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!

Introduction to Bokeh

When to use a scatter plot

Blocks vs. rebounds

Kevin Durant's performance across seasons

Shooting ability by position

Configuration tools

Options

Pan/Drag Tools

Scroll/Pinch Tools

Click/Tap Tools

The best tools for the job

Setting tools

Adding LassoSelectTool

The HoverTool

Adding a HoverTool

Formatting the HoverTool

For this chapter, you’ll learn how to customize axes, create and enhance a legend, modify glyph settings, and apply Bokeh's custom themes!

Adding style

Colors, legend, and theme

Customizing glyphs

Customizing axes

Average building size

Sales over time

Subplots

Categorical column subplots

Size, location, and price

Using gridplot

Changing size

Visualizing categorical data

High to low prices by region

Creating nested categories

Visualizing sales by period

Customizing 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 glyph settings

Shooting guards versus small forwards

Big shooters

Evolution of the point guard

Highlighting and contrasting

Highlighting by glyph size

Steals vs. assists

Adding a color bar

Free throw percentage by position

Communicating with text

Sales by time and type of day

Products sold by the time of day

Adding annotations

Box annotations for sales performance

Setting up a polygon annotation

Annotating Netflix stock price growth

Storytelling with Visualizations

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.

Introduction to widgets

Adding a Div

Modifying glyph size with a widget

Slider widgets

Automotive stocks analysis

Tech stock performance over time

Select widgets

Travel analysis

Changing line plots with Select

Congratulations!

Introduction to Widgets

Bakery Sales

Stocks

NBA Player Statistics

Australia Property Market

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&apos;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&apos;ll also get to grips with configuration tools to change how viewers interact with your plot, discover Bokeh&apos;s custom themes, learn how to generate subplots, and even how to add widgets to your plots!

Data Manipulation with pandas

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

Interactive Data Visualization with Bokeh

When to use a scatter plot

Interactive Data Visualization with Bokeh

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