apply

问题 I have seen many answers posted to questions on Stack Overflow involving the use of the Pandas method apply . I have also seen users commenting under them saying that \" apply is slow, and should be avoided\". I have read many articles on the topic of performance that explain apply is slow. I have also seen a disclaimer in the docs about how apply is simply a convenience function for passing UDFs (can\'t seem to find that now). So, the general consensus is that apply should be avoided if

问题 I know that loops are slow in R and that I should try to do things in a vectorised manner instead. But, why? Why are loops slow and apply is fast? apply calls several sub-functions -- that doesn\'t seem fast. Update: I\'m sorry, the question was ill-posed. I was confusing vectorisation with apply . My question should have been, \"Why is vectorisation faster?\" 回答1: Loops in R are slow for the same reason any interpreted language is slow: every operation carries around a lot of extra baggage.

问题 So we are used to say to every R new user that \" apply isn\'t vectorized, check out the Patrick Burns R Inferno Circle 4 \" which says (I quote): A common reflex is to use a function in the apply family. This is not vectorization, it is loop-hiding . The apply function has a for loop in its definition. The lapply function buries the loop, but execution times tend to be roughly equal to an explicit for loop. Indeed, a quick look on the apply source code reveals the loop: grep(\"for\", capture

问题 ...regarding execution time and / or memory. If this is not true, prove it with a code snippet. Note that speedup by vectorization does not count. The speedup must come from apply ( tapply , sapply , ...) itself. 回答1: The apply functions in R don't provide improved performance over other looping functions (e.g. for ). One exception to this is lapply which can be a little faster because it does more work in C code than in R (see this question for an example of this). But in general, the rule