😤 Stick to line charts they say!

👋 Hi!

Yesterday I posted a short piece on LinkedIn that sparked way more discussion than I expected.
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The topic? Alternatives to the line chart for time series data.

We all know the line chart. It’s the default choice to show how something evolves over time. And for good reason: it’s clear, simple, and familiar to almost everyone.
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But that's not the only option!
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Line chart alternatives

I love exploring chart types. That’s why I built Data To Viz, a decision tree that helps you pick the right chart for your dataset.

Time series offer many possibilities, and I’ve written in depth about them before.

Here’s a quick refresher with some alternatives you might find useful:

➡️ Heatmap – Great for many groups & spotting clear patterns

➡️ Bump chart – Perfect for visualizing rankings

➡️ Circular chart – Reveal seasonal patterns across years

➡️ Area chart – Emphasize trend with a single group

➡️ Lollipop / Bar chart – Best for discrete time points

➡️ Streamgraph – Organic shapes, works only with strong patterns

➡️ Candlestick – A must-have for financial data

➡️ Calendar heatmap – Ideal for daily data and weekly/monthly trends
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If you're not familiar with those, read my previous newsletter issue and visit Data to Viz! Improving your graphicacy is important.

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The debate

Some people commented on LinkedIn that these alternatives are useless, or even counter-productive.

That sparked an interesting discussion! And I’m grateful to those who raised it! 🙏

Their main arguments were:

1. Niche chart types are less familiar, so audiences might not understand them.
2. They can be harder to read.
3. They’re often less precise, sometimes even misleading.

All fair points!

So the real question is: should we limit ourselves to the five 'core' chart types (scatterplot, line, bar, histogram, map)?

Or is it worth keeping some of these niche options in our toolbox for the right situations?

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My take

The core chart types cover most of our needs, and their strength is universal readability.

💥 If they work, use them! 💥

Don't go fancy for the sake of it. Or just for novelty.
And if your audience does not know much about charts, stick to it.

💥 But sometimes you need more! 💥

A line chart with 30 overlapping series quickly turns into a spaghetti chart: almost impossible to read. In those cases, a heatmap can reveal patterns that would otherwise stay hidden.

If you have one data point per day, a calendar plot is a natural choice. Even GitHub uses it to display user activity.

In finance, candlestick charts have been the standard for decades. It’s hard to argue that professionals in that field should just stick to line charts.

And for cyclic data, circular layouts can be brilliant. After all, January and December are only a month apart. Why force them to opposite ends of a chart?

Sure, core chart alternatives may take a few extra seconds to interpret, but they sometimes deliver clarity where the classic options fail.

That’s why I believe analysts should know both the core and the niche chart types. And when to apply each.

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Your thoughts?

What do you think?

Do you consider niche chart types valuable, or are they overkill? Have you seen good real-world examples where they worked better than a line chart?

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Last note

All these alternatives can be built in Python, R, or D3.js. I’ve shared plenty of examples in my galleries.

If you're a Python user, note that I'm currently working on a major update to my Matplotlib Journey project with Joseph Barbier. It's a deep dive into mastering dataviz with python. Both about code and dataviz best practices.

If you’ve ever wanted to go beyond basic plots and really understand the API, it's half price for a few more days, before the price raises forever!

Learn dataviz with Python

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See you next week with some more dataviz tips & ideas!
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Yan
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PS: In December, we're also hosting a live, hands-on workshop on Dataviz with Python. Come spend a few days with me and level up your skills!
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