4 ms·
Probably something like: with multiprocessing.Pool(core_count) as p: p.map(do_something, list)
by powersnail 3y ago
Probably something like:
with multiprocessing.Pool(core_count) as p:
p.map(do_something, list)
- j7ake 3y agoThe parallel code structure looks very different from the standard for loops in python. So there is a lot of rewriting to get things to work on parallel in python compared to R. In R you just replace lapply with mclapply.
- qfwfq_ 3y agoBut, many R tools are already vectorised, so your shift from lapply() to mclapply() is about as fair a comparison as claiming it's "just" a shift from python's builtin map() to pool.map(). Anybody can play this game, and it's not helpful. I've been using+teaching R now for nearly seven years and the number of times I've used lapply can be counted on one hand. > just https://justsimply.dev/ https://justsimply.dev/
- Bootvis 3y agoI use sapply all the time to transform data all the time. It tends to be less code (no counter, no output initialisation ) and easier to follow if that style is familiar.
- j7ake 3y agoI am curious now, what do you use instead of lapply (or other *apply variants)?
- nequo 3y agoThere is also this in the futureverse if you like for loop style code more: library(doFuture) plan(multisession) y <- foreach(x = 1:4, y = 1:10) %dofuture% { z <- x + y slow_sqrt(z) } https://dofuture.futureverse.org/ https://dofuture.futureverse.org/
- Dylan16807 3y ago> just This isn't documentation or a guide or helping someone. It's a friendly competition between languages so it gets to use the perspective of someone that's familiar with things.
- lozenge 3y agoSerialisation/deserialisation code will bite you unless you are very careful.
- mellavora 3y agoso, only twice as much code!