Document

to_dict_of_dicts(G, nodelist=None, edge_data=None)

Notes

For a more custom approach to handling edge data, try:

dod = {
    n: {
        nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()
    }
    for n, nbrdict in G.adj.items()
}

where custom returns the desired edge data for each edge between n and :None:None:`nbr`, given existing edge data :None:None:`dd`.

Parameters

G : graph: A NetworkX graph
nodelist : list: Use only nodes specified in nodelist
edge_data : scalar, optional: If provided, the value of the dictionary will be set to :None:None:`edge_data` for all edges. Usual values could be :None:None:`1` or :None:None:`True`. If :None:None:`edge_data` is :None:None:`None` (the default), the edgedata in G is used, resulting in a dict-of-dict-of-dicts. If G is a MultiGraph, the result will be a dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize handling edge data. :None:None:`edge_data` should not be a container.

Returns

dod : dict: A nested dictionary representation of G. Note that the level of nesting depends on the type of G and the value of :None:None:`edge_data` (see Examples).

Returns adjacency representation of graph as a dictionary of dictionaries.

Examples

>>> G = nx.path_graph(3)
... nx.to_dict_of_dicts(G)
{0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}}

Edge data is preserved by default ( edge_data=None ), resulting in dict-of-dict-of-dicts where the innermost dictionary contains the edge data:

>>> G = nx.Graph()
... G.add_edges_from(
...     [
...         (0, 1, {'weight': 1.0}),
...         (1, 2, {'weight': 2.0}),
...         (2, 0, {'weight': 1.0}),
...     ]
... )
... d = nx.to_dict_of_dicts(G)
... d  # doctest: +SKIP
{0: {1: {'weight': 1.0}, 2: {'weight': 1.0}},
 1: {0: {'weight': 1.0}, 2: {'weight': 2.0}},
 2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}}

>>> d[1][2]['weight']
2.0

If :None:None:`edge_data` is not :None:None:`None`, edge data in the original graph (if any) is replaced:

>>> d = nx.to_dict_of_dicts(G, edge_data=1)
... d
{0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}}

>>> d[1][2]
1

This also applies to MultiGraphs: edge data is preserved by default:

>>> G = nx.MultiGraph()
... G.add_edge(0, 1, key='a', weight=1.0)
'a'

>>> G.add_edge(0, 1, key='b', weight=5.0)
'b'

>>> d = nx.to_dict_of_dicts(G)
... d  # doctest: +SKIP
{0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}},
 1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}}

>>> d[0][1]['b']['weight']
5.0

But multi edge data is lost if :None:None:`edge_data` is not :None:None:`None`:

>>> d = nx.to_dict_of_dicts(G, edge_data=10)
... d
{0: {1: 10}, 1: {0: 10}}

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

networkx.linalg.graphmatrix.adjacency_matrix networkx.convert.to_dict_of_dicts

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