How to measure the similarity of sequence values

How to measure the similarity of sequence values

To measure the similarity of data sequences, we can use the methods designed to measure the similarity of strings. In this blog post, I am going to show which metrics can be used to measure the difference between ordered sequences of values, usually between two texts.

Levenshtein distance

In the case of text, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions).

That method can also be reused for measuring the distance between any pair of sequences, for example, pages visited by a user during a single session or products purchased during a user lifetime.

Kendall tau distance

Kendall tau distance is a metric of difference between rankings. It is defined as the number of swaps to be done while bubble-sorting one sequence to get the same order as the second sequence.

Similarly, we can use the metric to get the difference between any sequences. Conveniently, this metric is implemented in Scipy as the scipy.stats.kendalltau function.


Remember to share on social media!
If you like this text, please share it on Facebook/Twitter/LinkedIn/Reddit or other social media.

If you watch programming live streams, check out my YouTube channel.
You can also follow me on Twitter: @mikulskibartosz

For business inquiries, send me a message on LinkedIn or Twitter.


Bartosz Mikulski
Bartosz Mikulski * data scientist / software engineer * conference speaker * organizer of School of A.I. meetups in Poznań * co-founder of Software Craftsmanship Poznan & Poznan Scala User Group