Book Image

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar
Book Image

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

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.
Table of Contents (9 chapters)

Getting Started with Interactive Data Visualizations

As we mentioned earlier, the key aspect of interactive data visualizations is its ability to respond and react to human inputs either in the moment or within a very short time span. Thus, human inputs themselves play an important role in interactive data visualizations. In this section, we'll look at some human inputs, how they can be introduced into data visualizations, and the impact that they have on the comprehension of data.

The following are some of the most popular forms of human input and interactive features:

  • Slider: A slider allows the user to see data pertaining to a range of something. As the user changes the position of the slider, the plot changes in real time. This allows the user to see several plots in real time:
Figure 3.10: A slider tool
  • Hover: Hovering a cursor above an element of a plot allows the user to receive more information about the datapoint than can be seen just by observing...