Python Data Visualization Transcripts
Chapter: Plotly
Lecture: Additional plot types
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Now that we've gone through how to use some colors and other customizations. Let's go through some other plot types.
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This example I want to create a box plot. So I use the box function and pass in the X. And Y. For this individual plots.
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So we want to look at the fuel cost as well as the class summary. And then I also add color.
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I update the labels and then I also decide that I don't want to show the legend. So let me show you what that looks like.
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So right now I have the legend but it's really duplicating the information I have here So I want to turn it off and so I use this fig update layout
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and show legend equals false to make sure that that doesn't show. So now I have my box plots and this is a really nice way to see
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what the distribution the fuel cost looks like across these different summaries.
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And then the hover effect that we have seen with plotly works well here. So you can see the medium,
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the mean, the portals, the upper fence as well as some of these outliers So this is where you get some really powerful features a plotly to help
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you understand the data and quickly kind of see what's going on with some of the
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outliers in this specific case, if we wanted to do a strip plot we would do the exact same thing instead of a box we would say P.
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X.strip. I'm gonna turn off my legend as well. And now we have a strip plot. So you can see each individual observation. There's a lot of detail here.
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But a good way to see all the observations for these specific vehicles. And then I'm going to show how we can do some annotations on our plots.
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So let me plot this and I'll show you what I'm doing. So I want to show my fuel distribution but also add a line with the average
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and annotate that average. So what we've done here is created that average cost. I'm rounding it to make it a little bit easier to read.
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I create my histogram. I've added a different qualitative color sequence in this case G10. Now I'm using update Y axis to say a different title.
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So instead of count, it says number of cars. And then I'm also updating my figure by adding a vertical line at the average cost
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and I add an annotation and I can use my python f string formatting to put
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that average cost in there with a dollar sign and then finally call figure show to
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view the actual image. So I've had a really nice visualization that I've annotated with my specific data that I wanted to show.
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Similar to what we've done in some of our other plots sticking with the theme of
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histograms. I'm also gonna show how there are different ways that you can configure what you show on your margins with your histogram.
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So this is a little bit different plot that maybe we haven't seen before where we can show the fuel cost is a histogram.
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But up here we can actually see each individual distribution what that box plot looks like
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So it's a powerful way to summarize a lot of data in one plot and we accomplish that by saying the marginal equals box.
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So that tells it to add a box plot on that margin up here, the top margin. This is just a really nice way to get to understand your
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data, get to understand how you can use plotly to quickly configure your visualization and zoom in on the visualization.
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That's going to be right for whatever you're trying to accomplish with that specific task.