I rewrote some python code using numpy to do a performance comparison.
The results were the opposite of what I wanted. Numpy was slower
than Python without numpy. Is there something wrong with my approach?
# mean of n values within an array
import numpy, time
def nmean(list,n):
a = []
for i in range(1,len(list)+1):
start = i-n
divisor = n
if start < 0:
start = 0
divisor = i
a.append(sum(list[start:i])/divisor)
return a
t = [1.0*i for i in range(1400)]
start = time.clock()
for x in range(100):
nmean(t,50)
print "regular python took: %f sec."%(time.clock() - start)
def numpy_nmean(list,n):
a = numpy.empty(len(list),dtype=float)
for i in range(1,len(list)+1):
start = i-n
if start < 0:
start = 0
a[i-1] = list[start:i].mean(0)
return a
t = numpy.arange(0,1400,dtype=float)
start = time.clock()
for x in range(100):
numpy_nmean(t,50)
print "numpy took: %f sec."%(time.clock() - start)
Results:
regular python took: 1.215274 sec.
numpy took: 2.499299 sec.
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