On the power of the BAN

BANs or big ass numbers are a visualisation technique that are frequently overlooked in favour of unnecessarily complex graphs. While some stakeholders might need to know sales figures going back 18 months and need lots of stats about how last week performed, for others a top level stat that can identify good or bad weeks will suffice and have more impact. In this post I’m going share a nice BAN-based doughnut plot code that I’ve developed in python to do just that and I’ll apply it to 2 common datasets.

Air Passengers Dataset

Let’s start with the air passengers dataset. This set contains information on the number of air passengers per month from 1949–1960. The stats I want to show from my BAN doughnut plot is the number of passengers in a given month and how good that performance is relative to other months. The BAN simply shows the number of passengers that month while doughnut plot shows the percentile rank relative to all other months. The whole figure is coloured appropriately to show instantly whether it was a good week or not based on the quartile. I’ve also used intuitive colours with red meaning bad and green good. Here are the plots for 3 example weeks:

Melbourne Temperatures

The second dataset I’m using is the minimum temperature in Melbourne from 1981–1990. The plots show the same statistics with the BAN displaying the temperature on a particular day. I’ve included this dataset to show a different colour scale that reflects the difference in intuition when it comes to temperature: red is hot and blue is cold. Here’s 4 examples:

Conclusions

Not everyone needs the same level of granularity when it comes to data; simplicity and intuition are key.

Code

The code used to generate these plots is available in this github repo .