Seaborn is a great tool for quickly producing formatted plots without much coding work. However, by adding a few chart parameters to a plotting function, we can customize our plots and present more complex information.

For example, we can make it easier to compare plots by positioning them side-by-side. We can also show trends for multiple groups through differences in color and style in a single plot. We can even use seaborn parameters to make our plots publication-ready by adding titles, writing in annotations, and choosing color palettes that are appropriate to the plot’s use.

In this lesson, we will:

  • Add confidence intervals to bars and lines
  • Adjust the color and style of lines and points
  • Create multi-plot displays
  • Annotate plots to make them even more informative!

In the Jupyter notebook to the right, we’ll start by running some code examples to see how modifying chart parameters can transform our plots.


The provided Jupyter Notebook has code to view a plot before and after adding additional parameters. Run the initial code cells to load necessary libraries and data. Then run each code cell in the notebook to see how the plot changes.

As you work through the notebook, check out the similarities and differences in the code structure of each plot. Think about:

  • What additional parameters do the plots take?
  • What do you think those extra parameters do to the plot?
  • How has the plot been improved?

Your work here is not graded, so feel free to explore and play with the code before selecting Next to move to the next exercise.

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