Numpy arrays are not a replacement for lists that you just drop in and everything runs faster. Numpy arrays are a commitment to doing things a certain way. If used correctly, Numpy arrays can be blazing fast. If used naively, they make things very slow.
The short answer to how to use numpy correctly is to act upon whole arrays in one swoop with universal functions (ufuncs). Rather than looping through the array, a ufunc applies a compiled function directly on the area of memory represented by the array. “+”, “-“, “*’, and “/” are interpreted as ufuncs when applied to an array. Others are listed here. If what you want can’t be done with these operations acting on big sections of memory, then you might consider writing a ufunc in c, numba.vectorize, cython, or just not using numpy.
Anyways, here is an example. Using numpy incorrectly led to the code running 4x slower than not using numpy at all. Using numpy the way it was designed to be used resulted in the code running in 1/30th the time as python lists. Using numpy incorrectly is 100x slower than using it correctly.
First, the results are:
Setting up data for test...
Building huge numpy arrays is fast
Done setting up numpy arrays in 0.13 s
Actually, first build some python lists
It takes MUCH longer to build the
equivilent Python lists...
done setting up lists in 0.86 s
Now the tests
cpython working on python lists...
done in 1.99 s
cpython working on numpy arrays...
done is 7.78 s
numpy working on arrays...
done in 0.07 s
and the code for you to run is:
'''calculate the normals of some lines''' import numpy as np def numpy_arrays1(segs): '''segs = [[x1,y1,z1,x2,y2,z2],...] => normals [[xn,yn,zn],...] this function makes a tmp array inorder to reuse memory and not create unnecessary intermediate arrays.''' result = segs[:,3:] - segs[:,:3] tmp = np.empty((len(segs),4),dtype=segs.dtype) tmp[:,:3] = np.square(result) #noticed that if the result of sum goes to an element #of the same array, that element is not part of the sum. #tmp[:,3] here is still garbage, so we can write over it tmp[:,3] = np.sum(tmp,axis=1,out=tmp[:,3]) # SAFE? seems to be tmp[:,3] = np.sqrt(tmp[:,3]) #lame, why do this in two step? result[:,0] = result[:,0] / tmp[:,3] #TODO use newaxis result[:,1] = result[:,1] / tmp[:,3] # and do 3 in 1! result[:,2] = result[:,2] / tmp[:,3] return result def python_loop(segs): '''notice the for loop, and appending to the result list. otherwise, code is the same''' result =  for seg in segs: source = [seg[3+i]-seg[i] for i in xrange(3)] squared = [s**2 for s in source] length = sum(squared)**.5 result.append([s/length for s in source]) return result if __name__ == '__main__': from time import time from math import cos, sin, sqrt print "Setting up data for test..." print " Building huge numpy arrays is fast" now = time() n = 1000000 ts = np.linspace(0,2*np.pi,n) np.seterr(all='ignore') #I might just divide by zero #an r is [x1,y1,x2,y2] segs = np.ones((n,6),dtype=np.float64) segs[:,3] = 1+np.cos(ts) segs[:,4] = 1+np.sin(ts) segs[:,5] = np.arange(n) print " Done setting up numpy arrays in %.2f s"%(time()-now) print "starting tests\n" if True: print "Actually, first build some python lists" print "It takes MUCH longer to build the" print " equivilent Python lists..." now = time() pylist = [[1,1,1,1+cos(t),1+sin(t),i] for t,i in zip(ts,xrange(n))] print " done setting up lists in %.2f s"%(time()-now) print "Now the tests\n\n" print "cpython working on python lists..." now = time() norm1 = python_loop(pylist) print " done in %.2f s\n"%(time()-now) if True: print "cpython working on numpy arrays..." now = time() norm2 = python_loop(segs) print " done is %.2f s\n"%(time()-now) if True: print "numpy working on arrays..." now = time() norm3 = numpy_arrays1(segs) a = time()-now print " done in %.2f s"%(a)