#### 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.
Preface
1. Introduction to Visualization with Python – Basic and Customized Plotting
Free Chapter
2. Static Visualization – Global Patterns and Summary Statistics
3. From Static to Interactive Visualization
4. Interactive Visualization of Data across Strata
5. Interactive Visualization of Data across Time
6. Interactive Visualization of Geographical Data
7. Avoiding Common Pitfalls to Create Interactive Visualizations

# Visualization of Temporal Data

In temporal data visualization, time is the independent variable and the other features that are being visualized are plotted against time. So, the other features are dependent variables. Usually, time is plotted on the x axis, while the dependent variables are plotted on the y axis. We can see a few plots here:

• Line graph:
##### Figure 5.5: Line plot representing temporal data

This line graph shows the percentage change in the population of a country for each year. If multiple lines are plotted on the same graph, then it gives us a comparative study of the features. Lines plots are easy to interpret and also simple to plot.

• Grouped bar chart:
##### Figure 5.6: Grouped bar plot representing temporal data

This grouped bar chart shows the counts of medals (shown on the y axis) received in 2012, 2014, and 2016. Having many lines on the same line graph plot makes visibility and comparability poor...