Yes, and what I find most frustrating in my experience is additions to languages that break convention or are against the spirit / zen of the language itself. Speaking from experience with different paradigms
sklearn is an excellent example of a whole ecosystem which feels like you are coding machine learning in Python, whereas something like the Python ReaScript API feels awful and far away from the design of the language. It’s a real barrier in the latter to thinking creatively and tugs at your attention all the time so I definitely something that is worth getting right.
In light of that, I think datasets perhaps need better ways of accessing them programatically that is suited to the language or perhaps better documentation for those kinds of maneouvers people might want to do. Without being the total fanboy of Python there are numerous ways to access data structures depending on the task at hand.
Do you need one thing from your ‘dataset-like’ object?:
x = dataset["label"]
Now you have a speedy pass by reference to that vaue (no copies)
Do you want all the keys from a dataset that start with drums?
x = [dataset[x] for x in dataset.keys() if x.startswith("drums")]
Do you need to do something horrible to your data?
for key, value in dataset.items():
if "modular" in key and value < 10:
I’m not sure what the answer is, definitely Max is going to suck at constructing these kinds of accessors and queries but perhaps SC is a place to think about these types of manipulations initially followed by a whole load of JS spaghetti for the visual-ites.
Perhaps another set of lightweight objects that perform primitive dataset commands would be useful.
Here is an imagination of how that might look disregarding all possible problems this entails
Honestly, the barrier to datasets is the only thing that stops me using them more and prototyping stuff elsewhere rather than Max (which is where I prefer to test out things dirty with audio to boot).