If you are a passionate programmer in the Python language in today’s world, you must be highly familiar with Jupyter Notebook.
The first web application for generating and exchanging computational documents is called Jupyter Notebook. It provides a straightforward, efficient, document-focused experience. It is the perfect environment to grow as a developer and deploy your models.
However, many such notebooks lack features nowadays, which can take your coding experience to a new level. Luckily, there are other methods to make this essential tool better, including Jupyter Notebook extensions.
Today, we will discuss 10 Jupyter Notebook Extensions that can really take your overall experience with the platform to new heights and optimize your overall coding experience. So, let’s explore them right away!
Enabling Jupyter Notebook Extensions
Jupyter Notebook extensions are straightforward add-ons that increase the notebook environment’s standard functionality. They are written in JavaScript and can do things like autoformat your code or notify your browser when a cell is finished. As of right now, extensions are limited to Jupyter Notebooks and not Jupyter Lab.
These extensions are add-ons to the Jupyter notebook that are maintained and supplied by the community. These extensions can be used as teaching aids or as tools for modifying and converting notebooks, among other things.
The notebook’s graphical user interface is typically used to add and enable extensions. But not all users on a hub can access the extension through this interface; just the user can. Rather, you can use the command line to make donated extensions available to your users.
You can finish this by using the JupyterHub terminal (or by SSH-ing into your virtual machine and using this terminal).
Here’s how you can enable Jupyter Notebook extensions via pip:
Open the command line and run this:
sudo -E pip install jupyter_contrib_nbextensions
Next, update the Jupyter configuration files with the notebook extension-style files.
sudo -E jupyter contrib nbextension install --sys-prefix
After that, you can utilize the extensions you want to use. To do this, type jupyter nbextension enable and then the path to the main file of the desired extension. For instance, you would input the following to activate Scratchpad:
sudo -E jupyter nbextension enable scratchpad/main --sys-prefix
Once this is finished, the enabled extension ought to show up in the list of extensions:
jupyter nbextension list
10 Best Jupyter Notebook Extensions
Here are 10 Jupyter Notebook Extensions that you should try out to enhance your overall experience with the platform. These extensions are excellent resources for learning, teaching, exploring, and testing out new techniques.
1) autopep8
Autopep8 is a tool that automatically formats Python code to conform to the PEP 8 style guide, which is the standard style guide for Python code. The Autopep8 extension for Jupyter Notebook brings this functionality directly into the notebook environment, allowing you to automatically reformat code cells to comply with PEP 8 standards.
Key Features:
- Automatic Formatting: Automatically formats code cells in the notebook to follow PEP 8 standards.
- Integration: Seamlessly integrates with the Jupyter Notebook interface, adding options to format code with a click.
- Customization: Allows configuration of formatting rules based on personal or project-specific style preferences.
Install Jupyter Contrib Nbextensions:
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
pip install autopep8
Enable the Autopep8 Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Autopep8” and enable it by checking the box.
Usage: Once enabled, the Autopep8 extension adds a button to the toolbar and an option in the context menu of each code cell. Here’s how to use it:
Auto-Format a Code Cell:
- Write or paste Python code into a cell.
- Click the Autopep8 button in the toolbar, or right-click inside the cell and select Autopep8 code.
Example Before and After Autopep8 Formatting:
Before
def foo( a, b= None):
if a+b==1:print(“foo”)
After
def foo(a, b=None):
if a + b == 1:
print(“foo”)
The Autopep8 extension for Jupyter Notebook is a powerful tool for enforcing PEP 8 compliance in your Python code. By automatically reformatting code cells, it ensures a consistent style throughout your notebook, improving readability and collaboration. It’s easy to install and use, with customizable settings to fit different coding preferences. Whether you’re working on a personal project or collaborating with a team, Autopep8 can help keep your code clean and standardized.
2) SpellChecker
The Spellchecker extension for Jupyter Notebook is a tool that helps you identify and correct spelling errors in Markdown cells. This ensures that the documentation and narrative parts of your notebooks are free of typographical errors, enhancing the professionalism and clarity of your notebooks.
Key Features:
- Highlighting Errors: Automatically highlights spelling mistakes in Markdown cells.
- Real-time Checking: Checks spelling as you type.
- Dictionary Support: Uses a built-in dictionary to identify errors and provides suggestions for corrections.
- Configurable: Allows customization of languages and dictionaries.
Install Jupyter Contrib Nbextensions:
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Spellchecker” and enable it by checking the box.
Checking Spelling:
Once the Spellchecker extension is enabled:
- Write or edit text in Markdown cells.
- The Spellchecker will underline any spelling errors it detects in real-time.
Example of Spelling Errors Highlighted:
# Speling Mistake
This sentnce contains a typo.
In the example above, “Speling” and “sentnce” will be underlined in red.
Correcting Errors:
To correct a spelling mistake:
- Right-click on the underlined word.
- A context menu will appear with suggested corrections.
- Click on the correct word to replace the misspelled one.
The Spellchecker extension for Jupyter Notebook is a valuable tool for ensuring the quality of your written content. By automatically highlighting spelling mistakes in Markdown cells and providing correction suggestions, it helps you maintain a high standard of documentation and narrative. This is particularly useful for creating clear, error-free notebooks that are easy to read and professional in appearance.
Using this extension enhances both personal and collaborative projects by improving the clarity and correctness of the documentation, making your notebooks more effective as a tool for communication and knowledge sharing.
3) ExecuteTime
ExecuteTime is a Jupyter Notebook extension that provides timing information for each cell in a notebook. It records and displays the start and end times for cell execution, as well as the total duration, which is useful for monitoring the performance of code and understanding execution flow.
Key Features:
- Timing Information: Displays the start and end times, along with the duration of execution for each code cell.
- Performance Monitoring: Helps in identifying bottlenecks and optimizing code by showing how long each cell takes to run.
- Visual Feedback: Provides a visual representation of execution times directly in the notebook interface.
Install Jupyter Contrib Nbextensions:
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “ExecuteTime” and enable it by checking the box.
Displaying Execution Times:
Once the ExecuteTime extension is enabled:
- Execute any code cell in your notebook.
- The extension will automatically record and display the start time, end time, and total duration of the execution for that cell.
import time
start = time.time()
time.sleep(2)
end = time.time()
print("Execution time:", end - start)After running this cell, you will see something like:
[Started: 10:30:45, Finished: 10:30:47 (2s)]
The ExecuteTime extension will display this timing information automatically below the code cell:
- Started: Indicates the exact time when the cell execution started.
- Finished: Shows the exact time when the cell execution finished.
- (Duration): Displays the total time taken to execute the cell, in seconds or milliseconds.
This information can be particularly useful for:
- Performance Tuning: Identifying slow-running cells and optimizing their code.
- Tracking: Monitoring the execution flow and performance over time.
The ExecuteTime extension is a valuable tool for enhancing Jupyter Notebooks with detailed execution timing information. It helps in performance analysis, debugging, and optimizing code by providing precise start and end times, along with the duration for each cell’s execution.
This extension integrates seamlessly with the notebook interface, offering visual feedback that aids in understanding and improving code efficiency.
By using the ExecuteTime extension, you can gain insights into how long different parts of your code take to run, identify bottlenecks, and make informed decisions about where to focus your optimization efforts. This makes it an essential tool for anyone looking to refine their workflows and improve the performance of their Jupyter Notebooks.
4) Codefolding
The Codefolding extension for Jupyter Notebook adds the ability to collapse and expand sections of code within code cells. This functionality is particularly useful for organizing long notebooks, hiding less relevant code sections, or focusing on specific parts of the code while keeping the notebook clean and readable.
Key Features:
- Collapse/Expand Code Blocks: Allows you to fold (collapse) and unfold (expand) blocks of code.
- Toggle Code Visibility: Temporarily hide complex or lengthy code to focus on high-level logic or narrative.
- Gutter Markers: Adds fold markers (triangles) in the gutter (left margin) of the code cells to control folding.
- Supports Python and Other Languages: Works with Python as well as other programming languages supported in Jupyter Notebooks.
Enable the Codefolding Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Codefolding” and enable it by checking the box.
Folding and Unfolding Code:
Once the Codefolding extension is enabled:
- Markers: Triangular fold markers will appear in the gutter of code cells next to lines that can be folded.
- Click: Click on a marker to collapse the corresponding code block. Click again to expand it.
Example of Folded Code:
Before Folding:
def long_function():
for i in range(10):
print(i)
print(“Done”)
After Folding:
def long_function():…
Example of Code with Multiple Sections:
Before Folding:
# First Section
def foo():
print(“foo”)
# Second Section
def bar():
print(“bar”)
After Folding:
# First Section
def foo():…
# Second Section
def bar():…
The Codefolding extension for Jupyter Notebook is a powerful tool for managing and organizing large notebooks. By allowing you to collapse and expand code sections, it enhances readability, focuses on relevant code, and reduces visual clutter. This extension integrates smoothly into the notebook environment, making it easier to work with extensive or complex codebases.
Using Codefolding effectively can improve your workflow by allowing you to hide details when they are not needed, thus keeping your notebook clean and helping you concentrate on the higher-level structure of your code.
This makes it a valuable extension for both individual work and collaborative projects, where clarity and organization are crucial.
5) Table of Contents (toc2)
The Table of Contents (toc2) extension for Jupyter Notebook automatically generates a table of contents (TOC) for your notebook. It provides a structured overview of the notebook’s content, making it easier to navigate large notebooks, locate specific sections quickly, and improve overall organization and readability.
Key Features:
- Automatic Generation: Dynamically generates a TOC based on the headers (Markdown cells) in your notebook.
- Interactive Navigation: Clickable links in the TOC allow quick navigation to different sections of the notebook.
- Customization: Various options to configure the appearance, position, and behavior of the TOC.
- Section Numbering: Option to automatically number the sections in the TOC.
- Floating TOC: Option to display the TOC in a floating window or docked to the side.
Enable the Table of Contents (toc2) Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Table of Contents (2)” and enable it by checking the box.
Generating the Table of Contents:
Once the toc2 extension is enabled:
- Automatic TOC: The extension will automatically generate a TOC based on the headers in your notebook.
- Position: By default, the TOC appears in a sidebar on the left side of the notebook. You can also configure it to appear as a floating window.
Example of TOC:
For a notebook with the following Markdown headers:
# Introduction
## Overview
## Installation
# Usage
## Basic Examples
## Advanced Features
# Conclusion
The TOC will appear like this:
1. Introduction
1.1 Overview
1.2 Installation
2. Usage
2.1 Basic Examples
2.2 Advanced Features
3. Conclusion
The Table of Contents (toc2) extension is a valuable tool for adding navigational structure to Jupyter Notebooks. By automatically generating a TOC from Markdown headers, it provides an interactive map of your notebook’s content, allowing for quick and efficient navigation.
Whether displayed as a sidebar or a floating window, the TOC enhances the readability and usability of notebooks, making it easier to organize and navigate through complex documents.
This extension is particularly useful for large notebooks, detailed tutorials, or any notebook where clear organization and easy access to different sections are beneficial. By using toc2, you can improve both your personal workflow and the experience of anyone who reads or collaborates on your notebooks.
6) Variable Inspector
The Variable Inspector extension for Jupyter Notebook provides a convenient way to view and manage the variables used in your notebook. It displays information about variables such as their names, types, values, and sizes in a dedicated panel, allowing you to keep track of the state of your notebook environment more easily.
Key Features:
- Variable Overview: Lists all variables in the current notebook session along with their types, sizes, and values.
- Interactive Inspection: Allows you to inspect and interact with variables directly from the panel.
- Filtering and Sorting: Provides options to filter and sort variables by name, type, size, or value.
- Variable Details: Click on a variable to view its detailed representation or summary, including values for complex objects like lists, arrays, and dataframes.
- Memory Management: Helps monitor memory usage by showing the size of variables.
Enable the Variable Inspector Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Variable Inspector” and enable it by checking the box.
Accessing the Variable Inspector:
Once the Variable Inspector extension is enabled:
- A new button labeled “Variables” or an icon (a small table) will appear in the toolbar of the Jupyter Notebook.
Viewing Variables:
- Open Panel: Click on the “Variables” button to open the Variable Inspector panel.
- Variable List: The panel displays a list of variables currently defined in the notebook, along with their types and values.
Example Variable Inspector Panel:
The panel might show variables like:
| Name | Type | Size | Value | |-------|---------|--------|------------------| | x | int | 4 bytes| 42 | | name | str | 5 bytes| 'Alice' | | data | DataFrame | 100 KB | [rows x columns]| | array | ndarray | 40 KB | [1, 2, 3, ..., 10]|
The Variable Inspector extension for Jupyter Notebook is a powerful tool for managing and inspecting variables within your notebook environment. It provides a clear and interactive overview of all the variables in use, including their types, sizes, and values, making it easier to debug, track data, and optimize your notebook.
This extension enhances your workflow by offering real-time updates and detailed views of complex variables, contributing to more efficient debugging and better memory management. Whether you’re working with simple variables or large datasets, the Variable Inspector provides a user-friendly interface to help you understand and manage your notebook’s data effectively.
7. Collapsible Headings
The Collapsible Headings extension for Jupyter Notebook enhances the user experience by allowing you to collapse and expand sections of content based on Markdown headers. This functionality helps organize large notebooks, making it easier to focus on specific sections while keeping the notebook clean and manageable.
Key Features:
- Collapsible Sections: Allows you to collapse and expand sections of the notebook based on Markdown headers (e.g., #, ##, ###).
- Nested Collapsibility: Supports multiple levels of nested collapsible sections, reflecting the hierarchy of headers.
- Visual Indicators: Provides visual indicators (arrows or triangles) to show which sections can be collapsed or expanded.
- State Preservation: Remembers the collapsed/expanded state of sections across notebook sessions.
Enable the Collapsible Headings Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Collapsible Headings” and enable it by checking the box.
Collapsing and Expanding Sections:
Once the Collapsible Headings extension is enabled:
- Markers: Small arrows or triangles appear next to Markdown headers. These serve as indicators that sections can be collapsed or expanded.
- Click: Click on the arrow or triangle next to a header to collapse the section beneath it. Click again to expand it.
Example of Collapsible Headings:
Before Collapsing:
# Introduction
Content under Introduction…
## Overview
Content under Overview…
# Methods
Content under Methods…
After Collapsing:
▼ Introduction
… (Content collapsed)
▶ Methods
… (Content collapsed)
Visual Indicators:
- (Down Arrow): Indicates the section is expanded.
- (Right Arrow): Indicates the section is collapsed.
Managing Nested Sections:
- Nested Collapsibility: You can collapse and expand nested sections independently, providing a flexible way to organize complex documents.
- Hierarchy: The extension respects the hierarchical structure of Markdown headers (#, ##, ###, etc.), allowing for multi-level collapsibility.
Example of Nested Sections:
Before Collapsing:
# Main Section
## Subsection 1
### Detail 1
Content under Detail 1…
### Detail 2
Content under Detail 2…
## Subsection 2
Content under Subsection 2…
▼ Main Section
▼ Subsection 1
▶ Detail 1
▶ Subsection 2
Preserving State:
- Save and Reload: The extension preserves the collapsed/expanded state of sections when you save and reload the notebook, ensuring your organization is maintained.
The Collapsible Headings extension for Jupyter Notebook provides a user-friendly way to manage and organize large notebooks by allowing you to collapse and expand sections based on Markdown headers. This functionality enhances readability, focuses on relevant sections, and reduces visual clutter.
By using collapsible headings, you can create a more structured and navigable notebook, making it easier to handle extensive content or detailed documentation.
The extension integrates seamlessly with the notebook environment, providing visual indicators for collapsible sections and preserving their state across sessions. This makes it a valuable tool for anyone looking to improve the organization and usability of their Jupyter Notebooks.
8) Scratchpad
The Scratchpad extension for Jupyter Notebook provides a convenient, temporary space for quick notes, code snippets, or calculations. It acts like a sticky note or a separate code cell that is always available, allowing you to jot down thoughts, test code snippets, or store temporary data without cluttering your main notebook.
Key Features:
- Temporary Storage: Offers a space to write temporary notes, code, or calculations that won’t clutter your main notebook.
- Floating Window: Displays as a floating window that you can move around or resize within the notebook interface.
- Persistent State: Remembers the content of the scratchpad across sessions, so you can retain your notes and code snippets even after closing and reopening the notebook.
- Code Execution: Allows for the execution of code snippets directly within the scratchpad, making it functional for quick tests or experiments.
Enable the Scratchpad Extension:
- Open the Jupyter Notebook dashboard.
- Go to the “Nbextensions” tab.
- Search for “Scratchpad” and enable it by checking the box.
Accessing the Scratchpad:
Once the Scratchpad extension is enabled:
- Toolbar Button: A new button labeled “Scratchpad” or an icon (a notepad or sticky note) will appear in the toolbar of the Jupyter Notebook.
Writing in the Scratchpad:
- Open Scratchpad: Click on the “Scratchpad” button to open the floating scratchpad window.
- Edit Content: You can type notes, write code snippets, or paste text into the scratchpad.
Example of Scratchpad Window:
Using the Scratchpad:
- Floating Window: The scratchpad appears as a floating window that you can move around the notebook interface or resize as needed.
- Code Execution: You can execute code snippets directly within the scratchpad. This is useful for quick tests or calculations without affecting the main notebook.
Example Usage:
# Temporary calculation
result = 42 * 2
print(result)
You can use the scratchpad to quickly test this code snippet without adding a new cell to the notebook.
Persistent State:
- Save and Reload: The Scratchpad retains its content even after closing and reopening the notebook, allowing you to keep your temporary notes and code snippets across sessions.
The Scratchpad extension for Jupyter Notebook provides a floating, temporary workspace for quick notes, code snippets, and calculations. It offers a non-intrusive way to handle supplementary content without cluttering your main notebook, making it easier to stay organized and focused.
By using the Scratchpad, you can efficiently manage temporary tasks, jot down thoughts, and test code snippets while keeping your main notebook clean and structured.
This extension is particularly useful for jotting down ideas, performing quick tests, or keeping track of temporary information that doesn’t belong in the main document. Its flexibility and ease of use make it a valuable tool for enhancing productivity and workflow in Jupyter Notebooks.
9) Highlight Selected Word
The Highlight Selected Word extension for Jupyter Notebook is a useful tool that enhances the readability and navigation within a notebook by highlighting all instances of a selected word. Here’s how it works and how you can use it:
Key Features:
- Highlighting: When you select a word (double-click or highlight it), all occurrences of that word in the notebook are highlighted. This makes it easy to identify and track the usage of variables, functions, or any specific term throughout your notebook.
- Customizable Colors: The extension allows you to customize the highlight color, making it easier to differentiate between different highlighted words.
- Case Sensitivity: You can toggle case sensitivity for the highlighting, which is useful for distinguishing between terms like variable and Variable.
Open or create a Jupyter Notebook and use the following example code to see the extension in action:
# Example Notebook Cell
def calculate_sum(a, b):
result = a + b
return result
# Another cell using the same variable names
a = 10
b = 20
sum_result = calculate_sum(a, b)
print(“The sum is:”, sum_result)
Highlighting a Word:
- Double-click on the word a in the code. You will see all instances of a highlighted throughout the notebook.
- Similarly, double-click on b, result, or any other word to see all occurrences highlighted.
Customizing the Highlight Color:
- You can customize the highlight color through the Nbextensions configuration menu. This can help differentiate between multiple highlighted words.
Imagine you have the above code in a Jupyter Notebook. When you double-click on the word result, the extension will automatically highlight all instances of result.
By using the Highlight Selected Word extension, you can make your code more readable and maintainable, especially in larger notebooks. You can also easily find where variables are defined, used, or modified. Furthermore you can quickly spot issues by seeing where a particular variable is affecting the code and ensure consistent usage of variable names throughout the notebook.
10) NbTranslate
The Nbtranslate extension for Jupyter Notebook is a powerful tool designed to help users translate the content of their notebooks into different languages. This can be particularly useful for collaborating with international teams, publishing content for a global audience, or learning and teaching in different languages.
Key Features:
- Automatic Translation: The extension leverages translation services to automatically translate the text within your notebook cells.
- Multiple Languages: Supports translation to and from various languages, making it versatile for different needs.
- User Interface: Provides an easy-to-use interface within Jupyter Notebook to select the language and perform translations.
- Customizable: Allows users to customize translation settings and manage translations for specific cells or entire notebooks.
Enable the NbTranslate Extension:
jupyter nbextension enable nbtranslate/main
Translate Content:
- A new toolbar button or menu item will be available for translation.
- Select the text you want to translate or choose to translate the entire notebook.
- Choose the target language from the provided options.
- Execute the translation command, and the selected content will be translated into the chosen language.
Example Usage:
Initial Notebook Content:
# Introduction to Machine Learning
Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed.
Using Nbtranslate:
- Highlight the text “Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to ‘learn’ from data, without being explicitly programmed.”
- Choose the target language (e.g., Spanish).
- The extension translates the text to Spanish.
Translated Content:
# Introducción al Aprendizaje Automático
El aprendizaje automático es un campo de la inteligencia artificial que utiliza técnicas estadísticas para dar a los sistemas informáticos la capacidad de “aprender” de los datos, sin ser programados explícitamente.
The Nbtranslate extension for Jupyter Notebook is a valuable tool for translating notebook content into multiple languages, enhancing accessibility, collaboration, and educational reach. By providing an easy-to-use interface and supporting various languages, it allows users to work and share their notebooks in a globally inclusive manner.
Conclusion
These 10 Jupyter Notebook extensions are highly useful and are designed to enhance your scripting experience across a diverse range of aspects. While these Jupyter Notebook extensions don’t completely solve the problem, they do add a few benefits that will make your work easier.
