Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Seaborn overview
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Now let's go through a little bit of background on Seaborn, as I mentioned earlier. Seaborn has been around a long time.
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The initial release was in 2013 and it has been continually updated over the years. So it continues to get new features and improvements to the
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API. In addition to that, Seaborn is based on matplot lib. So a lot of the concepts that we've talked about are going to apply to Seaborn
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and give you the foundation to more effectively use it for your visualizations at its core
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Seaborne is a library for making statistical graphs and python. So it builds on top of matplot lib and integrates very well with pandas data
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frames. So a little bit more detail about what Seaborn does. So it operates on a whole data frame and array, so you don't slice it by column,
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you pass an entire data frame to your Seaborne plots and then internally it performs mapping
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and statistical aggregation and summary to produce the plots. So a lot of the examples that we've gone through up until now you had to
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do some of this on your own. Seaborn abstracts that away for you. It is a data set oriented.
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So it expects kind of this pandas data frame and uses a declared of API. For visualizing your data.
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And what I really like about Seaborn is that it lets you focus on the different elements of your plot and not a lot of time on how to actually draw the
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plot. So Seaborn is really well situated for quickly exploring your data in a very sophisticated way.