I think it's mostly a nod to the fact that R's data.table blows everybody else out of the water by such a ridiculously wide margin. It's like a factor of 2 faster than the next fastest...
So if you're writing a dataframe library as a hobby project, it's far less demotivating to use "all the other implementations" as your basis for comparison, at least initially.
R is from ~2000, while pandas started in 2011. Is it possible that the lack of compute power had an effect on the required performance characteristics?
Thank you, my brief research led to a list of versions that had R 1.0 as 2000, but it appears that v0 lasted a good many years. Pandas as well was in v0 for many years so it is the better comparison to use like-for-like.
So if you're writing a dataframe library as a hobby project, it's far less demotivating to use "all the other implementations" as your basis for comparison, at least initially.