π Hi!
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I'm really excited this week. π€
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After more than 5 months of hard work, my Matplotlib Journey project is finally complete! π πΎ
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If you want to learn how to make great chart with python, take a look! I'm very proud of what we've created and feedback are awesome.
The last module focuses on making maps in Python. As always, we combined Python techniques with dataviz theory. It was the perfect opportunity to dive into a dataviz story that I believe is foundational.
Let me share it with you and sharpen your dataviz skills at the same time!
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Impeach this
In October 2019, Trump posted a choropleth election map with those words.
The map looked striking: a sea of red counties that seemed to scream overwhelming support.
It racked up 162k likes π³
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Take a moment to look at it.
How accurate do you think it is?
Could it be misleading?
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Land doesnβt vote. People do.
Luckily, many pointed out how misleading this map was.
Karim Douieb created an alternative: a bubble map where each bubble represents a county, sized by population.
This version tells a very different story, probably much more accurate.
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This story highlights a critical flaw of choropleth maps:
Large areas dominate the visual, even if they represent very few people.
This can create a false impression of dominance when what usually matters is the number of people, not the amount of land.
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Another perspective
Thereβs another issue mentioned by someone on my LinkedIn Post.
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A choropleth map like this uses a red/blue binary. A county with 99% Trump votes looks the same as one with 50.05%.
Hereβs an alternative using a color gradient to show shades of support:
Different, right?
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What we would really need is probably a combination of both: bubbles + gradient!
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Why this matters
Even though this story dates back years, I believe itβs a must-know for anyone working with data visualization.
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Itβs rare for a dataviz topic to go mainstream and spark such widespread discussion about how charts can mislead.
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In fact, this moment arguably kickstarted the improvements weβve seen in election maps ever since! π
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Have a wonderful week!
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Yan
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βPS: Iβll be sending a few promotional emails about Matplotlib Journey next week. Apologies in advance for the extra messages π. Launch week is a big deal for me, and building courses like this is how I make a living. Thanks so much for your understanding and support! ππ
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π By the way, here is how I can help!
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- Master R: Join my productive R workflow online course, already helping hundreds to excel in R, Quarto, and GitHub.
- Team Training: Hire me to train your team on Data Visualization and Programming.
- Engaging Talks: Book me for short, impactful talks on Data Visualization and Programming.
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Check yan-holtz.com or hit reply any time! I love hearing from you.
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