Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Seaborn summary
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We'll wrap up the chapter on seaborn by talking about the pros and cons and where it fits into your data analysis stack.
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From a pro's perspective, Seaborn has some really sophisticated analysis tools and it's very customizable
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with a lot of different API's to suit your own needs. I find it very fast for exploratory analysis and at the end of the day it
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does create some very visually appealing plots from a cons perspective plots are not interactive so
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you can't click and drag and explore the individual output. Some of the customization can be difficult.
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It can take some time to figure out how to do what you want to do when you start to stray outside of the Standard API
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Where does that leave us for how Seaborn fits into our analysis
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stack? Well, my recommendation is it is a great tool for sophisticated exploratory analysis I think you should take some time to master the API.
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Use themes and some of the matplot lib concepts to customize your visualization when needed. And if you do need something that is truly interactive.
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We'll talk about some of the other tools that you may want to jump into once you are done with initial analysis in seaborn.