That'a true and Julia might be a solution but I don't see the adoption happening anytime soon.
But this particular problem (per row computation) have different options to tackle now in hep-python ecosystem. One approach is to leverage array programming with NumPy to vectorize operations as much as possible. By operating on entire arrays rather than looping over individual elements, significant speedups can often be achieved.
Another possibility is to use a library like Awkward Array, which is designed to work with nested, variable-sized data structures. Awkward Array integrates well with uproot and provides a powerful and flexible framework for performing fast computations on i.e jagged arrays.
Uproot already returns you Awkward array, so both things you mentioned are different ways of saying the same thing. The irreducible complexity of data analysis is there no matter how you do it, and "one-vector-at-a-time" sometimes feel like shoehorning (other terms people come up with include vector-style mental gymnastics).
For the record, vector-style programming is great when it works, I mean Julia even has a dedicated syntax for broadcasting. I'm saying when the irreducible complexity arrives, you don't want to NOT be able to just write a for-loop
But this particular problem (per row computation) have different options to tackle now in hep-python ecosystem. One approach is to leverage array programming with NumPy to vectorize operations as much as possible. By operating on entire arrays rather than looping over individual elements, significant speedups can often be achieved.
Another possibility is to use a library like Awkward Array, which is designed to work with nested, variable-sized data structures. Awkward Array integrates well with uproot and provides a powerful and flexible framework for performing fast computations on i.e jagged arrays.