How to sum values based on a second index array in a vectorized manner

Let' say I have a value array

values = np.array([0.0, 1.0, 2.0, 3.0, 4.0])

and an index array

indices = np.array([0,1,0,2,2])

Is there a vectorized way to sum the values for each unique index in indices? I mean a vectorized version to get sums in this snippet:

sums = np.zeros(np.max(indices)+1)
for index, value in zip(indices, values):
    sums[index] += value

Bonus points, if the solution allows values (and in consequence sums)to be multi-dimensional.

EDIT: I benchmarked the posted solutions:

import numpy as np
import time
import pandas as pd


values = np.arange(1_000_000, dtype=float)
rng = np.random.default_rng(0)
indices = rng.integers(0, 1000, size=1_000_000)


N = 100


now = time.time_ns()
for _ in range(N):
    sums = np.bincount(indices, weights=values, minlength=1000)
print(f"np.bincount: {(time.time_ns() - now) * 1e-6 / N:.3f} ms")


now = time.time_ns()
for _ in range(N):
    sums = np.zeros(1 + np.amax(indices), dtype=values.dtype)
    np.add.at(sums, indices, values)
print(f"np.add.at: {(time.time_ns() - now) * 1e-6 / N:.3f} ms")


now = time.time_ns()
for _ in range(N):
    pd.Series(values).groupby(indices).sum().values
print(f"pd.groupby: {(time.time_ns() - now) * 1e-6 / N:.3f} ms")


now = time.time_ns()
for _ in range(N):
    sums = np.zeros(np.max(indices)+1)
    for index, value in zip(indices, values):
        sums[index] += value
print(f"Loop: {(time.time_ns() - now) * 1e-6 / N:.3f} ms")

Results:

np.bincount: 1.129 ms
np.add.at: 0.763 ms
pd.groupby: 5.215 ms
Loop: 196.633 ms

Solution

Another possible solution, which:

First, creates an array of zeros b with a length equal to the number of unique elements in the indices array
It then uses the np.add.at function to accumulate the values from the values array into the corresponding positions in b as specified by the indices array.

b = np.zeros(1 + np.amax(indices), dtype=values.dtype)
np.add.at(b, indices, values)

Output:

array([2., 1., 7.])