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)

Understanding the Relation between Temporal Data and Time-Series Data

Time-series data is a more refined version of temporal data where observations are taken at equally spaced points in time successively. With temporal data, on the other hand, observations are simply attached to time, and the intervals may not be equally spaced.

Time-series data is a subset of temporal data, which means that time-series data is temporal data but temporal data may not be time-series data. For example, the following figure of the Puzhal reservoir in Chennai shows the water level over a period of time, which is not equally spaced out necessarily; therefore, the figure is plotted based on temporal data and not time-series data.

Let's look at what stories each type of data can tell:

  • Puzhal reservoir in Chennai depicts how water levels change over time:

Figure 5.3: June 15, 2018 (L) and April 6, 2019 (R)

This picture is courtesy of https://time.com/5611385/india-chennai-water...