> Do you use it over many machines (RAMs)? If you mean whether I run it distributedly a la Spark then no. If you mean whether I test it on various machines wit

> Do you use it over many machines (RAMs)? If you mean whether I run it distributedly a la Spark then no. If you mean whether I test it on various machines with different RAM sizes then yes. > I dont care so much about the memory and CPU stuff, I mostly leave the heavy lifting to an SQL engine. Well, I care. Both pandas and polars are, to my view, single-machine dataframe library, so the memory and CPU constraints are rather stringent. My comparison is based solely on my experience: reading csv files that are 20% to 50% the size of RAM, pandas takes (or errors out after) 2 to 10 minutes, while polars finishes in 20 seconds. Queries in pandas are almost always slower than polars. But reading your comment, it seems you and I have different use cases for dataframe libraries, which is fine. I mostly use them for exploratory analysis, so the SQL api is not that much of a plus to me, but the performance is.