A dataframe API allows you to write code in Python, with native syntax highlighting and your LSP can complete it, in one analysis file. Inlined SQL is not as nice, and has weird ergonomics.

UDFs in most dataframe libraries tend to feel better than writing udfs for a sql engine as well.

Polars specifically has lazy mode which enables a query optimizer, so you get predicate push down and all the goodies if SQL, with extra control/primitives (sane pivoting, group_by_dynamic, etc)

I do use ibis on top of duckdb sometimes, but the UDF situation persists and the way they organize their docs is very difficult to use.

because method chaining in Polars is much more composable and ergonomic than SQL once the pipeline gets complex which makes it superior in an exploratory "data wrangling" environment.