Hi Reader,

Have you ever had a day where you were planning to do one thing, but then something grabbed your attention so strongly that you spent the entire day doing something else?

That’s what this week’s tip is about! 👇

🔗 Link of the week

​AI and Open Source in 2023​

This article is an excellent review of “the year’s highs and lows” by ML & AI researcher Sebastian Raschka. (If you're short on time, you can read his 300-word summary on Twitter.)

👉 Tip #30: Create stunning data visualizations with Datawrapper

The other day, I was reading an article about the “advertised value” of lottery jackpots and was impressed by its attractive data visualizations. They were listed as being “Created with Datawrapper” (which I had never heard of), so I clicked through to see what it was.

Oh... WOW.

​Datawrapper is a tool for creating beautiful and interactive charts, maps, and data tables. It’s targeted towards journalists wanting to enrich their stories, but I think its use naturally extends to anyone who needs to present their data to an audience.

Because it has a generous free plan, I decided it was worth playing around with!

Over the next many hours, I created three different visualizations using data from the World Happiness Report. (I learned about this dataset from Bamboo Weekly, a pandas newsletter written by my pal Reuven Lerner. Thanks Reuven! 🙏)

Below are the visualizations I created, which I would encourage you to click on and interact with:

Although I spent many hours creating these visualizations, the majority of that time was actually spent preparing the data (in pandas). Datawrapper itself was incredibly intuitive to use, and I came away quite impressed with the attractive results that you can produce in minimal time (and without writing any code!)

Side note: This isn’t a sponsored ad for Datawrapper, but it probably should be! 😂

I’m going to spend the next few Tuesday Tips explaining how I prepared the happiness data for Datawrapper using pandas. This will include basic pandas topics, like filtering and sorting and formatting data, as well as intermediate pandas topics, like merging and reshaping data and handling missing values.

In the meantime, if you have a dataset that needs visualizing, I’d encourage you to play around with Datawrapper and perhaps use these visualizations for inspiration!

If you enjoyed this week’s tip, please forward it to a friend! Takes only a few seconds, and it really helps me reach more people! 🙏

See you next Tuesday!

- Kevin

P.S. Wikipedia’s Lamest Edit Wars (data)

Did someone awesome forward you this email? Sign up here to receive Data Science tips every week!