[DNF2]

In some cases, applying a limited set of basic operations might make up a significant part of development time, but in my experience most of the time is spent designing algorithms, parsing function input and managing the flow of vectorized expressions across function boundaries. And that is generally more efficient in the context of multiple dispatch with efficient loops.

For example, writing a function that works on scalars, then mapping or broadcasting it over array input is very good for productivity (and is efficient in Julia). On the other hand, designing an inherently array-based algorithm is often a headache that one must contend with for the sake of performance in numpy.

So, if you design somewhat complex or novel algorithms, I don't think object-dot notation, or the lack thereof, has a significant impact on productivity.