Interactive data visualization in Jupyter notebooks

Jorge Piazentin Ono, Juliana Freire, Claudio T. Silva

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number9391750
Pages (from-to)99-106
Number of pages8
JournalComputing in Science and Engineering
Volume23
Issue number2
DOIs
StatePublished - Mar 1 2021

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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