Hi Everyone, I've got two questions that I'd like help with:
1. Pandas and numpy arrays can handle multiple types in a sequence eg. a float and a string by using the dtype=object. From what I gather, Arrow arrays enforce a uniform type depending on the type of the first encountered element in a sequence. This looks like a deliberate choice and I'd like to get a better understanding of the reason for ensuring this conformity. Does making the data structure's type deterministic allow for efficient pointer arithmetic when reading contiguous blocks and thus making reading performant? 2. Pandas and numpy can also handle dictionary elements using the dtype=object while pyarrow arrays don't. I'd like to understand the reasoning behind the choice here as well. Thanks again for taking my questions. Kind Regards Simba
