Reputation: 65
I have a Dataframe with 1 column (+the index) containing lists of sublists or elements. I would like to detect common elements in the lists/sublists and group the lists with at least 1 common element in order to have only lists of elements without any common elements. The lists/sublists are currently like this (exemple for 4 rows):
Num_ID
Row1 [['A1','A2','A3'],['A1','B1','B2','C3','D1']]`
Row2 ['A1','E2','E3']
Row3 [['B4','B5','G4'],['B6','B4']]
Row4 ['B4','C9']
n lists with no common elements (example for the first 2):
['A1','A2','A3','B1','B2','C3','D1','E2','E3']
['B4','B5','B6','C9','G4']
Upvotes: 5
Views: 241
Reputation: 88226
You can use NetworkX
's connected_components
method for this. Here's how I'd approach this adapting this solution:
import networkx as nx
from itertools import combinations, chain
df= pd.DataFrame({'Num_ID':[[['A1','A2','A3'],['A1','B1','B2','C3','D1']],
['A1','E2','E3'],
[['B4','B5','G4'],['B6','B4']],
['B4','C9']]})
Start by flattening the sublists in each list:
L = [[*chain.from_iterable(i)] if isinstance(i[0], list) else i
for i in df.Num_ID.values.tolist()]
[['A1', 'A2', 'A3', 'A1', 'B1', 'B2', 'C3', 'D1'],
['A1', 'E2', 'E3'],
['B4', 'B5', 'G4', 'B6', 'B4'],
['B4', 'C9']]
Given that the lists/sublists have more than 2 elements, you can get all the length 2 combinations from each sublist and use these as the network edges (note that edges can only connect two nodes):
L2_nested = [list(combinations(l,2)) for l in L]
L2 = list(chain.from_iterable(L2_nested))
Generate a graph, and add your list as the graph edges using add_edges_from. Then use connected_components, which will precisely give you a list of sets of the connected components in the graph:
G=nx.Graph()
G.add_edges_from(L2)
list(nx.connected_components(G))
[{'A1', 'A2', 'A3', 'B1', 'B2', 'C3', 'D1', 'E2', 'E3'},
{'B4', 'B5', 'B6', 'C9', 'G4'}]
Upvotes: 5