How to get K most different rows in csv? We have boring CSV with 10000 rows of ages (float), titles (enum/int), scores (float). How to select 1000 most different rows? I look for a general solution that would work for more than one case.
What do I mean by different:

We have N columns each with int/float values in a table.
You can imagine this as points in ND space
We want to pick K points that would have maximised distance between each other.

So if we have 100 points in a tightly packed cluster and one point in the distance we would get something like this for three points:
 or this

It looks like an ND point cloud &quot;triangulation&quot; with a given resolution yet not for 3d points... So how to select K most distant rows (points) from N (with any complexity)?
To select the most different rows, you would need to define first what you consider different. For ages and scores, subtracting values would work, for example:
Row1

Age is 38
Score is 0.2

Row2

Age is 87
Score is 1.0

Difference by numeric feature:

Age Diff is 49
Score Diff is 0.8

Those values could be normalized or weighted to account for different importances between features.
For titles, you’d probably need to use a text similarity metric, such as tf-idf with cosine similarity or embedding vector distance. The most different ones are the ones with higher score/age difference and lower title similarity.