I have a numpy.array items of N~10^7 integers and a numpy.array coefficients of N floats.
items contains repeated entries, and only ~sqrt(N) unique entries.

I would like an array of unique entries unique_items and an array of accumulated coefficients unique_coefficients such that unique_coefficients[i] is the sum over j of coefficients[j] such that items[j]=unique_items[i].

For example if we have

items = [1,1,2,2,3,4]
coefficients = [1,2,3,4,5,6]

then

unique_items = [1,2,3,4]
unique_coefficients = [3,7,5,6]

The first entry of unique_coefficients is the sum of the 2 first entries of coefficients because the two first entries of items are 1 etc. Note that items is not necessarily sorted.

For now, I first compute a dict where each key is a unique element of items, and each value is a list of indices of the element in items, then I sum over the indices stored in this dict:

d = {}
for index, item in enumerate(items):
    if not item in d:
        d[item] = []
    d[item].append(index) 
unique_items = d.keys()
unique_coefficients = [np.sum(coefficients[i]) for i in d.values()]

Is there a way to avoid doing this python for loop and stay in numpy instead ? This for loop is where my code spends the most time.