On Sun, Feb 10, 2019 at 10:06 AM Kyle Lahnakoski firstname.lastname@example.org wrote:
but none of these are as elegant or readable as the vectorized syntax
result = process.(vector .+ sequence) .* items
I've a bit lost track of who is proposing what, but this looks like an
extra set of operators: ".*", ".+" etc. for vectorized operations.
So I want to point out that is was proposed way back when for numpy:
MATLAB for instance, has a the usual operators: *, +, etc meaning "matrix math", and then another set of "itemwise" operators with a "dot form: .*, .+ . - for "itemwise" math.
numpy, on the other other had uses the regular operators for itemwise operations (what we're calling vectorized here), and Python lacked an extra set of operators that could be used for matrix math. Adding another full set (.*, .+, etc) was discussed A LOT and the Python community did not want that.
Then someone had the brilliant observation that matrix multiplication was teh only one that was really useful and presto! the @ operator was born.
Anyway -- just suggesting that a full set of "vectorized" operators will liley see a lot of resistance. And for my part, having mean the opposite of what it does for numpy would be unfortunate as well.
I am interested in vector operations. I have situations where I want to
perform some conceptually simple operations on a series of not-defined-by-me objects to make a series of conclusions. The calculations can be done succinctly in SQL, but Python makes them difficult.
Bringing real world examples of this would be a good idea for this discussion
I'm inclined to think that something like pandas (maybe more generally SQL -like that the number crunching focus of Pandas) might be better than new syntax for the language -- but only real examples will tell.
I don't work with data like that much, but I"m pretty sure I've seen Python packages that to attempt to address these use cases. (that being querying and processing tabular data)