As a scientist from data, the Jupyter notebook has become one of the first platforms we are learning because it allows easier manipulation of data compared to the standard ideas of programming. Given its usability, the Jupyter notebook has become a standard tool that every scientist now uses in his daily work.
The jupyter notebook is already useful; However, we can additionally enhance its usefulness, using various extensions. In this article, we will examine seven different Jupyter notebook extensions that will improve your work.
1. Jupyter
. Jupyter Introduction nbextensionsOften referred to as NBextensions, it is not one extension, but a package with a length of over fifty jupyers that we can employ.
Most of the extensions are plain with one improvement in relation to our work, but these extensions still bring additional value that you should employ if you work with a jupyter notebook.
Follow the installation process described in the documentation and you will see a up-to-date card in your Jupyter notebook on the label Nbextensions. Choose this card and you will see a list of extensions as shown below.
There are many recommendations that you can choose, but are not confined to:
- Hinterland: Turn on the code in real time automatic supplementation
- Variable inspector: opens the side panel to display the current variables, which we initiate
- Runtools: Adds a floating toolbar for malleable cells
- Scratchpad: provides floating scratchpad for short-lived notes
- Perform time: show how long the code is made and when
There are many other extensions with nbexttensions that you can try, so check them yourself.
2. Jupyter-resource-USage
One thing that is missing from the Jupyter’s native notebook is the ability to monitor the employ of resources on the platform. Sounds plain, but understanding our memory and available cores is beneficial when working with gigantic data sets or training models.
. Jupyter-resource-service The extension comes to facilitate us as an indicator of controlling the general resources that we employ when working with the Jupyter notebook.
If you followed the installation, the employ of resources is shown similarly to the image below.
The extension should facilitate us in many of our scientific tasks, enabling us to provide available resources in our systems.
3. Widget Jupyter
The jupyter notebook is suitable for visualization, but it was initially designed as a inert tool. The obtained output data is not intended for further interaction.
But Widget jupyter Change the way you employ the Jupyter notebook because it allows you to transform the data that you have in a notebook into interactive visualization. Below we can see an example of Jupyter widgets.
By using Python code, we can generate an interactive visualization that allows users to engage in a more intuitive data exploration process.
4. Jupyter’s motifs
While we are working on the Jupyter notebook, sometimes the way our notebook is not convenient for us or simply is not the right tone for our work.
In this case, we can employ Jupyter motifs Extension to adjust the appearance of our Jupyter notebook. With one plain command of the command line, you can replace the default appearance with something else on the list.
For example, Jupyter’s motives monokai The theme will look like the image below.
If you need to change the theme, this extension is perfect for you.
5. NBConvert
The jupyter notebook can be used as a programming tool for data scientists, enabling them to control cells and provide explanations of Markdown. Although sharing them differs from the format, it is only available from IDE.
WITH NbconvertYou can transform Jupyter’s notebook into various formats, including HTML, Latex, PDF and many others. With one command, you can convert the notebook to another format.
Operate this extension if you want to publish your notebook in a format other than .ipynb file.
6. Voilà
Ordinary Jupyter notebooks are a inert application in which you run the code, not an independent application to run.
Voilà Changes standard Jupyter notebooks into an independent internet application with which you can interact and explore. With a single code line, you can present a notebook as an application similar to the one below.
If you want to present your jupyter notebook as an application, then the extension is perfect for you.
7. Uprising
Speaking of the presentation, there are moments when you want to create your jupyter notebook code in a slide show.
WITH INCREASEYou can transform your notebook into a live presentation that looks like a PowerPoint presentation.
You can see an example of an image -like growth below.
Whenever you need to present a notebook more sequentially, Rise will facilitate you create an interactive presentation.
Application
Jupyter Notebook is a platform used by many data scientists for data analysis and cooperation. This is already a good tool, but we can employ extensions that will facilitate our work. In this article, we examined seven different Jupyter notebook extensions, whom scientists should not miss:
I hope it helped!
Cornellius Yudha Wijaya He is a data assistant and data writer. Working full -time at Allianz Indonesia, he loves to share Python and data tips through social media and media writing. Cornellius writes on various AI topics and machine learning.