R-Python apples-to-apples tests

python_test.py

#
# I'm running the same tests described here : http://wesmckinney.com/blog/?p=268
# 

from pandas import *
from pandas.util.testing import rands

n = 100000
indices = Index([rands(10) for _ in xrange(n)])

def sample(values, k):
    from random import shuffle
    sampler = np.arange(len(values))
    shuffle(sampler)
    return values.take(sampler[:k])

subsample_size = 90000

x = Series(np.random.randn(n), indices)
y = Series(np.random.randn(subsample_size),
           index=sample(indices, subsample_size))

xs = x.sort_index()

ys = y.sort_index()
from pandas.util.testing import rands

# -----------

>>> timeit x+y
1 loops, best of 3: 349 ms per loop

>>> timeit xs+ys
1 loops, best of 3: 85.7 ms per loop

test_R.R

library(zoo)

indices = rep(NA, 100000)
for (i in 1:100000)
  indices[i] <- paste(sample(letters, 10), collapse="")

timings <- numeric()

x <- zoo(rnorm(100000), indices)
y <- zoo(rnorm(90000), indices[sample(1:100000, 90000)])

for (i in 1:10) {
  gc()
  timings[i] = system.time(x + y)[3]
}

> mean(timings)

[1] 3.2465

fun and cool