In a directed graph in which the nodes have not only IDs, but also data (a dictionary of attributes), I'd to obtain the DFS tree starting from a given source node, including the data of the child nodes.
I've noticed, however, that the nx.dfs_tree function seems to return a tree without any data:
In [1]: import networkx as nx
In [2]: G = nx.DiGraph()
In [3]: G.add_nodes_from([(0, {'name': 'foo'}), (1, {'name': 'bar'}), (2, {'name
...: ': 'baz'})])
In [4]: G.add_edge(0, 1)
In [5]: G.add_edge(1, 2)
In [6]: T = nx.dfs_tree(G, 1)
In [7]: T.nodes[1]
Out[7]: {}
In [8]: T.nodes[2]
Out[8]: {}
In [9]: T.nodes
Out[9]: NodeView((1, 2))
In [10]: G.nodes[1]
Out[10]: {'name': 'bar'}
As seen from the example above, T.nodes[1] is an empty dictionary, whereas G.nodes[1] contains the data dictionary originally passed in.
How can I make it so that T.nodes[1] and other tree nodes contain the same data as the original graph?
I ended up copying over the data from G to T using the add_nodes_from method, which updates existing attributes:
In [31]: T.add_nodes_from((i, G.nodes[i]) for i in T.nodes)
In [32]: T.nodes[1]
Out[32]: {'name': 'bar'}
Any more elegant solutions would be much appreciated.