R for 循环与 Python for 循环性能

Question

There already is some discussion on this topic but they don't quite address my question. Sorry in advance if they do and I didn't realize.

Here are two simple for-loop setups in R and python -

R for-loop (took 3.41s on my computer):

datafr  <- matrix(0,nrow=24*365,ncol=15)
matrix3d  <- array(0,dim=c(24*365,12,7))

#================
start_time <- Sys.time()
for (p in 1:150) {
  for (m in 1:2) {
    l  <- rep(0.7*runif(365),each=24)
    a  <- rep(0.7*runif(365),each=24)
    pp <- 1+floor(15*runif(7))
    for (j in 1:7) {
      bun     <- datafr[,pp[j]]*a
      for (h in 2:(24*365)) {
        matrix3d[h,m,j] <- matrix3d[h-1,m,j]*l[h] + bun[h]
      }  
    }
  }
}
Sys.time() - start_time
#================
#took 3.41s on my computer

And here's the same code in Python (#took 17.87s on my computer):

import numpy as np
import time
import pandas as pd

datafr= pd.DataFrame(0, index=range(24*365),columns=range(15))
matrix3d = np.zeros((24*365,12,7))

#=============
start_time = time.time()
for p in range(150):
    for m in range(2):
        l = np.repeat(0.7*np.random.random(365),24)
        a = np.repeat(0.7*np.random.random(365),24)
        pp = 1+np.floor(15*np.random.random(7))
        for j in range(7):
            bun = np.asarray(datafr.iloc[:,int(pp[j])-1],dtype=np.float32)*a
            for h in range(1,(24*365)):
                matrix3d[h,m,j] = matrix3d[h-1,m,j]*l[h]+bun[h] #bottleneck
round(time.time() - start_time,2)
#================
#took 17.87s on my computer

R is over 5 times faster than Python. Is this to be expected? I saw that Python's for-loop is faster than R's, unless you use R's lapply in which case R beats Python if the number of steps is greater than 1000 ( https://datascienceplus.com/loops-in-r-and-python-who-is-faster/ ), but that is not what I see here (I'm not using lapply). Can the Python script be improved in a way that doesn't use decorators or magic functions or generators etc? I'm simply curious. Thanks

Answer 1

R loops used to be slow during 2014 or 15. They aren't slow anymore software's and programming language evolve over time and things are never true forever. JS is a perfect example of this.

R for loops are not slow and you can use them anytime you want however the garbage collector of R is slow and you shouldn't grow a vector inside a loop which copies it multiple time. If you avoid that part you are almost always in safe hands

And you could also try set method from data.table if you need more speed from loop or parallelize it