如何根据另一个数组中的重复值在一个数组中添加值？

Question

Suppose I have an array:

Values = np.array([0.221,0.35,25.9,54.212,0.0022])
Indices = np.array([22,10,11,22,10])

I would like to add elements of 'Values' together that share the same number in 'Indices'.

In other words, my desired outputs(s):

Total = np.array([0.221+54.212,0.35+0.002,25.9])
Index = np.array([22,10,11])

I've been trying to use np.unique to no avail. Can't quite figure this out!

Answer 1

We can use np.unique with its optional arg return_inverse to get IDs based on uniqueness within Indices and then use those with bincount to get binned (ID based) summations and hence solve it like so -

Index,idx = np.unique(Indices, return_inverse=True)
Total = np.bincount(idx, Values)

Outputs for given sample -

In [32]: Index
Out[32]: array([10, 11, 22])

In [33]: Total
Out[33]: array([ 0.3522, 25.9   , 54.433 ])

Alternatively, we can use pandas.factorize to get the unique IDs and then bincount as shown earlier. So, the first step could be replaced by something like this -

import pandas as pd

idx,Index = pd.factorize(Indices)

Answer 2

One possibility is to consider using Pandas :

In [14]: import pandas as pd

In [15]: pd.DataFrame({'Values': Values, 'Indices': Indices}).groupby('Indices').agg(sum)
Out[15]: 
          Values
Indices         
10        0.3522
11       25.9000
22       54.4330

This should be self-explanatory, though it doesn't preserve the order of indices (it's not entirely clear from the question whether you care about that).