如何按包含 Python 列表的数据框的单元格进行分组？

Question

I am using Python and Pandas, trying to sum up, in an efficient way, dataframe's values in different rows based on lists of IDs instead of unique IDs.

df:

Name  -  ID  - Related IDs          - Value
z     -  123 - ['aaa','bbb','ccc']  -  10
w     -  456 - ['aaa']              -  20
y     -  789 - ['ggg','hhh','jjj']  -  50
x     -  012 - ['jjj','hhh']        -  60
r     -  015 - ['hhh']              -  15

It will be possible to try to explode each row by the element of the list but it may duplicate the values to sum and it might not be an efficient solution in terms of timing and resources.

```python
f = {'Sum': 'sum'}

df = df.groupby(['Related IDs']).agg(f) 
#it is not working has is matching element wise 
#rather then by element

df = df.reset_index()
```

What I am expecting is a new column "Sum" that sum up the values "Value" of rows which have one or more Related IDs in common. As the following:

Name  -  ID  - Related IDs          - Value - Sum
z     -  123 - ['aaa','bbb','ccc']  -  10  -  30
w     -  456 - ['aaa']              -  20  -  30
y     -  789 - ['ggg','hhh','jjj']  -  50  -  125
x     -  012 - ['jjj','hhh']        -  60  -  125
r     -  015 - ['hhh']              -  15  -  125

Answer 1

Use networkx with connected_components :

import networkx as nx
from itertools import combinations, chain

#if necessary convert to lists 
df['Related IDs'] = df['Related IDs'].apply(ast.literal_eval)

#create edges (can only connect two nodes)
L2_nested = [list(combinations(l,2)) for l in df['Related IDs']]
L2 = list(chain.from_iterable(L2_nested))
print (L2)
[('aaa', 'bbb'), ('aaa', 'ccc'), ('bbb', 'ccc'), 
 ('ggg', 'hhh'), ('ggg', 'jjj'), ('hhh', 'jjj'), ('jjj', 'hhh')]

#create the graph from the dataframe
G=nx.Graph()
G.add_edges_from(L2)
connected_comp = nx.connected_components(G)

#create dict for common values
node2id = {x: cid for cid, c in enumerate(connected_comp) for x in c}

#create groups by mapping first value of column Related IDs
groups = [node2id.get(x[0]) for x in df['Related IDs']]
print (groups)
[0, 0, 1, 1, 1]

---

#get sum to new column
df['Sum'] = df.groupby(groups)['Value'].transform('sum')
print (df)
  Name   ID      Related IDs  Value  Sum
0    z  123  [aaa, bbb, ccc]     10   30
1    w  456            [aaa]     20   30
2    y  789  [ggg, hhh, jjj]     50  125
3    x   12       [jjj, hhh]     60  125
4    r   15            [hhh]     15  125