Create An Infographic Using Matplotlib

From my previous posts about the hierarchical structure of matplotlib plotting and the many ways to instantiate axes, we can see that these features render matplotlib a great potential for creating highly complex and customisable visualisations. To demonstrate this and also improve my own understanding of matplotlib, I set out to make an infographic using matplotlib this week. An infographic generally combines visual imagery, data charts, and minimal text together.

Temperature patterns infographic

I designed this infographic to show the temerature patterns across eight Australian capital cities.

The Many Ways To Call Axes In Matplotlib

Although matplotlib is extremely powerful and the only limitation might be our imagination, it is a bit challenging for new users to find the right path as there are always more than one way to achieve the same goal in matplotlib. Calling axes is one of them. Let’s say you just decide to make plots using object-oriented interface (aka artist layer plotting) in matplotlib. However, I bet you will be soon running into problems when trying to instantiate axes to start your plotting.

How To Visualise Sydney’s Temperature Change from 1859 to 2019 Using Matplotlib

I believe one of the main current events you have read about must be the Australian Bushfire Crisis. The devastating disaster has affected more than 10 million hectares of land, which is compatible to the land area of Portugal, and an estimated 1 billion wild mammals, birds and reptiles have been killed (Source here). Increasing evidence has shown the links between climate change/global warming and this year’s bushfire, such as extreme temperature (1., or, That Is The Question In Matplotlib

As a newbie converted recently from using R to Python, data visualisation via the standard Python visualisation library matplotlib is an inevitable part through the learning process. At the very beginning, after following several easy-to-find matplotlib tutorials, I had an impression that “en, it’s not as hard as I thought”. After import matplotlib.pyplot as plt, I can basically plot everything by calling…) (e.g. plt.plot(…), plt.scatter(…),…), plt.xlabel(…), plt.ylabel(…), plt.