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)

Types of Temporal Data

Temporal data can contain information about the following:

  • Events: An event is a change in the state of an object at a given time. Event = Time + Object State. Examples of events are posting a tweet, sending an email, or sending a message.

    Temporal information in tweets helps us understand trending topics, get the latest news updates, and analyze the sentiment of topics over time.

  • Measurements: Measurements records values across time. Measurement = Time + Measures. Examples of measurements are sensor data, revenue, and stock values.

    Temporal measurement information is the key feature of time-series forecasting. Also, it helps us find patterns and anomalies in a dataset with sensor data.

Another view of time can be based on how it progresses:

  • Sequential: We consider time as continuous linear values here. An example of this type is a Unix timestamp.
  • Cyclical: Time can be viewed as a recurrent event, where it is understood as fixed...