Python Data Visualization Transcripts
Chapter: Pandas
Lecture: Advanced specialized plots
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In addition to the standard plots that we've talked about with Pandas.
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There are four very specialized visualizations that I want to walk through so that you're aware of them and can use them where appropriate.
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Each of these is available through a separate import. We're going to cover the scatter matrix and andrews,
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curves, parallel coordinates and the radviz report. Let me go ahead and rerun this new notebook that I've created and we'll go through
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the first example of how to show a scatter matrix. Now let's look at what the scatter matrix is doing.
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It's a really convenient tool to see what the interactions look like between your various columns So for each combination of column,
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it plots different types of visualizations, scatter plots or histograms comparing the two. So you can see in this example,
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If you look at the CO2 compared to the barrels of oil used per vehicle,
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you can see a strong correlation line which certainly makes sense and intuitively something that you
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would expect from our data. So let's bring this down to a smaller subset of
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variables that we want to compare to give a better example of how to use this
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tool. So here are all the vehicle class options that are available to us right now let's consolidate some of those.
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So I'm gonna create a new car class data frame that is just for compact cars
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Midsize cars, subcompact cars and large cars so we'll filter out trucks and other types of data. So we're just looking at compact cars,
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mid sized cars and then we're just gonna include cylinders, fuel costs, C02 and vehicle class. Let's take a look at what that looks like.
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So you can see it's a much smaller set of data and now we'll do a scatter matrix with this smaller data set just to make it a little bit easier to
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understand what's going on. There you go. Now you can see how each of these values is plotted against the other and in
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those areas where the fuel costs is plotted against fuel cost we just show a
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histogram. So this is a really useful tool to quickly explore your data and understand
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what sort of relationships there might be between the different columns.
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I'm going to go through a more complex visualization called the Andrews curves which are useful
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for visualizing high dimensional data and that means data with a lot of different variables that are hard or difficult to see the interactions between.
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And then Andrew's curve is a unique way to visualize that data and here you can
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see each of the different types of cars and start to visualize how the values differ I'm not going to go into the details on how to use.
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Andrews curves. This is really a more advanced machine learning visualization but it is fairly
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unique and I believe pandas is one of the few places that has this visualization.
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So as you move down your machine learning pathway and start to tackle more and more
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complex visualizations and projects you might want to consider this in a similar vein,
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parallel coordinates are also a useful tool for visualizing high dimensional data.
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Once again, this is an interesting way to look at the interaction of these multiple car variables to fuel cost cylinders in CO2.
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And this is another way to view high dimensional data and help you maybe
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understand different ways that you can cluster your observations together in the final chart we will
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go through the final visualization is a radio visualization, which is another way to see where you might have natural clustering of your data and
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once again all three of these are definitely more advanced visualizations but I wanted to call
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them out so that you are aware that they are available in pandas when you need them. And finally I have been using a little bit of the matplot lib
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customization to plot these and I'm gonna show how to create one figure with three rows and one column showing the andrews,
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curves, the parallel coordinates and the radviz. All in one plot. So I create my figure, create three axes,
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plot those values on the axis for each of those different visualization and do a little
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bit of customization along the way to make it more visually appealing and understandable.
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And now let's look at each of these plots together in one visualization, you can imagine how you could use this to start to get a better feel for
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some of those more complex data and doing further machine learning or analysis on it in the future.