Python Data Visualization Transcripts
Chapter: Matplotlib
Lecture: Styles
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We've gone through a lot of examples of matplot lib, code. And one of the things that you probably noticed is that the visualizations
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on average don't look very good. You can get the feeling that there's opportunities to customize the colors,
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but it would likely take a lot of code to set this visualization up to look
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more visually appealing. Fortunately matplot lib has some shortcuts available to us using styles
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Here's a list of all of the styles that are available to us. Let me show you how you would actually use the style.
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So let's say we wanted to use the 'ggplot' style, we would set that and behind the scenes it configures a bunch of different parameters.
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So let's just try for simple scatter plot. And now when we run it we get a much different display of the plot and
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maybe you can play around with this and figure out what works best for your own
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visualization. But I'll show a quick example of how you can print out several of
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the different styles and apply it to a visualization using a context manager.
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So let me walk through what we've done, I created a list of a sample of different styles and you can play around with this and see which ones you like.
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And then I use the plot.style context manager to generate our scatter plot using that different style. I also use an
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F string so that you can see what the style is. So let's take a look at the difference in some of these styles and you can
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see that the style controls a lot of different aspects of the visualization controls the color the grids, the fonts,
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even the size of the visualization. So as you play around with matplot lib and use it, you'll find the style that works best for your own scenarios,
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and I encourage you to play around with them and see what is visually appealing for your own applications.