Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Seaborn API summary

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0:00 Now that we've done some Seaborn plots. I'll summarize this API. In a quick reference that you can refer back to in the future.
0:09 Most of the plots we've talked about will follow this similar approach. First, we specify the type of plot in this example,
0:15 a catplot and passing the data frame that we're going to reference. All the future references will refer to columns in that data frame.
0:25 Here we pass in the Y and the X. Columns for the combined fuel economy and the number of cylinders we can optionally pass in
0:33 the drive and the date range, column and row that tell seaborn to vary the data by these two comb values.
0:43 We can pass in the color to use using the hue parameter. In this case we tell it based on the transmission value of automatic or manual and
0:52 we finalize this call with the type of plot that we want to do in this case a bar plot and behind the scenes,
1:00 Seaborn is then going to split the data up by these different variables, summarize it and display it. As shown here.
1:09 This is very powerful and speaks to the value that Seaborn brings and the types of really quick analytics that you can do using the Seaborn API.

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