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Data Storytelling With Python
How To Make Surprising Python Charts With Tufte’s Data-Ink Ratio
Stripping clutter to reveal the real story in agricultural data
In 1983, Edward Tufte introduced what became one of the most cited rules in data visualization: the data-ink ratio.
The idea is super simple — every drop of ink on a chart should showcase data.
Backgrounds, thick box borders, rainbow color schemes and legends that merely repeat what the labels already say are what Tufte called chartjunk, and getting rid of them is the fastest way to make a chart tell its story.
I have been running data visualization intensives at various workshops and seminars for more than 10 years, and watching students clean up their charts is a process I take great satisfaction in.
That is what the data-ink ratio does for you.
So let’s apply it hands-on, using the Our World in Data employment-in-agriculture dataset to show a side of China’s economy that most readers may not know about..
NOTE: The complete notebook file (data_ink_ratio.ipynb) and dataset can be accessed on my GitHub: [HERE]
