Just calculating the mean on an array and doing some profiling in Python.

profiling_mean_test.py

# mean of n values within an array

import numpy, time

listSize = 10000
meanLength = 50

def nmean(list,n):
   a = []
   for i in xrange(1,len(list)+1):
       start = i-n
       divisor = n
       if start < 0:
           start = 0
           divisor = i
       a.append(sum(list[start:i])/divisor)
   print "a=", a
   return a

def profile_nmean():
   t = [1.0*i for i in xrange(listSize)]
   start = time.clock()
   answer=nmean(t,meanLength)

def numpy_nmean(list,n):
   a = numpy.empty(len(list),dtype=float)
   for i in xrange(len(list)):
       start = i-n+1
       if start < 0:
           start = 0
       a[i] = list[start:i+1].mean(0)
   print "a=", a
   return a

def profile_numpy_nmean():
   t = numpy.arange(listSize,dtype=float)
   start = time.clock()
   answer=numpy_nmean(t,meanLength)

def numpy_nmean2(list,n):
   a = numpy.empty(len(list),dtype=float)
   b = numpy.cumsum(list)
   c = concatenate((b[n:],b[:n]))
   a[:n] = b[:n]/(i+1)
   a[n:] = (b[n:] - c[n:])/(i+1)
   print "a=", a
   return a

def profile_numpy_nmean2():
   t = numpy.arange(listSize,dtype=float)
   start = time.clock()
   answer=numpy_nmean(t,meanLength)

import hotshot, hotshot.stats
prof = hotshot.Profile('nmean.prof')
prof.runcall(profile_nmean)
prof.close()
prof = hotshot.Profile('numpy_nmean.prof')
prof.runcall(profile_numpy_nmean)
prof.close()
prof = hotshot.Profile('numpy_nmean2.prof')
prof.runcall(profile_numpy_nmean2)
prof.close()

print "profiling results of nmean:"
stats = hotshot.stats.load('nmean.prof')
stats.strip_dirs()
stats.sort_stats('cumulative', 'calls')
stats.print_stats()
print "profiling results of numpy_nmean:"
stats = hotshot.stats.load('numpy_nmean.prof')
stats.strip_dirs()
stats.sort_stats('cumulative', 'calls')
stats.print_stats()
print "profiling results of numpy_nmean2:"
stats = hotshot.stats.load('numpy_nmean2.prof')
stats.strip_dirs()
stats.sort_stats('cumulative', 'calls')
stats.print_stats()