Interactive visualizations are at the core of the exploratory data analysis process, enabling users to directly manipulate and gain insights from data. In this article, we present three different ways in which interactive visualizations can be included in Jupyter Notebooks: 1) matplotlib callbacks; 2) visualization toolkits; and 3) embedding HTML visualizations. We hope that this article will help developers to select the best tools to build their interactive charts in Jupyter Notebooks.
|Original language||English (US)|
|Number of pages||8|
|Journal||Computing in Science and Engineering|
|State||Published - Mar 1 2021|
ASJC Scopus subject areas
- Computer Science(all)