Add values in numpy array successively, without looping [duplicate]

Answer 1

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.

Answer 2

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.