Create and share docs
Jupyter Notebook is a free, open-source web app that you can use to share and create documents containing live code, equations, visualizations, and texts. This software is maintained and developed by Jupyter. Additionally, this notebook is an offshoot project from the IPython project, which used to include an IPython Notebook component.
Jupyter Notebook supports many programming languages that are core to scientific computing and data analysis—Julia, Python, and R. This web app ships with the IPython kernel, making it possible to write programs in Python, but there are still around 100 other kernels that may work better for you.
Feature-rich software
You will need to install the Jupyter Notebook software to try out Python's interactive environment. This web app has several menus to interact with your notebook—including file, view, kernel, and more. You can also create a text file, a folder, or a terminal in your browser. The terminal is probably the most interesting since it runs your operating system’s command line in a web browser.
This tool has multiple formats to convert or export your notebook when exporting notebooks. You can use its nbconvert tool, which includes HTML, LaTeX, PDF, RevealJS, Markdown, ReStructed Text, and Executable script. Moreover, this software also supports four types of extensions—Kernel, Notebook, IPython kernel, and Notebook server. Notebook extension is a JavaScript module that loads most views in the notebook’s front end.
However, there are a few downsides to using this app. First is the installation and configuration setup process, which might take much time, especially for beginners who need to become more familiar with this software. Second, since this tool only supports Python, it would be great if the developer considers supporting more programming languages related to data science, like R or scala.
A powerful tool
Jupyter Notebook is a powerful tool for creating, sharing, and exploring data science and machine learning projects. It allows you to draft reproducible data analyses and share code and results. With its plethora of features and support for many popular programming languages, you can use this notebook to create custom applications that will help you efficiently process and analyze large datasets.
