问题描述
我有一个非常大的 numpy 数组(包含多达一百万个元素)，如下所示:
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]
和一个小的字典映射，用于替换上面数组中的一些元素
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}
我想根据上面的地图替换一些元素.numpy 数组真的很大，只有一小部分元素(作为字典中的键出现)会被相应的值替换.最快的方法是什么?
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?
推荐答案
我相信还有更有效的方法，但现在，试试
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
微基准测试和正确性测试:
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()
结果:
$ 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
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