Python Data Visualization Transcripts
Chapter: Seaborn
Lecture: Data set changes
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Before we step into actually doing visualizations. I wanted to talk about a new file that will be using where I've summarized a
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few of the values that are in our fuel economy that we've been looking at to make it a little more easy to use Seaborn for visualization.
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So one of the things is that when you have a data set with a lot
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of different categorical values, sometimes it can be useful to break them up into smaller groups that are easier to summarize.
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I've made a couple changes that I wanted to walk through. The first one is the vehicle class. So you can see there are many types of cars,
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trucks, you can also tell if it's a four wheel drive or two wheel drive vehicle. So I've broken it up into a drive column.
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So the two wheel drive and four wheel drive vehicles have a separate indicator. There's also an indicator, whether it's a car,
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an suv a pick up a wagon or another. And then the transmission also has a lot of different values that really don't drive that
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much differentiation in the analysis that we're doing. So I've decided to break the transmission into an automatic and emmanuel category.
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And then another one where there are a lot of variables is the fuel type, where you can see that we have different types of gasoline,
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we have diesel, we have electric, we have other alternative fuel types. So I've chosen to break it into four categories,
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gas, diesel, electric and other. And then finally, for the years since we have so many years,
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I decided it would be interesting to just break the years into two ranges. So ranged from 2011, to 2020 and then another range from 2000 to 2010,
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and this is purely based on looking at the data and the types of visualizations that
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we wanted to do. I figured these different categories and groupings will showcase some of the unique features that Seaborn has.