Visualisation

Is the ubiquitous original still the master?

Matplotlib is probably the first plotting library that every data scientist, or data analyst, comes across if they work in the Python programming language. It appears to be used everywhere.

…so, is it so ubiquitous because it is the best available? Or has it just been around in the industry a long time? What alternatives are there, and how do they measure up?

You may note in the title of the article that I have specifically mentioned static plots.

Static vs Dynamic

Although dashboards, and interactive plots, are an extremely important aspect in exploring and presenting data, there will always be a requirement for static plots.

Reports, technical papers, articles, and anything in print are always going to require static plots. Being able to quickly and easily produce clear, logical and beautiful static plots therefore remains absolutely essential. With that in mind, this article will primarily focus on static plots.

Static plots pose additional challenges

Static plots require all the information to be displayed effectively in one view. There are no dynamic overlays, or zooming and panning, to help gain further information on the fly like you might see in a dashboard or interactive plot.

As the old saying goes “a picture can convey a thousand words”…but the CAN in that statement is very important!

As the old saying goes “a picture can convey a thousand words”. However, the CAN in that statement is very important! If the plot is poorly constructed, the message definitely will not be conveyed effectively.

User vs Library

Producing a well thought out plot, to some extent, comes down to the skill of the practitioner, but it also relies on the usability and extensibility of the plotting library in question.