[Gretl-users] Reading large csv and sorting data set -- a comparison with 2 python libs

Hi all, out of curiosity I tried to replicate a comparison between two python libraries, namely pandas and py-polars. I also added gretl to the horse race. There two simple tasks to do: 1) Load a 360MB csv file with two columns and 26 million rows. 2) Sort by one of the columns. The summary is as follows: 1) Py-polars takes about 2.5 seconds to load a 360MB large csv file as a data frame. Pandas is about 5 times slower while Gretl needs 36 seconds for this. 2) Sorting 26 million records takes py-polars about 4.6 seconds and hence is just slightly faster than Pandas with 5.8 sec. -- ok, still 20% percent. Gretl needs about 14 seconds for the same task. I am not too bothered with gretl's performance -- okay, reading a csv may be faster but still: how often do you load such a big file? The repo and readme can be accessed here: https://github.com/atecon/gretl_pandas_pypolars There is one thing which is weird though: at the end of the README.md you see that I tried to store the loaded csv data set as a gdtb file. But after an hour of computation I gave up... Maybe worth to look at. Best, Artur