With the dataclass I can look at the class definition, with SQL I can look at the database schema in git, or at the very least log in and `DESCRIBE table`. With Pandas I can find where the dataframe is defined, but then I need to walk through any structural transformations made to it and keep track of its structure in my head. Alternatively I can run the Pandas program in a debugger, set a breakpoint and inspect the dataframe.
With all you need to do some work, but I find the Pandas one more involved because you don't have an authoritative "reference", just an initial state then some transformations. With the Pandas example I have to run the program (in my head or actually). The program might need to pull in test data (hopefully some has been provided). The worst is when the structure of the DF is derived from the source data rather than stated in code (e.g. reading in a CSV). It's much more to do than looking at a class definition or declarative database schema; there's a "sequence" to it, there are transformation steps happening to the DF that I need to keep track of.
With all you need to do some work, but I find the Pandas one more involved because you don't have an authoritative "reference", just an initial state then some transformations. With the Pandas example I have to run the program (in my head or actually). The program might need to pull in test data (hopefully some has been provided). The worst is when the structure of the DF is derived from the source data rather than stated in code (e.g. reading in a CSV). It's much more to do than looking at a class definition or declarative database schema; there's a "sequence" to it, there are transformation steps happening to the DF that I need to keep track of.