Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Figure and axes level plots
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I want to go over a core Seaborn concept that is really critical to understanding how Seaborn works and most effectively using it in your own workflow.
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Seaborn has a concept of figure level plots. There are three main plots that you're going to want to use most of the time.
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Relational plots, Distribution plots and Categorical plots. Each of these underneath is kind of a wrapper for axes level plots.
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So when you think about relational plots, there are scatter plots and line plots that show the relationship between two different variables.
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And you can create a scatter or a line plot by passing the parameter kind equal scatter or kind equals line to the relation plot function.
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From a distribution plot perspective you can plot, histograms, kde, ecdf or rug plots using
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the displot function and the category plot or catplot has a lot of really useful visualizations. So a strip plot,
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a swarm plot the box plot is probably very familiar to many violin plots. Boxen plots, point plots, bar plots,
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count plots can all be visualized using the catplot function. So the key takeaway from this is that you want to use the figure level plots
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first. These are easier and more flexible to quickly get a plot, but then when you need to do really advanced level customizations,
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you may want to drop down to the axis level plots and customize it for your
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unique needs. But I think starting with the figure level plots will help you understand
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Seaborn more effectively and get up to speed more quickly in using seaborn for your own data visualize functions.