The data world owes a lot to pandas, but it has plenty of sharp edges and using it can sometimes involve pretty close knowledge of how things like indexing/slicing/etc work under the hood.
If I get stuck in polars, its almost always just a "what's the name of the function to use?" type problem rather than needing lots of knowledge about how things are working under the hood.
When you are still figuring out things step by step, pandas does a lot of heavy lifting for you so you don't have to think about it.
E.g. I don't have to think about timeseries alignment, pandas handles that for me implicitly because dataframes can be indexed by timestamps. Polars has timeseries support, but I need to write a paragraph of extra code to deal with it.
Would have made my life a lot easier when I was learning Pandas.
Would also be cool to have a Polars version of this too.
One suggestion:
A lot of folks come to Pandas from using SQL. It might be handy to have a couple "The equivalent of this SQL statement but in Pandas"