在 pandas DF中对连接的图进行分组 [英] Group connected graphs in pandas DF

问题描述

我有一个熊猫DF，其中每一列代表一个节点，两列代表一条边缘，如下所示:

I have a pandas DF where each column represent a node and two columns an edge, as following:

 import pandas as pd
df = pd.DataFrame({'node1': ['2', '4','17', '17', '205', '208'],
               'node2': ['4', '13', '25', '38', '208', '300']})

所有节点都是无向的，也就是说，您可以从一个到另一个 undirected_graph

All Nodes are Undirected, i.e. you can get from one to the other undirected_graph

我想将它们分为所有关联的组(连通性)，如下:

I would like to group them into all connected groupes (Connectivity), as following:

df = pd.DataFrame({'node1': ['2', '4','17', '17', '205', '208'],
           'node2': ['4', '13', '25', '38', '208', '300']
            ,'desired_group': ['1', '1', '2', '2',  '3', '3']})

例如，对前两行进行分组的原因是因为它可以从节点2到达节点13(通过4).

For example, the reason why the first two rows were grouped, is because its possible to get from node 2 to node 13 (through 4).

我设法找到的最接近的问题是这个: 熊猫-根据列值将数据框重塑为边缘列表但据我了解，这是一个不同的问题.

The closest question that i managed to find is this one: pandas - reshape dataframe to edge list according to column values but to my understanding, its a different question.

在此方面提供任何帮助都将非常有用，谢谢.

Any help on this would be great, thanks in advance.

推荐答案

使用networkx connected_components

import networkx as nx

G=nx.from_pandas_edgelist(df, 'node1', 'node2')

l=list(nx.connected_components(G))

L=[dict.fromkeys(y,x) for x, y in enumerate(l)]

d={k: v for d in L for k, v in d.items()}

#df['New']=df.node1.map(d)
df.node1.map(d)
0    0
1    0
2    1
3    1
4    2
5    2
Name: node1, dtype: int64

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