Question

我正在使用Python和Pandas，试图以有效的方式根据ID列表而不是唯一ID总结数据行在不同行中的值。

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

可能会尝试通过列表的元素爆炸每一行，但它可能会复制要累加的值，并且在时间和资源方面可能不是有效的解决方案。

```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()
```

我期望的是新列“ Sum”，该列汇总具有一个或多个相关ID的行的值“ Value”。如下：

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

将networkx与connected_components一起使用：

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

如何按包含Python中列表的数据框单元进行分组？

1 个答案: