Replace values of a numpy index array with values of a list
indexing, numpy, python
Solution
Instead of replacing the values one by one, it is possible to remap the entire array like this:
import numpy as np
a = np.array([1,2,2,1]).reshape(2,2)
# palette must be given in sorted order
palette = [1, 2]
# key gives the new values you wish palette to be mapped to.
key = np.array([0, 10])
index = np.digitize(a.ravel(), palette, right=True)
print(key[index].reshape(a.shape))
yields
[[ 0 10]
[10 0]]
Credit for the above idea goes to @JoshAdel. It is significantly faster than my original answer:
import numpy as np
import random
palette = np.arange(8)
key = palette**2
a = np.array([random.choice(palette) for i in range(514*504)]).reshape(514,504)
def using_unique():
palette, index = np.unique(a, return_inverse=True)
return key[index].reshape(a.shape)
def using_digitize():
index = np.digitize(a.ravel(), palette, right=True)
return key[index].reshape(a.shape)
if __name__ == '__main__':
assert np.allclose(using_unique(), using_digitize())
I benchmarked the two versions this way:
In [107]: %timeit using_unique()
10 loops, best of 3: 35.6 ms per loop
In [112]: %timeit using_digitize()
100 loops, best of 3: 5.14 ms per loop
Problem
Suppose you have a numpy array and a list: ``` >>> a = np.array([1,2,2,1]).reshape(2,2) >>> a array([[1, 2], [2, 1]]) >>> b = [0, 10] ``` I'd like to replace values in an array, so that 1 is replaced by 0, and 2 by 10. I found a similar problem here - http://mail.python.org/pipermail//tutor/2011-September/085392.html But using this solution: ``` for x in np.nditer(a): if x==1: x[...]=x=0 elif x==2: x[...]=x=10 ``` Throws me an error: ``` ValueError: assignment destination is read-only ``` I guess that's because I can't really write into a numpy array. P.S. The actual size of the numpy array is 514 by 504 and of the list is 8.