Python Data Visualization Transcripts
Lecture: Data set changes
0:00 Before we step into actually doing visualizations. I wanted to talk about a new file that will be using where I've summarized a
0:09 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.
0:18 So one of the things is that when you have a data set with a lot
0:22 of different categorical values, sometimes it can be useful to break them up into smaller groups that are easier to summarize.
0:30 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,
0:37 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.
0:45 So the two wheel drive and four wheel drive vehicles have a separate indicator. There's also an indicator, whether it's a car,
0:52 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
1:00 much differentiation in the analysis that we're doing. So I've decided to break the transmission into an automatic and emmanuel category.
1:09 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,
1:16 we have diesel, we have electric, we have other alternative fuel types. So I've chosen to break it into four categories,
1:23 gas, diesel, electric and other. And then finally, for the years since we have so many years,
1:29 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,
1:39 and this is purely based on looking at the data and the types of visualizations that
1:44 we wanted to do. I figured these different categories and groupings will showcase some of the unique features that Seaborn has.