It won’t. What will change it is using a completely different architecture in DataFrames. Have you read the discussions? You might want to mention these, or I think maybe they might be benchmarkable on master? The interesting development to track “live” would be how it’s doing on master.
Of course you will always be stuck with having to deal with dates and strings in some way, but you are still free to choose how you represent this data in memory or on disk. For example, in Julia, dates and times are represented by integers (do DateTime().instant.periods.value) and indeed, at the end of the day everything is an integer, but the usual approach is to keep them wrapped in DateTime objects when they sit in a dataframe. This is the approach I’m starting to question. Perhaps instea…
The history of Nullable is littered with PRs and ideas on how to make them work better in a data science context that were never merged/implemented. I think many of them were not taken up because folks felt those ideas were too magical for a software engineering Nullable type (and I strongly agree with that). I don’t have that constraint with DataValue, i.e. I’m just picking up many of these ideas. A second difference is that my array type DataValueArray does not use the higher order approach …