Since I discovered python and the plotly library, I fell in love with sankey diagrams. If you are not familiar with this type of data representation, it is a diagram that represents flows within a given system. The flows look like ribbons and their width is determined by their value. This type of data visualisation was named after Irish Captain Matthew Henry Phineas Riall Sankey, who used it in 1898 to show the energy efficiency of a steam engine. One of the earlier uses of this type of flow representation is credited to the French civil engineer and infographics pioneer Charles Minard who, in 1869, represented Napoleon’s invasion of Russia in a beautiful flow map combining topography, weather data, time span, and movements and number of French soldiers. This diagram is often quoted as an example of beauty, functionality, truthfulness and insight. The plotly library offers an interactive way to plot a sankey with your data, allowing you to rearrange the flows and aggregate elements (or nodes) before downloading it as a png. In my opinion, one sankey is worth a thousand bar charts, so I played a while with python, pandas and plotly in a Jupyter notebook. After a while, I decided to use Streamlit to create and publish my own webapp so I could generate sankey diagrams when needed even if I didn’t have access to my own private computer, for example at work. You can find the full code onmy Githuband the full article onmy Medium blog. Happy sankeying!
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