5 Best Open Source Multi-Platform Data Science Notebook Software

NOTEBOOKS FOR DATA SCIENCE

A notebook interface is a virtual collaborative environment which contains computer code and rich text elements. Notebook documents are human-readable documents with the analysis description and the results together with the executable documents which can be run to perform data analysis. These documents can be saved as files, checked into revision control just like code, and freely shared. They run on any platform, thanks to their browser-based user interface. In essence, they are a virtual notebook environment used for literate programming.

Notebooks offer a more exploratory method to write code compared with Integrated Development Environments. They provide a handy way to run ad-hoc queries, to perform complex data analysis and data visualizations. Edit, run and re-run snippets of code. Make beautiful data-driven, interactive and collaborative documents. Seeking a solution to a data science problem? Notebooks offer an interactive environment to work and share code with others. Experimentation, exploration and collaboration with notebooks is an effective way to teach computational thinking. Notebooks are also entering the sphere of powering business intelligence dashboards. For a clear and reproducible report, a notebook can be the perfect solution.

The concept of computer notebooks is not new. MATLAB and Mathematica introduced the idea almost 30 years ago. But this type of interactive environment is blossoming for sharing and developing data science. Notebooks have changed how data science teams work enabling them to access scalable computing clusters.

This article selects 5 excellent multi-platform notebooks. They are all released under an open source license, and offer a flexible coding and prototyping environment. Our strongest recommendation is awarded to Jupyter Notebook.

Click the above links to learn more about these excellent notebooks.

Worthy of a brief mention is nteract, a promising notebook.