Interactive Data Visualization with Python: Present your data as an effective and compelling story

Interactive Data Visualization with Python: Present your data as an effective and compelling story
Interactive Data Visualization with Python: Present your data as an effective and compelling story by Abha Belorkar
English | 2019 | ISBN: 1838648350 | 362 Pages | EPUB | 14 MB

Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python
With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.
You’ll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You’ll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you’ll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You’ll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.
By the end of the course, you’ll have a new skill set that’ll make you the go-to person for transforming data visualizations into engaging and interesting stories.
What you will learn

  • Explore and apply different static and interactive data visualization techniques
  • Make effective use of plot types and features from the Matplotlib, Seaborn, Altair, Bokeh, and Plotly libraries
  • Master the art of selecting appropriate plotting parameters and styles to create attractive plots
  • Choose meaningful and informative ways to present your stories through data
  • Customize data visualization for specific scenarios, contexts, and audiences
  • Avoid common errors and slip-ups in visualizing data