Python Print Latex Jupyter

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  • Nbconvert is a Python library that allows you to convert your Jupyter Notebook into other formats, like HTML, LaTeX and PDF. Nbconvert uses Jinja templates to define how Jupyter Notebooks will be displayed in these formats. You can define custom templates or extend the default Jupyter templates using inheritance.
  • As I wrote earlier, I am using jupyter notebook, so I can use $ operators in markdown cells to create LaTeX formulas. My question is, is it possible to generate output using Python code in a way that it will be understood as LaTeX formula and printed in such a way, that: Thanks for all replies.
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In terms of the code, we first create a variable called dash that contains 40 dashes. The code then looped over each of the 12 items in our data variable. If we are dealing with our first item (i.e., i = 0), we tell Python that we want to print a line of dashes, then print our headers, followed by another line of dashes. In an IPython console session, or a regular Python session, it will use the Unicode pretty printer if the terminal supports Unicode. In a terminal that does not support Unicode, the ASCII pretty printer is used. To explicitly not use L A T E X, pass uselatex=False to initprinting or initsession. Text handling through LaTeX is slower than Matplotlib's very capable mathtext, but is more flexible, since different LaTeX packages (font packages, math packages, etc.) can be used. The results can be striking, especially when you take care to use the same fonts in your figures as in the main document.

This is one of the 100+ free recipes of the IPython Cookbook, Second Edition, by Cyrille Rossant, a guide to numerical computing and data science in the Jupyter Notebook. The ebook and printed book are available for purchase at Packt Publishing.

Text on GitHub with a CC-BY-NC-ND license
Code on GitHub with a MIT license

Go toChapter 1 : A Tour of Interactive Computing with Jupyter and IPython
Get the Jupyter notebook

The Jupyter Notebook is a web-based interactive environment that combines code, rich text, images, videos, animations, mathematical equations, plots, maps, interactive figures and widgets, and graphical user interfaces, into a single document. This tool is an ideal gateway to high-performance numerical computing and data science in Python, R, Julia, or other languages. In this book, we will mostly use the Python language, although there are recipes introducing R and Julia.

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In this recipe, we give an introduction to IPython and the Jupyter Notebook.

Getting ready

This chapter's introduction gives the instructions to install the Anaconda distribution, which comes with Jupyter and almost all Python libraries we will be using in this book.

Once Anaconda is installed, download the code from the book's website and open a terminal in that folder. In the terminal, type jupyter notebook. Your default web browser should open automatically and load the address http://localhost:8888 (a server that runs on your computer). You're ready to get started!

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How to do it...


1. Let's create a new Jupyter notebook using an IPython kernel. We type the following command in a cell, and press Shift + Enter to evaluate it:

A notebook contains a linear succession of cells and output areas. A cell contains Python code, in one or multiple lines. The output of the code is shown in the corresponding output area.

In this book, the prompt >>> means that you need to type everything that starts after it. The >>> characters themselves should not be typed.

2. Now, we do a simple arithmetic operation:

The result of the operation is shown in the output area. More precisely, the output area not only displays text that is printed by any command in the cell, but it also displays a text representation of the last returned object. Here, the last returned object is the result of 2+2, that is, 4.

3. In the next cell, we can recover the value of the last returned object with the _ (underscore) special variable. In practice, it might be more convenient to assign objects to named variables such as in myresult = 2 + 2.

4. IPython not only accepts Python code, but also shell commands. These commands are provided by the operating system. We first type ! in a cell before typing the shell command. Here, assuming a Linux or macOS system, we get the list of all the notebooks in the current directory:

On Windows, one may replaces ls by dir.

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5. IPython comes with a library of magic commands. These commands are convenient shortcuts to common actions. They all start with % (the percent character). We can get the list of all magic commands with %lsmagic:

Cell magics have a %% prefix; they target entire code cells.

6. For example, the %%writefile cell magic lets us create a text file. This magic command accepts a filename as an argument. All the remaining lines in the cell are directly written to this text file. Here, we create a file test.txt and write Hello world! into it:

7. As we can see in the output of %lsmagic, there are many magic commands in IPython. We can find more information about any command by adding ? after it. For example, to get some help about the %run magic command, we type %run?as shown here:

The pager (a text area at the bottom of the screen) opens and shows the help of the %run magic command.

8. We covered the basics of IPython and the Notebook. Let's now turn to the rich display and interactive features of the Notebook. Until now, we have only created code cells (containing code). Jupyter supports other types of cells. In the Notebook toolbar, there is a drop-down menu to select the cell's type. The most common cell type after the code cell is the Markdown cell.

Markdown cells contain rich text formatted with Markdown, a popular plain text- formatting syntax. This format supports normal text, headers, bold, italics, hypertext links, images, mathematical equations in LaTeX (a typesetting system adapted to mathematics), code, HTML elements, and other features, as shown here:

Running a Markdown cell (by pressing Shift + Enter, for example) displays the output, as shown in the bottom panel of the screenshot above.

By combining code cells and Markdown cells, we create a standalone interactive document that combines computations (code), text and graphics.

9. The Jupyter Notebook also comes with a sophisticated display system that lets us insert rich web elements in the Notebook. Here, we show how to add HTML, SVG (Scalable Vector Graphics), and even YouTube videos in a notebook. First, we need to import some classes:

10. We create an HTML table dynamically with Python, and we display it in the (HTML-based) notebook.

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11. Similarly, we create an SVG graphics dynamically:

12. We display a Youtube video by giving its identifier to YoutubeVideo:

There's more...

Notebooks are saved as structured text files (JSON format), which makes them easily shareable. Here are the contents of a simple notebook:

Jupyter comes with a special tool, nbconvert, which converts notebooks to other formats such as HTML and PDF (

Another online tool, nbviewer (, allows us to render a publicly-available notebook directly in the browser.

We will cover many of these possibilities in the subsequent chapters, notably in Chapter 3, Mastering the Notebook.

There are other implementations of Jupyter Notebook frontends that offer different ways of interacting with the same notebook documents. Jupyterlab, an IDE for interactive computing and data science, is the future of the Jupyter Notebook. It is introduced in Chapter 3. nteract is a desktop application that lets the user open a notebook file by double-clicking on it, without using the terminal and using a web browser. Hydrogen is a plugin of the Atom text editor that provides rich interactive capabilities when opening notebook files. Juno is a Jupyter Notebook client for iPad.

Here are a few references about the Notebook:

  • Installing Jupyter, available at
  • Documentation of the Notebook available at
  • Security in Jupyter notebooks, at
  • User-curated gallery of interesting notebooks available at
  • JupyterLab at
  • nteract at
  • Hydrogen at
  • Juno at

See also

  • Getting started with data exploratory analysis in the Jupyter Notebook
  • Introducing Jupyterlab


  • Print the following:


  • Print the following:
    HINT: There are ‘t’ and ‘n’ which you might find useful


  • Some characters in printing statements can be a bit tricky to handle. Think about the following:

“John is a great fan of Shakespeare, so he decided to reference the famous “Be of not the be?” from ‘The Hamlet’ is his latest essay”

This confuses the Python parser because quotation marks are reserved for the beginning and end of a string. So we use backslash e.g. ' to escape certain characters (like we do with n and t). Print the sentence from the example.


  • Printing can be art! Use some characters to show your creativity! Try a triangle for example:

    HINT: What about printing the backslash?


We had to escape the backslash by putting another backslash in front of it. We could achieve the same with raw strings, which, similarly to f-strings, need to begin with r and they will escape the backslash automatically, i.e. the string will be printed raw - exactly as it is written, without any special features like n and t.

If you want more inspiration, google ASCII art.

 __ _--=_,-. /--`' [email protected]@.--< `--'  <___/.   ' / >=_/`< ____ /=   _ /_' ` _/ ___/`___/ //./=/~  // /   :   ._/_,__ _ : __ /  `--  /    _ : /:- `.__' `-____/  -- :    ::`-' _>   // /   __/--/ <  /  / _`-- / /--'      -`------/`--' / ___-'