SQL is definitely easier and faster to compose than any dataframe syntax but I think pandas syntax (via slicing API) is faster to type and in most cases more intuitive but I still use polars for all df-related tasks in my workflow since it's more structured and composable (although needs more time to construct but that's a cost I'm willing to take when not simply prototyping). When in an ipython session, sql via duckdb is king. Also: python -m chdb "describe 'file.parquet'" (or any query) is wonderful