Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Seaborn overview

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0:00 Now let's go through a little bit of background on Seaborn, as I mentioned earlier. Seaborn has been around a long time.
0:07 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
0:17 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
0:26 and give you the foundation to more effectively use it for your visualizations at its core
0:32 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
0:44 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,
0:54 you pass an entire data frame to your Seaborne plots and then internally it performs mapping
1:00 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
1:08 do some of this on your own. Seaborn abstracts that away for you. It is a data set oriented.
1:14 So it expects kind of this pandas data frame and uses a declared of API. For visualizing your data.
1:21 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
1:31 plot. So Seaborn is really well situated for quickly exploring your data in a very sophisticated way.

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