Interactive Data Visualization with Bokeh
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Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Course Description
Bokeh is a powerful Python package for interactive data visualization, enabling you to go beyond static plots and allow stakeholders to modify your visualizations! In this interactive data visualization with Bokeh course, you’ll work with a range of datasets, including stock prices, basketball player statistics, and Australian real-estate sales data. Through hands-on exercises, you’ll build and customize a range of plots, including scatter, bar, line, and grouped bar plots. You’ll also get to grips with configuration tools to change how viewers interact with your plot, discover Bokeh’s custom themes, learn how to generate subplots, and even how to add widgets to your plots!
What You’ll Learn
Introduction to Bokeh
Learn about the fundamentals of the Bokeh library in this course, which will enable you to level up your Python data visualization skills by building interactive plots. You’ll see how to set up configuration tools, including the HoverTool, providing various opportunities for stakeholders to interact with your plots!
Storytelling with Visualizations
Learn how to use various elements to communicate with stakeholders. You’ll produce grouped bar plots with categorical data, build multiple subplots, add annotations, and modify the text to make your Bokeh visualizations even more striking!
Customizing Visualizations
For this chapter, you’ll learn how to customize axes, create and enhance a legend, modify glyph settings, and apply Bokeh’s custom themes!
Introduction to Widgets
Discover Bokeh’s widgets and how they enable users to modify Python visualizations! You’ll learn about Spinners, which allow viewers to change the size of glyphs. We’ll discuss Sliders, which can be used to change axis ranges. Lastly, we’ll introduce the Select widget, which will enable plot updates based on dropdown options.
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