Fun fact: the aj (asof join) function was my inspiration for pandas.merge_asof. I added the extra parameters (direction, tolerance, allow_exact_matches) because of the limitations I kept hitting in kdb.
https://pandas.pydata.org/docs/reference/api/pandas.merge_as...
aj[`sym`time;t;q]
t,'(`sym`time _q)(`sym`time#q)bin`sym`time#t
A lot of the joins can be distilled to the core operators/functions in a similar manner. For example the plus-join is
x+0i^y(cols key y)#xhttps://numpy.org/doc/2.2/reference/generated/numpy.searchso...
I couldn't figure-out how Arthur's bin matched on symbol though, so I switched to a linear scan on the right table to record the last-seen index for each "by" element. While it worked, my hash table was messy because I relied on Python to handle a whole tuple as a key, which had some issues during initial testing.
The asof join I wrote for Empirical properly categorizes the keys before they are matched. That approach worked far better.
Figuring out things like "what percentage of the time will my starting hand contain the cards I need for my deck to function, and if it doesn't, how many mulligans will it take" will basically ruin competitive MTG for you. I used to buy physical verisons of the decks I made it to top 500 with in Arena, stopped because it wasn't a fun challenge anymore (and lost all interest when they started dragging other IPs in, they'll never get another cent from me)