Fast replacement of values in a numpy array

Question

I have a very large numpy array (containing up to a million elements) like the one below:

[0,1,6,5,1,2,7,6,2,3,8,7,3,4,9,8,5,6,11,10,6,7,12,11,7, 8,13,12,8,9,14,13,10,11,16,15,11,12,17,16,12,13,18,17,13, 14,19,18,15,16,21,20,16,17,22,21,17,18,23,22,18,19,24,23] 

and a small dictionary map for replacing some of the elements in the above array

{4: 0, 9: 5, 14: 10, 19: 15, 20: 0, 21: 1, 22: 2, 23: 3, 24: 0} 

I would like to replace some of the elements according to the map above. The numpy array is really large, and only a small subset of the elements (occurring as keys in the dictionary) will be replaced with the corresponding values. What is the fastest way to do this?

Answer 1

I believe there's even more efficient method, but for now, try

from numpy import copy  newArray = copy(theArray) for k, v in d.iteritems(): newArray[theArray==k] = v 

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Microbenchmark and test for correctness:

#!/usr/bin/env python2.7  from numpy import copy, random, arange  random.seed(0) data = random.randint(30, size=10**5)  d = {4: 0, 9: 5, 14: 10, 19: 15, 20: 0, 21: 1, 22: 2, 23: 3, 24: 0} dk = d.keys() dv = d.values()  def f1(a, d):     b = copy(a)     for k, v in d.iteritems():         b[a==k] = v     return b  def f2(a, d):     for i in xrange(len(a)):         a[i] = d.get(a[i], a[i])     return a  def f3(a, dk, dv):     mp = arange(0, max(a)+1)     mp[dk] = dv     return mp[a]   a = copy(data) res = f2(a, d)  assert (f1(data, d) == res).all() assert (f3(data, dk, dv) == res).all() 

Result:

$ python2.7 -m timeit -s 'from w import f1,f3,data,d,dk,dv' 'f1(data,d)' 100 loops, best of 3: 6.15 msec per loop  $ python2.7 -m timeit -s 'from w import f1,f3,data,d,dk,dv' 'f3(data,dk,dv)' 100 loops, best of 3: 19.6 msec per loop