[RadiozRadioz]

Performance is my heuristic. I can't make it quantitative, because 100M records in 1 minute might be considered fast for some use cases, but slow for others. For me it's the qualitative "is this thing too slow?".

Personally, I see a dataframe library as a last resort. I prefer to improve the algorithm, or push more things into the database, or even scale up the hardware in some cases. If I've exhausted all other options and it's still too slow, then I use a dataframe library.

But then I'm not a data scientist. I've found that data scientists have a hammer that they really really like, so they'll use it everywhere.