Find out about Crystal Bar Charts and create your personal with Python

12 min learn

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14 hours in the past

There are a lot of methods to raised perceive a sequence of information. Whether or not or not it’s a set of temperatures, check scores, grocery costs, step counts or UFO sightings, there’s all the time one thing to be taught in regards to the nature of one thing by way of the lens of information evaluation and visualization.

Within the following, I’ll describe a brand new method for visualizing a sequence of information that makes use of easy threshold-based clustering. I hope you’ll discover it to be a recent strategy to pair with standard strategies of visualization like histograms and field plots for gathering a brand new perspective on a function of curiosity.

Numerically

How does one set itself aside? By way of a single numerical attribute, let’s assume a easy threshold is the usual by which ordered values are in comparison with each other and separated into teams. Take for instance the next sequence:

[ 0 , 1 , 1 , 2 , 3 , 5 , 8 , 13 , 21 , 34 , 55 , 89 , 144 ]

That is after all the primary 13 digits of the Fibonacci sequence. If we take the distinction of every worth and the prior worth within the sequence, beginning with third place, we get the identical sequence shifted ahead by 2 steps:

[ _ , _ , 0 , 1 , 1 , 2 , 3 , 5 , 8 , 13 , 21 , 34 , 55 ]

Now let’s set a threshold of 5 and assume something better than 5 delineates a brand new cluster as we traverse the sequence from left to proper. This may end result within the first 8 gadgets being grouped collectively (values of 0–13) with every subsequent merchandise being the one merchandise in its group since all remaining variations exceed 5.

For the Fibonacci sequence this methodology yields a predictable end result, however in exploratory knowledge evaluation all kinds of patterns could emerge. I’ll describe easy methods to discover these patterns with a brand new sort of information visualization, beginning with a few acquainted chart sorts under for comparability.

With a Histogram