[ad_1]
The best way to use the complete capabilities of Matplotlib to inform a extra compelling story
Telling a compelling story with information will get method simpler when the charts supporting this very story are clear, self-explanatory and visually pleasing to the viewers.
In lots of circumstances, substance and type are simply equally essential.
Nice information poorly introduced won’t catch the eye it deserves whereas poor information introduced in a slick method will simply be discredited.
I hope this may 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 simple to plot information with off-the-shelf features however the wonderful tuning steps take extra effort.
I spent fairly a while researching finest practices to construct compelling charts with Matplotlib, so that you don’t should.
On this article I deal with 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 photos, except in any other case famous, are by the creator.
For example the methodology, I used a public dataset containing particulars about how the US are producing their electrical energy and which might be discovered right here — https://ourworldindata.org/electricity-mix.
On high of being a fantastic dataset for example stacked space charts, I additionally discovered it very insightful.
Supply: Ember — Yearly Electrical energy Information (2023); Ember — European Electrical energy Overview (2022); Power Institute — Statistical Overview of World Power (2023)
License URL: https://creativecommons.org/licenses/by/4.0/
License Sort: CC BY-4.0
[ad_2]
Source link
