[Python] For loop is several times faster in R than in Python using the rpy2 library

The following simply for block takes about ~3 sec to complete in R:

library(MASS)
nruns <- 2000
nelems <- 50
maxX <- 1
maxY <- 1
for(i in 1:nruns) {
dataX <- runif(nelems, 0, maxX)
dataY <- runif(nelems, 0, maxY)
kde2d(dataX, dataY, n=50, lims=c(0, maxX, 0, maxY) )
}


The same code run in Python through the rpy2 library takes between 4-5 times more:

from rpy2.robjects import r
from rpy2.robjects.packages import importr
importr('MASS')

nruns = 2000
r.assign('nelems', 50)
r.assign('maxX', 1)
r.assign('maxY', 1)
for _ in range(nruns):
r('dataX <- runif(nelems, 0, maxX)')
r('dataY <- runif(nelems, 0, maxY)')
r('kde2dmap <- kde2d(dataX, dataY, n=50, lims=c(0, maxX, 0, maxY))')


Is this just because I'm using the rpy2 library to communicate with R or is there something else at play? Can this be improved in any way (while still running the code in Python)?

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