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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!
Introduction to BokehFree
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 Bokeh50 xpWhen to use a scatter plot50 xpBlocks vs. rebounds100 xpKevin Durant's performance across seasons100 xpShooting ability by position100 xpConfiguration tools50 xpThe best tools for the job100 xpSetting tools100 xpAdding LassoSelectTool100 xpThe HoverTool50 xpAdding a HoverTool100 xpFormatting the HoverTool100 xp
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 style50 xpColors, legend, and theme100 xpCustomizing glyphs100 xpCustomizing axes50 xpAverage building size100 xpSales over time100 xpSubplots50 xpCategorical column subplots100 xpSize, location, and price100 xpUsing gridplot100 xpChanging size100 xpVisualizing categorical data50 xpHigh to low prices by region100 xpCreating nested categories100 xpVisualizing sales by period100 xp
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 glyph settings50 xpShooting guards versus small forwards100 xpBig shooters100 xpEvolution of the point guard100 xpHighlighting and contrasting50 xpHighlighting by glyph size100 xpSteals vs. assists100 xpAdding a color bar100 xpFree throw percentage by position100 xpCommunicating with text50 xpSales by time and type of day100 xpProducts sold by the time of day100 xpAdding annotations50 xpBox annotations for sales performance100 xpSetting up a polygon annotation100 xpAnnotating Netflix stock price growth100 xp
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.
PrerequisitesData Manipulation with pandas
Core Curriculum Manager, DataCamp
George is a Core Curriculum Manager at DataCamp. He has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about health technologies and all things data science.
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