top toolbar 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 next to the variable 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).

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.

Within the Notebook Editor window, double-click any plot to open it in the viewer, or select the plot viewer button on the upper left corner of the plot (visible on hover).

Note: There is support for rendering plots created with matplotlib and Altair.

Debug a Jupyter Notebook

If you need additional debug support in order to diagnose an issue in your code cells, you can export it as a Python file. Once exported as a Python file, 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. To debug your Python file:

1. In VS Code, if you haven't already, activate a Python environment in which Jupyter is installed.

2. From your Jupyter Notebook (.ipynb) select the convert button in the main toolbar.

   Once exported, you'll have a .py file with your code that you can use for debugging.

3. After saving the .py file, 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, use the Python Interactive window to export the Python file as a Jupyter Notebook (.ipynb).

Connect to a remote Jupyter server

Jupyter Markdown Url

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.

Jupyter Notebook Markdown

To connect to a remote Jupyter server:

Python Jupyter Notebook Markdown Cheat Sheet

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

2. When prompted to Pick how to connect to Jupyter, select Existing: Specify the URI of an existing server.

3. When prompted to Enter the URI of a Jupyter server, 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.) Alternatively, you can specify a username and password after providing the URI.

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.