Python Tutorial: Using the distribution plot

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Now that you have a basic understanding of Seaborn and how it relates to matplotlib and pandas, we will spend some time looking at how to customize distribution plots in Seaborn. The basic ideas learned with this plot type can be applied to the other Seaborn plotting functions we will see in upcoming exercises.

The Seaborn API supports customizing the type of plot by using different arguments. In this example, we can use the same distplot() function to create a standard histogram and customize the number of bins to further understand how the values are distributed. This basic approach is used by most functions in Seaborn and is a powerful tool for analyzing data quickly.

As you can see, the distplot() function has several options for configuration and customization. The benefit of this approach is that you can rapidly try different views and settle on the one that seems most appropriate for your analysis. Trying different combinations of the kde and rugp lot can yield important insights.

There are many functions in Seaborn that build upon each other. The distplot() function we have been discussing relies on using additional Seaborn functions such as the kdeplot() and rugplot(). By understanding this relationship, you can further customize Seaborn plots by passing additional arguments to the underlying functions. In this case, we can tell the underlying kde() function to shade the plot by passing the kde_kws dictionary.

Now that you know about several Seaborn functions, let's practice some more.

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