How to Display Contents of Different Types in Jupyter Notebook/Lab?¶

Jupyter notebook and Lab has become a favorite tool for data scientists and developers all over the world to perform data analysis and related tasks. Its user-friendly interface and out-of-box functionalities like supporting shell commands from the notebook make it unique and go to tool in the data science community. The jupyter notebook is based on the IPython kernel running under the hood. IPython kernel is just like a normal python interpreter but with lots of additional functionalities. As Jupyter notebook is used by the majority of data scientists worldwide, it supports displaying contents of different types (markdown, latex, video, audio, HTML, etc.) which prevents users from the hassle of using different tools to see contents of different types. All pieces of analysis are available in one place. This makes reproducible research easy to conduct.

IPython has a module named display which provides us with a list of classes and methods that can be used to display contents of different types in Jupyter notebook and Jupyter Lab. As a part of this tutorial, we'll be covering how we can display contents of different types like audio, video, latex, markdown, HTML, iframe, SVG, pdf, etc.

`IPython.display` Module¶

Below is a list of classes and methods available with the IPython.display module.

Classes¶

The classes displayed below accepts data of a particular type and when executed from the jupyter notebook cell will display the content of that type in a notebook.

Audio
Code
FileLink
FileLinks
HTML
IFrame
Image
SVG
JSON
JavaScript
Markdown
Latex
ScribdDocument
Pretty
Video
YouTubeVideo
VimeoVideo

Methods¶

The display_*() methods can take as input many objects created using classes mentioned above and display all of them one after another. The method as per their name takes objects of one kind as input except for the last display() method which can combine contents of different types and display them.

display_html()
display_jpeg()
display_png()
display_svg()
display_json()
display_javascript()
display_markdown()
display_latex()
display_pretty()
display()

We'll start by importing the display module.

from IPython import display

`Audio`¶

The Audio class let us display audio files in a jupyter notebook. It provides us with a simple player that we can pause/play to listen to the audio. The first argument of the method is data which accepts one of the below inputs and generates an Audio object which when displayed will display a small player that can play audio.

numpy array (1d or 2d) of a waveform
list of floats containing waveform
local audio filename
URL

Below we have given as input URL of an audio file and it'll display an audio object which can play that audio. We have also explained below with examples of how we can play audio from local files. We can also set the autoplay parameter to True and it'll start playing audio immediately. It has another important parameter named rate which specifies sampling rate and should be used if data is provided as a numpy array or list of floats.

When we give an object created by any class as the last line in the notebook cell, it'll display an object of that type.

Please make a note that the majority of classes available from the display module provides boolean parameter named embed which if set to True will put DATA URI of the content into a notebook and next time we won't need to load that content into the notebook from file/URL.

display.Audio(url="https://file-examples-com.github.io/uploads/2017/11/file_example_MP3_700KB.mp3")

display.Audio(filename="sample_audio.mp3")

display.Audio(data="sample_wav.wav")

display.Audio(data="sample_wav.wav", autoplay=True)

`Code`¶

The Code class can be used to display code in syntax-highlighted format. We can provide code information to the class in one of the below-mentioned ways.

String of code
Local filename
URL where the file resides

Below we have explained with simple examples of how we can use this class.

display.Code("profiling_examples/example1_modified.py")

from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)

if __name__ == "__main__":
    main_func()

display.Code(filename = "sample.js")

// program to find the largest among three numbers

// take input from the user
const num1 = 12
const num2 = 10
const num3 = 35
let largest;

// check the condition
if(num1 >= num2 && num1 >= num3) {
    largest = num1;
}
else if (num2 >= num1 && num2 >= num3) {
    largest = num2;
}
else {
    largest = num3;
}

// display the result
element.innerHTML = 'The Largest Number is : ' + largest

`FileLink`¶

The FileLink class lets us create links around files on local. It accepts a file name as input and will create a link around it. We can give prefix and suffix to use around link using result_html_prefix and result_html_suffix commands.

We have explained the usage of the class below with simple examples. This can be useful when we are running a notebook on platforms like kaggle or google collab or any other platform where we don't have access to local disks to download files which might be generated during our analysis like model weights file, plotting files, etc.

display.FileLink(path="profiling_examples/example1_modified.py")

display.FileLink(path="profiling_examples/example1_modified.py", result_html_prefix="Profiling Examples : ")

display.FileLink(path="profiling_examples/example1_modified.py", result_html_prefix="Profiling Examples : ", result_html_suffix=" Sample Profiling File")

`FileLinks`¶

The FileLinks class works exactly like the FileLink class with the only difference that it accepts directory names as input and creates a list of links for all the files present in it.

Below we have explained the usage of the same on a temporary folder named sample_files which we created for this. It also provides a boolean parameter named recursive which is True by default and recurses in all subdirectories to display files in all of them. We can set this parameter to False if we don't want links of subdirectories.

display.FileLinks(path="sample_files")

display.FileLinks(path="sample_files", result_html_prefix="Sample Text Files : ", recursive=False)

display.FileLinks(path="sample_files", result_html_prefix="Sample Files : ", recursive=True)

`HTML`¶

The HTML class let us display HTML in notebook. The class accepts a list of the below-mentioned data types as input to create an HTML object.

String containing HTML code
URL
HTML File on local system

Below we have explained the usage of the class with simple examples. We have also created a sample HTML file from one of our blogs and displayed it as a sample.

sample_html = '<html><body><h3>Hello World</h3><h4>Hello World</h4><h5>Hello World</h5><h6>Hello World</h6></body></html>'

display.HTML(data=sample_html)

display.HTML(url="https://google.com")

display.HTML(filename="basic-principles-of-information-visualization.html")

from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)

if __name__ == "__main__":
    main_func()

from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)

if __name__ == "__main__":
    main_func()

`display_html()`¶

The display_html() method takes as input list of HTML object created using display.HTML class and displays all of them one after another in jupyter notebook. Below we have explained the usage with a simple example where we combine HTML of google URL and local file.

html1 = display.HTML(url="https://google.com")
html2 = display.HTML(filename="basic-principles-of-information-visualization.html")

display.display_html(html1, html2)

`IFrame`¶

The IFrame class lets us display iframes in jupyter notebooks. It lets us also specify the width and height of the iframe. Below we have used an iframe to display local HTML files and IPython docs using URL.

display.IFrame(src="basic-principles-of-information-visualization.html", width=500, height=500)

display.IFrame(src="https://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html", width=500, height=600)

`Image`¶

The Image class let us display image of type jpg/jpeg/png/gif in jupyter notebook. We can give either image information as str/bytes or filename/URL. Below we have explained with a few simple examples of how we can display images of different types loaded from local file and URL.

display.Image("cc2.png", height=150, width=150)

display.Image("https://storage.googleapis.com/coderzcolumn/static/blogs/home/home_main.jpg")

img_data = open("cc2.png", "rb").read()
display.Image(data=bytearray(img_data), height=150, width=150)

`display_jpeg()`¶

The display_jpeg() method takes as input image objects of jpeg/jpg files created using the Image class and display images one after another in a notebook.

img2 = display.Image("https://storage.googleapis.com/coderzcolumn/static/blogs/home/home_main.jpg")

display.display_jpeg(img2)

`display_png()`¶

The display_png() method works like the display_jpeg() method and takes as input a list of image objects containing information about the list of png files.

img1 = display.Image("cc2.png", height=150, width=150)

display.display_png(img1)

`SVG`¶

The SVG class lets us display the SVG images in a jupyter notebook. We can provide the file name of the image on a local system or web URL to display the SVG image.

display.SVG(filename="supervised_workflow.svg")

`display_svg()`¶

The display_svg() image takes as an input list of SVG objects created using the SVG class and displays them one after another.

img = display.SVG("supervised_workflow.svg")

display.display_svg(img)

`JSON`¶

The JSON class displays contents of json as a directory-like structure in jupyter notebook which we can explore by expanding/collapsing node of structure. We need to give json dictionary as input to the method and it'll display contents in a tree-like interactive structure. The class can load JSON from the local files as well as URLs on the web.

Please make a note that this functionality only works with Jupyter lab. It does not work in a jupyter notebook at the time of writing this article.

json_data = [{ "Name": "William", "Employee ID" : 1, "Address": "New York"}]

display.JSON(data=json_data)

display.JSON(data=json_data, expanded=True)

`display_json()`¶

The display_json() method takes as input a bunch of json objects created using JSON class and displays all of them one after another.

json1_data = [{ "Name": "William", "Employee ID" : 1, "Address": "New York"}]
json2_data = [{ "Name": "Bill", "Employee ID" : 1, "Address": "New York"}]

json1_obj = display.JSON(json1_data, expanded=True)
json2_obj = display.JSON(json2_data, expanded=True)

display.display_json(json1_obj, json2_obj)

`JavaScript`¶

The Javascript class lets us execute javascript code in jupyter notebook. We can provide the file name or URL of javascript code and it'll execute it. We can access the HTML element of cell output using the element variable in javascript and modify it according to our need to display the output of script execution in the cell output of the notebook. Below we have executed a simple javascript that compares three numbers and prints the largest number as cell output by setting the innerHTML attribute of element.

Please make a note that this functionality only works with Jupyter lab. It does not work in a jupyter notebook at the time of writing this article.

sample.js

// program to find the largest among three numbers

// take input from the user
const num1 = 12
const num2 = 10
const num3 = 35
let largest;

// check the condition
if(num1 >= num2 && num1 >= num3) {
    largest = num1;
}
else if (num2 >= num1 && num2 >= num3) {
    largest = num2;
}
else {
    largest = num3;
}

// display the result
element.innerHTML = '<h1>Hello World</h1>'

display.Javascript(filename="sample.js")

`display_javascript()`¶

The display_javascript() method takes as input list of objects created using Javascript class and executes all of them one after another.

jscript1 = display.Javascript(filename="sample.js")

display.display_javascript(jscript1)

`Markdown`¶

The Markdown class let us display markdown in jupyter notebook. Jupyter notebook itself provides markdown cells already where we can display markdowns but this class can be useful when we are getting markdown data from a different sources in code. Below we have explained with a simple example of how we can use it. The class can also load markdown from a file on local or URL on the web.

If you are interested in learning about markdown then please visit the below link.

Markdown Guide

markdown = '''
# H1 Heading
## H2 Heading

* L1
* L2

**Bold Text**
'''

display.Markdown(markdown)

`display_markdown()`¶

The display_markdown() method accepts a bunch of markdown objects created using the Markdown class and displays all of them one after another.

m1 = '''
# H1 Heading
## H2 Heading
'''
m2 = '''
* L1
* L2
'''
m3 = '''
**Bold Text**
'''
m1_obj = display.Markdown(m1)
m2_obj = display.Markdown(m2)
m3_obj = display.Markdown(m3)

display.display_markdown(m1_obj, m3_obj, m2_obj)

`Latex`¶

The Latex class let us display latex in a jupyter notebook which is generally used to display mathematical formula in a jupyter notebook. The jupyter notebook uses mathjax javascript to display latex in the jupyter notebook. We can provide latex data as string or file name or URL on the web to class. Below we have explained with a simple example of how we can display formula in a jupyter notebook which can be a requirement of many scientific projects.

If you are interested in learning about latex then please visit the below link.

LaTex Guide

idf = '''
$
idf(t)  = {\log_{} \dfrac {n_d} {df(d,t)}} + 1
$
'''

display.Latex(idf)

`display_latex()`¶

The display_latex() takes as an input list of Latex objects and displays latex in all of them one after another.

idf = '''
$
idf(t)  = {\log_{} \dfrac {n_d} {df(d,t)}} + 1
$
'''
tf_idf = '''
$
tf{-}idf(t,d) = tf(t,d) * idf(t)
$
'''

idf_latex = display.Latex(idf)
tf_idf_latex = display.Latex(tf_idf)

display.display_latex(idf_latex, tf_idf_latex)

`Scribd Documents`¶

The ScribdDocument class lets us display Scribd pdf files in a jupyter notebook. We need to provide the unique id of the book on Scribd and it'll display a document in a notebook which we can then read. We can also specify the height and width of the frame which will display the book. It even let us specify the starting page number using the start_page parameter in order to start reading from that page.

Below we have shown the notebook available from the below URL.

https://www.scribd.com/doc/201731015/SEO-Footprints

display.ScribdDocument("201731015", width=400, height=300)

display.ScribdDocument("201731015", width=550, height=700, start_page=5, view_mode="book")

`Pretty Print`¶

The Pretty class lets us display contents of file/URL or string in pretty format.

display.Pretty("profiling_examples/example1_modified.py")

from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)

if __name__ == "__main__":
    main_func()

`display_pretty()`¶

The display_pretty() method accepts a list of Pretty objects and displays all one after another.

pretty1 = '''
from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)
    
if __name__ == "__main__":
    main_func()
'''

pretty1_obj = display.Pretty(pretty1)

display.display_pretty(pretty1_obj)

from memory_profiler import profile

@profile(precision=4)
def main_func():
    import random
    arr1 = [random.randint(1,10) for i in range(100000)]
    arr2 = [random.randint(1,10) for i in range(100000)]
    arr3 = [arr1[i]+arr2[i] for i in range(100000)]
    tot = sum(arr3)
    print(tot)

if __name__ == "__main__":
    main_func()

`Video`¶

The Video class let us display and play video in a jupyter notebook. We can provide either file on local or a URL on the web from where to display the video. We can also provide video data as str or bytes and it'll create a video out of it and display it in a notebook. We have below explained how we can play video from URL and local file.

display.Video("https://file-examples-com.github.io/uploads/2017/04/file_example_MP4_480_1_5MG.mp4")

display.Video("file_example_MP4_480_1_5MG.mp4")

`YouTube Video`¶

The YouTubeVideo class let us display and play youtube videos in a jupyter notebook. We need to provide an ID of the video and it'll embed the video in the jupyter notebook. We can then watch it in the notebook. We can also specify the starting position of the video and it'll play from that second.

display.YouTubeVideo(id="OfIQW6s1-ew", width=400, height=300)

display.YouTubeVideo(id="OfIQW6s1-ew", width=400, height=300, start=15)

`Vimeo Video`¶

The VimeoVideo class let us watch Vimeo videos in a jupyter notebook. We need to provide an ID of the video in order to view it.

display.VimeoVideo(id=253989945, width=400, height=300,)

`display()`¶

The display() method can take as input objects created using any of the classes mentioned above and will display all of them one after another. This method can be used to combine the contents of different types to display all of them together in a notebook.

Below we have explained with a simple example of how we can combine markdown, latex, code, and video objects in order to display all of them together in one cell output.

markdown = '''
# H1 Heading
## H2 Heading

* L1
* L2

**Bold Text**
'''

idf = '''
$
idf(t)  = {\log_{} \dfrac {n_d} {df(d,t)}} + 1
$
'''

idf_obj = display.Latex(idf)

markdown_obj = display.Markdown(markdown)

code_obj = display.Code("profiling_examples/example1_modified.py")

video_obj = display.YouTubeVideo(id="OfIQW6s1-ew", width=400, height=300)

display.display(markdown_obj, idf_obj, code_obj, video_obj)

This ends our small tutorial explaining how we can use the IPython.display module to display contents of different types in jupyter notebook. Please feel free to let us know your views in the comments section.

How to Display Contents of Different Types in Jupyter Notebook/Lab?¶

IPython.display Module¶

Classes¶

Methods¶

Audio¶

Code¶

FileLink¶

FileLinks¶

HTML¶

Hello World

Hello World

Hello World

Hello World

Basic Principles of Information Visualization¶

Data Visualization¶

display_html()¶

IFrame¶

Image¶

display_jpeg()¶

display_png()¶

SVG¶

display_svg()¶

JSON¶

display_json()¶

JavaScript¶

display_javascript()¶

Markdown¶

H1 Heading¶

H2 Heading¶

display_markdown()¶

H1 Heading¶

H2 Heading¶

Latex¶

display_latex()¶

Scribd Documents¶

Pretty Print¶

display_pretty()¶

Video¶

YouTube Video¶

Vimeo Video¶

display()¶

References¶

Sunny Solanki

Other Python Tutorials

Other Categories

Newsletter Subscription

`IPython.display` Module¶

`Audio`¶

`Code`¶

`FileLink`¶

`FileLinks`¶

`HTML`¶

`display_html()`¶

`IFrame`¶

`Image`¶

`display_jpeg()`¶

`display_png()`¶

`SVG`¶

`display_svg()`¶

`JSON`¶

`display_json()`¶

`JavaScript`¶

`display_javascript()`¶

`Markdown`¶

`display_markdown()`¶

`Latex`¶

`display_latex()`¶

`Scribd Documents`¶

`Pretty Print`¶

`display_pretty()`¶

`Video`¶

`YouTube Video`¶

`Vimeo Video`¶

`display()`¶