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...