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)

Introduction

In the previous chapters, we learned how to create interactive visualizations to represent data in different contexts, such as creating bar plots for stratified data. In this chapter, we will learn how to create interactive visualizations to present data over a period of time. Plotting data against time gives us insights into trends, seasonality, outliers, and important events present in a dataset. Adding a time dimension on a static plot means that one of the axes of the plot will represent time. Adding interactivity on top of that gives us the freedom to explore and analyze the data. In an interactive visualization, we can manipulate the graph according to the user requirements on the fly.

We'll see how to manipulate and plot temporal data in Python. To plot timed data, we will first preprocess the time. Time is composed of units such as seconds, minutes, days, and weeks. So, we first parse the time into the required unit in order to visualize it. Pandas library...