Introduction

Jupyter Notebooks have gained immense popularity in the data science and machine learning community due to their interactive and versatile nature. They provide an excellent platform for exploring and analyzing data, developing models, and sharing insights. However, did you know that you can further enhance your productivity and efficiency by leveraging Jupyter Notebook extensions?

In this blog post, we will introduce you to 18 must-have Jupyter Notebook extensions that can significantly boost your productivity. These extensions offer additional functionalities and features, ranging from code formatting and spellchecking to table of contents generation and interactive visualizations. By incorporating these extensions into your Jupyter Notebook workflow, you can streamline your development process, improve code quality, and expedite data analysis.

Whether you are a data scientist, researcher, or developer, these extensions are designed to cater to your specific needs, making your Jupyter Notebook experience even more powerful and enjoyable. Let’s dive in and discover the essential extensions that will empower you to get the most out of Jupyter Notebook and elevate your productivity to new heights.

1. Spellchecker

SPellchecker is one of the most useful extensions to have in an IDE as this extension alerts developers whenever there is any spelling mistake/typos error and the same gets highlighted in Red Colour so that doesn’t get missed. Besides this, the extension offers support for the following languages:

• English (for America, Britain, Australia, and Canada)
• French
• German
• Spanish
• Portuguese

2. Debugger

The purpose of this extension was to offer a quick outcome to maintain the workstream and that’s why Debugger became one the best source of finding and fixing bugs. This extension works with Jupyter Notebook, (for both console and source files) and elevates the capability so that the model can work properly.

The extension can be downloaded from the following syntax:

jupyter labextension install @jupyterlab/debugger

Besides this, it’s more or less like pixie-dust GUI (which was the first debugging tool in Python) and fits well with Jupyter Notebook.

3. Hinterland

This extension is a good choice for people who struggle to write code in Jupyter Notebook due to the lack of an autocompletion feature. By selecting the ‘Hinterland’ box, autocompletion is activated in Jupyter Notebook and you will be able to write code quickly.

4. Tree Filter

This extension allows you to filter the Jupyter Notebook file tree (or dashboard) page by filename.

5. GIT

Jupyter Notebook extensions allow you to extend the functionality of Jupyter Notebook. There are several useful Git extensions available for Jupyter Notebooks that allow you to manage your notebooks under version control using Git. These extensions let you commit, push and pull notebooks directly from within the Jupyter interface. This makes it easy to track changes, revert to previous versions, collaborate with others and maintain a history of your notebook development. Popular Git extensions for Jupyter Notebooks include nbgitpuller, nbstripout and jupyter_nbextensions_configurator. These extensions make using Git with Jupyter Notebooks a seamless experience.

Reference: You can also check our article on Version Control with Jupyter Notebook

6. Plotly

This is one of the most elegant, highly-interactive, open-source libraries which helps developers create unique charts based on stats, geographic, scientific, and 3-D dimension projects in a much simpler way.

This tool enables developers to work and visualize within Jupyter Notebook and is also referred to as Plotly.py in a standard format. With the help of this extension, a developer can work on different desktop editors, including Spyder, PyCharm, etc., and the same file can be exported back into PDF formats.

This can easily be installed from below mentioned syntax:

pip install plotly

Either if you use our VM, so the command for the VM is :

! sudo pip install plotly

7. NBdime

This is one of the most elegant tools that helps in diffing and merging while working on any project. It provides an opportunity to decide intelligently how to render image diffs in a web view and can resolve general conflicts (that includes execution counters.

You can download the nbdime following the below syntax:

pip install nbdime

Besides this, it also offers some major key features that developers need to implement while working with this extension:

• Its auto-conflict function helps in merging notebooks in multiple ways.
• A developer can easily view a notebook by simply using terminal
• It helps in comparing notebooks in terminal-friendly
• This extension uses diff tools for both input and output (only from existing ones)

8. Code Folding

This feature enables developers to perform folding a piece of code along with the main class or function. Developers generally use this feature to cut long lengths for ease and this also includes expand collapse, etc. add-ons. You can easily enable

This can be performed by simply hitting Alt+F (default function) and you can also check the below-mentioned example for the best reference:

class MyClass(object):
 """
Heloo
 """
 def afun(param1):
  """ Hi """
  return param1**2

9. nbTranslate

This extension will come in handy whenever you need to read a notebook in a foreign language. Simply enable the “nbTranslate” inside Nbextensions in your Jupyter notebook, and a wrench icon will be visible on your toolbar.

You can set up the primary and secondary languages by using this new icon. To convert a markdown cell to your native language, select the complete text and click on the translate icon hovering next to that text.

10. Variable Inspector

It helps in collecting defined variables after collecting and previews within a floating window. The variable inspector enables users to show previews of their type and size along with the content. The best part is the columns are sortable and that’s why the floated window can easily be dragged, resized, and collapsed as per requirement.

To install Variable Inspector, enter the following:

pip install lckr-jupyterlab-variableinspector

Besides this, it also holds some major key features that developers need to implement while working with variable inspector:

• A developer can easily inspect the interactivity and inline
• This extension is suitable for Python and Jupyter (currently supports Python and R kernels)
• While working on a limited matrix — this extension can be used to view matrices in Datagrid-pattern.

11. Table of Contents

Select the option “Table of Contents (2)” inside Nbextensions to enable this extension in your Jupyter Notebook.

The following symbol will be seen in the toolbar after refreshing the notebook.

Navigating through your notebook will become tough after writing a lengthy code. The “Table of Contents (2)” or “toc2” extension gathers all the headings available in a Jupyter Notebook and shows them in the sidebar, making browsing easier.

12. Hide input and Hide input all

Hiding individual cell inputs or all inputs can be done by selecting the “Hide input” and “Hide input all” checkboxes, respectively. After refreshing your notebook, the following icons will be seen on the toolbar.

The icon on the left will help you hide the code of all code cells, and the second icon will only help you hide the code of specific cells.

13. Snippets Menu

This extension includes ready-to-use code snippets for several Python libraries like Matplotlib, Numpy, SciPy, etc. that allow adding relevant code snippets with a single click. Using the Snippets Menu, we don’t have to look through the entire documentation to search for the syntax since all the necessary syntax is available in one place with this extension.

14. Markdown Tools

Markdown Tools is a useful Jupyter Notebook extension that adds useful tools for working with Markdown cells. Markdown Tools extension significantly enhances the Markdown editing experience within Jupyter Notebook by providing features like preview, citations, table of contents, spell check, code highlighting, and LaTeX math support. If you work with a lot of Markdown content in your notebooks, this is a highly useful extension to install.

15. Code Prettify

The Code Prettify extension for Jupyter Notebook allows you to automatically beautify and format Python code within code cells based on PEP 8 style guidelines. Some of its main features are:

• It indents code blocks using 4 spaces as recommended by PEP 8.
• It wraps lines of code at 79 characters to adhere to the PEP 8 limit.
• It inserts blank lines between top-level functions and classes as per PEP 8.
• It fixes issues like missing whitespace around operators and commas.
• It can reformat both new and existing code cells when you run the prettify command.
• It provides keyboard shortcuts to prettify the current cell or the entire notebook.

This allows you to write code quickly in your notebook cells without worrying about proper indentation and formatting. You can then run the prettify command to automatically format the code according to PEP 8 standards. This makes the code more readable, consistent, and Pythonic.

16. RISE

RISE is a useful Jupyter Notebook extension that allows you to convert your notebooks into beautiful slides and PDF reports. Some of its main features are:

• Slide mode: It can turn your Jupyter Notebook into a reveal.js slide deck that you can present live.
• PDF export: It can export your notebook as a multi-page PDF report with slides.
• Themes: It comes with multiple themes to customize the look and feel of your slides.
• Slide timing: You can set the duration for each slide so the presentation progresses automatically.
• Fragments: You can reveal parts of a slide incrementally using fragments.
• Plot magnification: It allows you to magnify plots and images within a slide with the click of a button.
• Keyboard shortcuts: It provides keyboard shortcuts for common actions like the next slide, previous slide, etc.
• Speaker notes: You can add speaker notes to each slide for your own reference during the presentation.
• Markdown support: It supports rendering Markdown within slides.

17. Auto-Indent

The Auto-Indent extension for Jupyter Notebook allows you to automatically indent and dedent code cells as you type based on Python conventions. Some of its main features are:

• Automatic indenting: As you type opening brackets like (,[,{ etc, the extension automatically indents the next line by 4 spaces.
• Automatic dedenting: As you type closing brackets like ),] and } , the extension automatically dedents the next line by 4 spaces.
• Works with new and existing cells: The extension works both for new code cells you create as well as existing code cells when you edit them.
• Configurable: You can configure the number of spaces for indentation according to your preferences.
• Compatible with other extensions: It works well with other Jupyter extensions without conflicts.

This extension is useful because it saves you the trouble of manually indenting and dedenting code as you type. The automatic indenting and dedenting makes your code more readable and consistent by adhering to Python conventions.

18. Vim Mode

The Vim Mode extension for Jupyter Notebook allows you to use Vim keybindings within your notebook, providing a more Vim-like editing experience. Some of its main features are:

• Normal mode: You can enter Normal mode in a code cell by pressing Escape, and then use Vim commands like ‘i’ for insert, ‘dd’ for delete line, ‘yw’ for yank, etc.
• Insert mode: You can enter Insert mode by pressing ‘i’ in Normal mode and then typing code as usual.
• Visual mode: You can select texts in Visual mode using Vim commands like ‘v’ or ‘V’, and then perform actions on the selection.
• Text objects: You can use text objects like ‘iw’ for an inner word, ‘ab’ for a bracket, etc to select text ranges.
• Motions: Vim motions like ‘w’ to go to the next word, ‘b’ to the previous word, ‘0’ to the start of the line, etc work as expected.
• Undo/Redo: You can use Vim commands like ‘u’ to undo and ‘Ctrl + r’ to redo.
• Search: You get Vim’s powerful search commands like ‘/’ to search forward and ‘?’ to search backward.
• Compatibility: It works well with most other Jupyter extensions.

Conclusion

In conclusion, the Jupyter Notebook extensions discussed above can significantly boost your productivity and efficiency by:

• Enhancing code quality through linting, formatting, and beautifying.
• Improving code editing experience through features like autocompletion, indentation, Vim mode, etc.
• Streamlining development workflows through debugging, git integration, and variable inspection.
• Facilitating content creation through markdown tools, table of contents generation, and spell-checking.
• Enabling interactive visualizations and presentations through extensions like Plotly, RISE, and others.
• Reducing manual efforts through features like snippet menus, code folding, and more.

Together, these extensions provide a wide array of useful functionalities that cater to the specific needs of data scientists, researchers, and developers. By incorporating the right set of extensions into your Jupyter Notebook workflow, you can save time and effort, improve code quality, expedite data analysis, and make insights more meaningful and impactful. So I would highly recommend evaluating and adopting these Jupyter Notebook extensions to elevate your productivity to the next level.

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