Geometric means are a quick and easy way to benchmark system/interpreter performance. The below function is an example of calculating geometric averages in Python:

```def geometric_mean(nums):
'''
Return the geometric average of nums
@param    list    nums    List of nums to avg
@return   float   Geometric avg of nums
'''
return (reduce(lambda x, y: x*y, nums))**(1.0/len(nums))```

I use the below script when I am comparing different Python interpreters, and I also use it to determine when to offload from specific servers due to low performance.

```#!/usr/bin/env python
from random import randint
from timeit import timeit

def geometric_mean(nums):
'''
Return the geometric average of nums
@param    list    nums    List of nums to avg
@return   float   Geometric avg of nums
'''
return (reduce(lambda x, y: x*y, nums))**(1.0/len(nums))

def random_means(loop_cnt=1):
'''
Loop geometric_mean multiple times, use random seeds
@param    int    loop_cnt    Amount of times to loop
'''
seed_nums = [0,0,0,0,0] #< Will be randomly filled in below loop
for i in xrange(0, loop_cnt):
for j in xrange(0, 5):
seed_nums[j] = randint(0, 1000)
geometric_mean(seed_nums)

# Loop 10,000 iterations of random_means(), print the amount of time it takes
print timeit(random_means, number=10000)```
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