You are aware the pandas was designed to replicate the behavior of base R's dataframes?
I've been a heavy user of both and R's data frames are still superior to pandas even without the tidyverse.
Pandas is really nice for the use case it was designed for: working with financial data. This is a big part of why Pandas's indices feel so weird for everything else, but if your index is a time in a financial time series then all of a sudden Pandas makes sense and works great
When not working with financial data I try to limit the amount of time my code touches pandas, and increasingly find numpy + regular python works better and is easier to build out larger software with. It also makes it much easier to port your code into another language for use in production (i.e. it's quick and easy to map standard python to language X, but not so much a large amount of non-trivial pandas).