Maybe has been asked before, but I can't find it. Sometimes I have an index I, and I want to add successively accordingly to this index to an numpy array, from another array. For example:
A = np.array([1,2,3])
B = np.array([10,20,30])
I = np.array([0,1,1])
for i in range(len(I)):
A[I[i]] += B[i]
print(A)
prints the expected (correct) value:
[11 52 3]
while
A[I] += B
print(A)
results in the expected (wrong) answer
[11 32 3].
Is there any way to do what I want in a vectorized way, without the loop? If not, which is the fastest way to do this?
Use numpy.add.at:
>>> import numpy as np
>>> A = np.array([1,2,3])
>>> B = np.array([10,20,30])
>>> I = np.array([0,1,1])
>>>
>>> np.add.at(A, I, B)
>>> A
array([11, 52, 3])
Alternatively, np.bincount:
>>> A = np.array([1,2,3])
>>> B = np.array([10,20,30])
>>> I = np.array([0,1,1])
>>>
>>> A += np.bincount(I, B, minlength=A.size).astype(int)
>>> A
array([11, 52, 3])
Which is faster?
Depends. In this concrete example add.at seems marginally faster, presumably because we need to convert types in the bincount solution.
If OTOH A and B were float dtype then bincount would be faster.
You need to use np.add.at:
A = np.array([1,2,3])
B = np.array([10,20,30])
I = np.array([0,1,1])
np.add.at(A, I, B)
print(A)
prints
array([11, 52, 3])
This is noted in the doc:
ufunc.at(a, indices, b=None)
Performs unbuffered in place operation on operand ‘a’ for elements specified by ‘indices’. For addition ufunc, this method is equivalent to a[indices] += b, except that results are accumulated for elements that are indexed more than once. For example, a[[0,0]] += 1 will only increment the first element once because of buffering, whereas add.at(a, [0,0], 1) will increment the first element twice.