Data Visualization: Building Interactive Visualizations with Bokeh

Bokeh 2.2.3    |    Beginner
  • 10 videos | 1h 5m
  • Includes Assessment
  • Earns a Badge
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An interactive visualization library, Bokeh allows users to create diverse graphics and highly interactive dashboards and data applications. In this course, you'll achieve a foundational knowledge of using Bokeh to build simple graphs and visualizations. You'll start by exploring how to install Bokeh on your local machine, display charts inline within your Jupyter notebooks, and create an interactive visualization. You'll then recognize how to save Bokeh charts as HTML and PNG files. Next, you'll investigate how to visualize categorical data using bar charts, stacked bar charts, and clustered bar charts. You'll also identify how to implement pie charts and donut charts to represent compositions in your data. You'll finish the course by examining the ease of interactivity and granular customizations that Bokeh offers.


  • discover the key concepts covered in this course
    use Jupyter notebooks to install and import Bokeh
    create a Bokeh chart and save it in PNG and HTML formats
    display your visualization inline in a Jupyter notebook
    visualize data using a bar chart
  • visualize data using a stacked bar chart
    represent data using a clustered bar chart
    visualize proportions in data using pie charts
    visualize proportions in data using donut charts
    summarize the key concepts covered in this course


  • 1m 54s
    In this video, you’ll learn more about your instructor and the course. In this course, you’ll install Bokeh and then use Jupyter notebooks to implement and display charts. You’ll also learn how to use Bokeh to visualize categorical data using charts. FREE ACCESS
  • 5m 43s
    In this video, you’ll watch a demo and begin learning about Bokeh. First, you’ll learn how to check the version of Python you’re using. Then, you’ll open up a Jupyter notebook and rename it. Following onscreen directions, you’ll learn how to add additional cells to your notebook. Then, you’ll check to see that you have the correct versions of libraries in place. FREE ACCESS
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    3.  Saving Bokeh Charts as PNG and HTML Files
    8m 22s
    In this video, you’ll watch a demo that teaches you how to save Bokeh charts. First, you’ll begin by importing bits of functionality from bokeh.plotting. You’ll learn more about the figure, output_file, and show modules. You’ll discover Bokeh includes two sets of APIs, a relatively low-level API, which is contained within bokeh.models and a higher level API, which is in bokeh.plotting. FREE ACCESS
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    4.  Displaying Bokeh Charts Inline in Jupyter Notebooks
    9m 15s
    In this video, you’ll watch a demo. In this demo you’ll learn about the output_notebook function. This function brings the Bokeh visualization to your Jupyter notebook. Following onscreen directions, you’ll use bokeh.plotting to import figure, output_notebook, and show. Then you’ll learn to import the math module. You’ll learn how to use bokeh.plotting, which contains the code for figure and various other high level abstractions. FREE ACCESS
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    5.  Creating Bar Charts in Bokeh
    6m 15s
    In this video, you’ll watch a demo. In this demo, you’re going to make use of bar charts in Bokeh. You’ll also learn about one of the most important Bokeh abstractions called the ColumnDataSource, or CDS. First, you’ll import pandas as pd. Pandas give you the valuable data frame abstraction, which is a helpful way to access data that is organized into rows and columns. FREE ACCESS
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    6.  Creating Stacked Bar Charts in Bokeh
    9m 27s
    In this video, you’ll watch a demo. In this demo, you’re going to construct a second bar chart. This time you’ll do so by making use of a column datasource abstraction. First, you’ll create another Pandas DataFrame from our original data frame, which was called sales_data. You’ll use the groupby method. You’ll use the Pandas DataFrame to build a ColumnDataSource. Once you’ve executed the code, you’ll see your bar chart displayed in your Jupyter Notebook. FREE ACCESS
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    7.  Implementing Bokeh Clustered Bar Charts
    3m 58s
    In this video, you’ll watch a demo. In this demo, you’re going to construct a group bar chart. First, you’ll prepare your data source. You’ll use the pd.crosstab method. Following onscreen instructions, you’ll perform operations on the sales_data Pandas DataFrame and save the results. You’ll enter the onscreen code and view the resulting visualization. Next, you’ll import dodge from bokeh.transform to create your group bar chart. FREE ACCESS
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    8.  Visualizing Data in Bokeh Using Pie Charts
    10m 22s
    In this video, you’ll watch a demo. In this demo, you’ll learn how to construct pie charts. You’ll discover pie charts are great for analyzing composition. You’ll start by looking at the various required import statements in Bokeh. After all of those import statements, you’ll learn to invoke output_notebook. This will set the output to be displayed inline inside your Jupyter notebook. FREE ACCESS
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    9.  Creating Donut Charts in Bokeh
    6m 50s
    In this video, you’ll watch a demo. In this demo, you’ll learn more about donut charts. In some situations, donut charts are better than pie charts because they allow you to specify two donuts, which are concentric. In this demo, you’ll build a simple donut chart, which represents the composition at one point in time. You’ll begin building the chart by performing a groupby operation on the same data frame used in the previous demo. FREE ACCESS
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    10.  Course Summary
    2m 55s
    In this video, you’ll summarize what you learned in this course. You learned Bokeh is an interactive visualization library that provides elegant, concise construction of versatile graphics and affords high-performance interactivity over large or streaming data sets. You explored how to display Bokeh plots inline in a Jupyter notebook. You learned how to visualize categorical data using clustered and grouped bar charts. Then, you learned more about pie and donut charts. FREE ACCESS


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