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)

Examples of Domains That Use Temporal Data

Easily accessible, yet information-dense, temporal visualizations are the result of accurate interpretations of data. There are different domains that use temporal and time-series data for interactive visualizations:

  • Finance: Examples include the study of a country's GDP growth and the study of the revenue growth of a country. In these cases, we use a time-series dataset.
  • Meteorological: Forecasting the surface temperature change of a geographical region over time, for example, CO2 emissions by countries per year, again uses time-series data.
  • Traffic/mobility: Routing of vehicles/cabs for efficient operations and solving supply and demand problems pertaining to mobility could use time-series traffic data.
  • Medical/healthcare: Some examples include studies of life expectancy over time, patients' temporal reports, and medical history analysis.