Creating effective data visualisations is a critical skill within many disciplines, be it business analysis, data science or geoscience. Aesthetically pleasing and easy-to-understand data visualisations can help spark ideas in our target audience or get them to take action based on the information we display.
Within the Python world, there are several data visualisation libraries available. However, many learners of Python and data science start out with matplotlib.
Matplotlib provides a versatile way to present your data however you want. In my previous articles, I have shown various data visualisations that go a few steps beyond the default charts.
However, creating these figures does require patience and extra Python code. This often results in much searching on StackOverflow or through the library documentation to find possible solutions to modify even the smallest parts of the figures.
By following a few simple guidelines, we can immediately improve any figure created with matplotlib.
In this article, I share four of my favourite guidelines I regularly use when creating figures for sharing on Medium or in academic publications.
These guidelines are not necessarily restricted to matplotlib; they can equally be applied to any software that allows you to create charts, such as Excel or Tableau.
One of the quickest and easiest ways to improve matplotlib charts is to reduce the amount of “chart junk” displayed.
Chart junk refers to the unnecessary and confusing elements on the chart that don’t really add any value to the reader or the data being presented.
When building your chart, you should ensure you only include elements that help the reader understand the data better.