Originally developed for data science applications written in Python, R, and Julia, Jupyter Notebook is useful in all kinds of ways for all kinds of projects: Data visualizations.

Anaconda was created to make it easy to work with Python and its galaxy of data science tools, and it includes the Jupyter Notebook software as a standard-issue pack-in.

For data scientists, Python integration with Jupyter Notebooks now enables users to create and share documents that contain live code, equations, visualizations and explanatory text.

"A hands-on session and guided tutorial for advanced users to explore how to use the EO4SD CR platform Jupyter Notebook, including how to access EO data, conduct analyses and extract data time series.

TOOLBOX 30 October 2018 Why Jupyter is data scientists’ computational notebook of choice An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.

For instance, PyXLL-Jupyter can integrate data from your Excel workbook into a Jupyter Notebook using a single command. This enables you to analyze massive datasets quickly.

Image: Jupyter Notebook Jupyter is a browser-based, open-source data science notebook tool that supports Python, Julia, and other dynamic programming languages such as R, Scilab and Octane.