The right way to use the total capabilities of Matplotlib to inform a extra compelling story

Telling a compelling story with information will get manner simpler when the charts supporting this very story are clear, self-explanatory and visually pleasing to the viewers.

In lots of circumstances, substance and kind are simply equally necessary.
Nice information poorly introduced won’t catch the eye it deserves whereas poor information introduced in a slick manner will simply be discredited.

I hope this can resonate with many Information Analysts, or anybody who needed to current a chart in entrance an viewers as soon as of their lifetime.

Matplotlib makes it fast and straightforward to plot information with off-the-shelf capabilities however the tremendous tuning steps take extra effort.
I spent fairly a while researching greatest practices to construct compelling charts with Matplotlib, so that you don’t need to.

On this article I give attention to stacked space charts and clarify how I sewed collectively the bits of data I discovered right here and there to go from this…

… to that:

All photographs, until in any other case famous, are by the creator.

As an example the methodology, I used a public dataset containing particulars about how the US are producing their electrical energy and which will be discovered right here — https://ourworldindata.org/electricity-mix.

On high of being an incredible dataset as an example stacked space charts, I additionally discovered it very insightful.

Supply: Ember — Yearly Electrical energy Information (2023); Ember — European Electrical energy Assessment (2022); Vitality Institute — Statistical Assessment of World Vitality (2023)
License URL: https://creativecommons.org/licenses/by/4.0/
License Kind: CC BY-4.0