❌ Don't use a legend (do this instead)

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As always, today’s tip is short but powerful. Let’s talk about…
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The problem with legends

Most dataviz libraries make it easy to add legends. That's great! Sometimes, they’re essential for understanding a chart.

But most of the time? Legends are a hassle.

Take this stacked area chart, for example:
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Reading it means constantly jumping back and forth between the chart and the legend. What does this color mean? Check the legend. What about this one? Check again. And again. 😳

That’s a lot of unnecessary cognitive load. Instead of absorbing the story, your audience will struggle just to decode the chart. You’ll lose their attention.

So here’s my advice: If you can remove the legend, remove it.

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Solution 1: Direct labeling

Instead of using a separate legend, label the groups directly on the chart.

Line charts / Stacked area charts? Place the labels near the end of the lines. Scatterplots? Find a good spot near the group’s center. Streamgraphs? Position labels inside the flow where they fit naturally.

Here is an example we've built for the Python Graph Gallery:
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You see a group, and know instantly what it is! This takes extra effort, but trust me: your readers will thank you.

Technical tip:​
In R, the ggrepel package helps position labels without overlap. In Python, check out drawarrow for easy annotation with arrows.

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​Solution 2: Color in the title

That's another solution: use colors in the title to highlight key categories.
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This way, you deliver the main message and introduce the groups in one go.
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Cedric Scherer uses this technique very often. Here is an example with a dumbbell chart from the r graph gallery:
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Takeaway

Whenever possible, ditch the legend to make your chart easier to read.

Still skeptical? Check out Dataviz-inspiration.com. You’ll notice that most top-tier visualizations use direct annotation, not legends.

Hope this helps and see you next week!

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Yan
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​PS: this post is a short abstract from the 3rd module of Matplotlib Journey. We just shipped it, and the course is now almost complete 🎉
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