Read_csv - yes. I just tried loading a 5MM row csv. Pandas took 5.5 secs. Polars took 0.6 secs.

.apply - might be faster in Polars but will not be as fast as using native expressions. That’s because applying a Python function invokes the GIL which kills parallelization and this is an inherent limitation of Python.

That said, I try not to use Python functions these days. I write transformations in SQL (or native expressions in Polars) and these can be executed at full speed with complete vectorization and parallelization.