Interactive Data Visualization in Jupyter Notebooks
Jorge Piazentin Ono, Juliana Freire, Claudio Silva
Abstract
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.
Topics & Concepts
Computer scienceVisualizationCallbackData visualizationInteractive computingInteractive visualizationData explorationProcess (computing)Exploratory data analysisEmbeddingVisual analyticsHuman–computer interactionData scienceWorld Wide WebProgramming languageData miningArtificial intelligenceData Visualization and AnalyticsData Analysis with RComputational Physics and Python Applications