Python Data Visualization Transcripts
Chapter: Plotly
Lecture: Customizing
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Now go through some examples of how we can customize our visualization. The first thing I want to talk about our templates.
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So I'm gonna do the hissed a gram that we did before. But I'm gonna add this variable template equals plotly white and now you can see
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that I have a very different background and look so compare this to this one.
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So we've got a whole different look just by specifying a different template. So if we wanted to, let's say try Seaborn,
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it gives you a visualization that's reminiscent of your seaborn plots,
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but you have the full interactivity that we have learned to appreciate and plotly you
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can refer to the documentation for all the different template options but this is a really good way to control the visualization of your plotly
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plots as we discussed with Seaborn and some of the other tools using colors is really
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important. So one of the things you can do with with plotly is generate
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these swatches of the colors. So here I'll show all the qualitative colors and you can see that we have a whole bunch of colors.
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So if you want to figure out what colors you want to use, you can use these swatches to kind of get a feel for what you like with
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the pallets that are already there, I'll show how to use one. So let's look at our histogram and I'm going to say I want a color
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discrete sequence of the qualitative set to which is right here and now we'll have a very different visualization with different colors.
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So that can be helpful for you to figure out what colors make the most sense
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for the visualizations you're creating. We can also generate sequential swatches.
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There's a lot of these. This is really helpful when you're doing heat maps and
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other types of visualization. So you can play around with this and decide what looks good for your specific use case.
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So let's do another, histogram using our sequential colors. You can see that I've got these kind of green colors and what you might notice
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that I put this underscore R on here. That means reverse the color. So if I take that out,
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I get very light colors and dark and it's not really that easy to read. So, by putting the underscore R in here,
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then I reversed the color palette so it's from low to high or high to low
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Really helpful as you're playing around with your visualizations and figuring out the best way to present that to your customers.