numpy.add.at

numpy.ufunc.at

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.

Parameters:
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first operand. If first operand has multiple dimensions, indices can be a tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be broadcastable over first operand after indexing or slicing.
Examples

Set items 0 and 1 to their negative values:>>> a = np.array([1, 2, 3, 4])>>> np.negative.at(a, [0, 1])>>> print(a)array([-1, -2, 3, 4])Increment items 0 and 1, and increment item 2 twice:>>> a = np.array([1, 2, 3, 4])>>> np.add.at(a, [0, 1, 2, 2], 1)>>> print(a)array([2, 3, 5, 4])#Add items 0 and 1 in first array to second array, and store results in first array:>>> a = np.array([1, 2, 3, 4])>>> b = np.array([1, 2])>>> np.add.at(a, [0, 1], b)>>> print(a)array([2, 4, 3, 4])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.at.html#numpy.ufunc.at