People complain about R, but compared to the multitude of import lice and unergonomic APIs in Pandas, R always felt like living in the future.
Polars is a much much more sane API, but expressions are very clunky for doing basic computation. Or at least I can't find anything less clunky than pl.col("x") or pl.literal(2) where in R it's just x or 2.
Still, I'm using Python a ton more now that polars has enough steam for others to be able to understand the code.
In many cases you can pass a string or numeric literal to a Polars function instead of the pl.col (e.g. select()/group_by()).
Overall I agree it's less convenient than in dplyr in the cases where pl.col is required, sure, but not terrible and has the benefit of making the code less ambigious which reduces bugs.
For anything production though, I just stick to pl.col and pl.lit as it's widely used.
IMO R is really slept on because it's limited to certain corners of academia, and that makes it seem scary and outdated to compsci folks. It's really a lovely language for data analysis.
C.blah
Though, I guess they're not on this site :')
Pandas supports so many use cases and is still more feature rich than polars. But you always have the polars.DataFrame.to_pandas() function in your back pocket so realistically you can always at least start with polars.