20.12.2018

Christmas has come. Break out the jumpers, the eggnog, the Christmas Carols. I'll be taking a break from the newsletter over the holiday season, with more good stuff coming in January of 2019.

Until then, it's data as usual.

Read_

3rd wave data viz and starting 2019 inspired

Have we reached a new age of data visualization?
Elijah Meeks has a lot of things to say about data visualization, as seen from his weird and wonderful streams of tweets. His newest blog on Medium covers what he has labelled “the 3rd wave of data visualization". It’s thoughtful, insightful and downright inspired.

Ride the wave →


Where can I find data viz ideas to kick-start my 2019?
Sophie Sparkes has been introducing her own version of the 12 days of Christmas on Twitter this year, and it’s full of great links to learning references. You can find live talks from OpenVisConf, Eyeo Festival, Tableau and more.

Merry Christmas nerds →

Explore_

The path to equality and WSJ visual journalism roundup

What are the biggest barriers facing women across the world?
This interactive data quiz reveals the challenges and restrictions faced by women in 189 different countries (h/t to Beyond Words Studio for a fantastic project).

On the path to equality →


What has the Wall Street Journal visual journalism team been up to this year?
Dunno, it's behind a paywall. Except for this year in Review 2018 roundup of their best visual stories. You can preview the graphs, maps and visuals for free.

Nice one →

Analyse_

Beer and the Pudding

How many units of alcohol are in different types of beer?
The Open Units database contains data on over 1,000 different beers. You can find the brand name, type, ABV, units of alcohol and more for each. Happy Christmas drinking.

Bottoms up →


Where can I find all the datasets used on The Pudding?
2018 was definitely the year of The Pudding. As an online publication, they consistently released interesting, innovative and downright fun stories. You can find all the datasets they used for their pieces on Github.

Data pudding →

Learn_

Working with large data in Pandas and visualizing networks


How can I work with large datasets using Pandas (and still maintain processing speed)?

Pandas is great. It is by far one of the most essential data libraries in Python. But often people complain when working with datasets over 1gb in size. This article has some great tips for working with larger datasets, up to 100gb.

Into that →

How can I create network graphs in Python?
Here's an interesting write-up on using Pandas + Python-igraph for visualizing the Twitter interactions of NBA players.

Slam dunk →

Spread the love.
Share this edition of Data Curious.

Thanks for reading. More to come next week.

Ben_ say hi: twitter | medium | github