I have two arrays with the same length (6, in this example). One stores `floats`:

```
a = np.array([0.2, 0.01, 0.5, 0.7, 0., 0.002])

```

the second one stores indices (hence, `int` values):

```
indices = np.array([4, 9, 0, 2, 2, 4])

```

In my code I initialize another array, which has length in general different from that of `a` and `indices`, for example 10 in this example:

```
c = np.zeros(10)

```

I would like to find a Pythonic way to accomplish the following:

```
for i in range(len(indices)):
    c[indices[i]] += a[i]

```

which, in this example, produces:

```
[0.5   0.    0.7   0.    0.202 0.    0.    0.    0.    0.01 ]

```

I tried looking at [this brilliant example](https://stackoverflow.com/questions/23662704/conditional-numpy-cumsum), however I am not sure how this can be applied here.