Latex In Jupyter Notebook Python


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:

  1. Writing Latex In Jupyter Notebook
  2. Python - Latex In Jupyter Notebook IPython - Stack Overflow
  3. More Images For Latex In Jupyter Notebook Python »
  4. Run Jupyter Notebooks In Your Workspace - Azure Machine ..
  5. Latex In Jupiter Notebook Python Code
  6. Learn How To Write Markdown & LaTeX In The Jupyter Notebook By ..
  • 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

Markdown and LaTeX can change your notebook from plain code to an interesting paper that people would love to read.Notebook Link: Eventually i found this site with instructions on installing some latex enviorments for jupyter notebook but i'm not sure it contains the physics package AND i failed to actually install it. This is the readout of the terminal when typing. Pip3 install jupyterlatexenvs -user-upgrade.

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.

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.

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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

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. 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.

The Jupyter Notebook Interface

When a new Jupyter notebook opens, you will see the Jupyter notebook interface. Across the top of the notebook you see the Jupyter icon and the notebook name. You can click on the notebook name field and change the name of the notebook. Note that the file extension .ipynb is not printed in the file name field, but if you look in the Home tab, you will see that the notebook is saved with the .ipynb extension.

Menus and Buttons

A Jupyter notebook is comprised of a bunch of cells which are arrayed one after another in boxes below the menu items and buttons. There are three main types of cells: code cells, output cells, and markdown cells.

Code Cells

In code cells, you can write Python code, then execute the Python code and see the resulting output. An example of a code cell is shown below.

You can tell you are typing in a code cell because In [ ]: is shown to the left of the cell and the cell-type drop-down menu shows Code.

To run the Python code in a code cell push the [Run] button or type [Shift]+[Enter]. Hitting [Enter] when the cursor is inside a code cell brings the cursor down to a new line.

Output Cells

After a code cell is run, an output cell can be produced below the code cell. The output cell contains the output from the code cell above it. Not all code produces output, so not all code cells produce output cells. The results in output cells can't be edited. If a code cell produces plots, charts or images, these outputs are shown in output cells.

You can clear all the output cells and re-run code cells by selecting [Kernal] --> [Restart Kernal and Clear Output].

Markdown Cells

Markdown cells don't contain Python code. Markdown cells contain text written in Markdown format. Text in markdown cells can be formatted to show bold or italic text. Tables, images, and lists can also be included in markdown cells.

Markdown cells are used for documentation and explaining your code. The text in a markdown cell is not executed. Markdown cells can be formatted with a few special characters.

Markdown cells are run like code cells. The difference is that when markdown cells are run, the text is formatted (when code cells run, code is executed). Markdown cells are run by clicking the [Run] button or by pressing [Shift] + [Enter].

Text in markdown cells can be formatted using markdown syntax. An example of markdown syntax is putting an underscore before and after a word to cause the word to be formatted in italics.


Headings are created in markdown cells using the hash symbol #. One # is the largest heading. Four hashes #### is the smallest heading.

Code Blocks

Code blocks can be inserted in Jupyter notebook markdown cells. For inline code blocks use the ` left quote character, the character to the left of the number [1] and above [Tab] on most keyboards.

This is inline code: ` ` ` Inline code block ` ` ` within a paragraph

For a separated code block use three ` left quote characters on one line, followed by the code block on separate lines. Terminate the separate code block with a line of three ` left quote characters.


Separated code block


The code in markdown cell code blocks do not execute when the markdown cell is run. A code block in a markdown cell is formatted when the markdown cell executes.

Bold and Italics

Bold and italic font is displayed by surrounding text with a double asterisk for **bold** and a single underscore for _italics_

**bold** produces bold


_italics_ produces italics

**_bold and italic_** produces bold and italic


Tables are displayed using the pipe character, which is [Shift] + [] on most keyboards. Columns are separated by pipes and rows are separated by lines. After the header row, a row of pipes and dashes --- are needed to define the table.


header1header 2header 3
col 1col 2col 3
col 1col 2col 3

Bullet Points and Lists

Bullet points are produced using the asterisk character *


  • item 1
  • item 2
  • item 3

Numbered lists are produced using sequential numbers followed by a dot. Indent sub-items with two spaces.


  1. First item
  2. Second item
  3. Third item
  4. sub item
  5. sub item
    1. sub-sub item
    2. sub-sub item

Horizontal Rule

A horizontal rule is specified with three asterisks *** on a single line.

Writing Latex In Jupyter Notebook



Hyperlinks are specified using a set of square brackets [ ] followed by a pair of parenthesis ( ) The text inside the square brackets will be the link, the link address goes in the parenthesis.



Images are embedded in Jupyter Notebook markdown using the exclamation point and square brackets ![ ], followed by the image file path in parenthesis ( ). If the image can not be displayed, the text in square brackets will be shown. The image can be in the same directory as the notebook, or a relative path can be specified. In this case, the image engineering.png is stored in the images directory, which is a subdirectory of the directory the notebook is saved in.

displays the image

LaTeX Math

LaTeX Math equations and symbols are rendered by markdown cells. A more extensive list of LaTeX commands can be found in the appendix.



Because Jupyter notebooks are rendered by web browsers, just about any HTML tag can be included in the markdown portion of a notebook. An example of an HTML tag is the <sup></sup> tags that surround superscript text.



Text can be colored using html <font></font> tags


Red Text

Warning Boxes

bootstrap style warning boxes can be included in Jupyter notebook markdown using <div> tags

Python - Latex In Jupyter Notebook IPython - Stack Overflow


Warning! Python lists start at 0

Creating a new cell

You can create a new cell in a Jupyter Notebook by clicking the [+] button in the upper menu. Clicking the [+] button produces a new code cell below the active cell.

You can also create a new cell using Insert --> Insert Cell Above or Insert Cell Below. You can choose to insert a cell above or below the active cell.

Changing the cell type

The type of cell: code cell or markdown cell, is changed by clicking on a cell and selecting the cell type from the drop-down menu. Typing [Esc] + [m] changes the cell type to a markdown cell. Typing [Esc] + [y] changes the cell type to a code cell.

Saving a Jupyter Notebook

Jupyter notebooks can be saved using the save icon in the upper menu or by pressing [Ctrl] + [s].

Jupyter notebooks can also be saved as a copy, similar to the Save As command common in many programs. To save a copy of a Jupyter notebook use File --> Make a Copy..

Renaming a Jupyter Notebook

Jupyter notebooks are renamed by clicking on the notebook name above the upper menu and typing a new name into the dialog box.

Downloading a Jupyter Notebook

Jupyter notebooks can be downloaded and saved using File --> Download As --> Notebook (.ipynb). Selecting this menu option will download the notebook as a .ipynb file.

Note that when a .ipynb file is viewed in a text editor like notepad, the notebook is unformatted and looks like a confusing jumble of text. The notebook needs to be opened in a Jupyter notebook file browser in order for the code in the notebook to run and the markdown text to render.

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Saving Jupyter Notebooks in Other Formats

Jupyter notebooks can be saved in other formats besides the native .ipynb format. These formats can be accessed using the [File] --> [Download As] menu.

Run Jupyter Notebooks In Your Workspace - Azure Machine ..

The available file download types are:

Latex In Jupiter Notebook Python Code

  • Notebook (.ipynb) - The native jupyter notebook format
  • Python (.py) - The native Python code file type.
  • HTML (.html) - A web page
  • Markdown (.md) - Markdown format
  • reST (.rst) - Restructured text format
  • LaTeX (.tex) - LaTeX Article format
  • PDF via LaTeX (.pdf) - a pdf exported from LaTeX, requires a converter

Learn How To Write Markdown & LaTeX In The Jupyter Notebook By ..

When a notebook is saved as a .py file, all text in markdown cells is converted to comments, and any code cells stay intact as Python code.