Historically my use of data frames is for reading or analyzing data, not updating it, which is why I mentioned cursors. That said, I am always up for learning something new if it works and performs just as well as my old way.
When it comes to using data frames to update data, I guess my first question is how does that work with very large data sets (feature classes) and memory management. Whereas ArcPy cursors are generators of sorts, i.e., they yield data instead of returning all of the data at once, they still allow data to be updated.
Looking at the pandas documentation for something like iterrows(), is clearly states:
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.
As I mentioned, I am not overly familiar with updating data using data frames, so I will just state that one of my requirements for managing file geodatabase data, or really any data, using the ArcGIS Python API would be to update the data as it is being iterated over and not require an entire data set to be dumped to a data frame and rewritten back to update any values.
