  When exploring a new dataset, Data Scientists often apply analysis queries,
look for insights in the resulting dataframe, and repeat to apply further
queries. We propose in this paper a novel solution that assists data scientists
in this laborious process. In a nutshell, our solution pinpoints the most
interesting (sets of) rows in each obtained dataframe. Uniquely, our definition
of interest is based on the contribution of each row to the interestingness of
different columns of the entire dataframe, which, in turn, is defined using
standard measures such as diversity and exceptionality. Intuitively,
interesting rows are ones that explain why (some column of) the analysis query
result is interesting as a whole. Rows are correlated in their contribution and
so the interesting score for a set of rows may not be directly computed based
on that of individual rows. We address the resulting computational challenge by
restricting attention to semantically-related sets, based on multiple notions
of semantic relatedness; these sets serve as more informative explanations. Our
experimental study across multiple real-world datasets shows the usefulness of
our system in various scenarios.
