to_numpy_recarray(G, nodelist=None, dtype=None, order=None)
to_numpy_recarray
is deprecated and will be removed in NetworkX 3.0. Use nx.to_numpy_array(G, dtype=dtype, weight=None).view(np.recarray)
instead.
When :None:None:`nodelist` does not contain every node in G, the adjacency matrix is built from the subgraph of G that is induced by the nodes in :None:None:`nodelist`.
The NetworkX graph used to construct the NumPy recarray.
The rows and columns are ordered according to the nodes in :None:None:`nodelist`. If :None:None:`nodelist` is None, then the ordering is produced by G.nodes().
A valid NumPy named dtype used to initialize the NumPy recarray. The data type names are assumed to be keys in the graph edge attribute dictionary. The default is dtype([("weight", float)])
.
Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. If None, then the NumPy default is used.
The graph with specified edge data as a Numpy recarray
Returns the graph adjacency matrix as a NumPy recarray.
>>> G = nx.Graph()
... G.add_edge(1, 2, weight=7.0, cost=5)
... A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)])
... print(A.weight) [[0. 7.] [7. 0.]]
>>> print(A.cost) [[0 5] [5 0]]See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.convert_matrix.from_numpy_matrix
networkx.convert_matrix.to_numpy_recarray
networkx.convert_matrix.to_numpy_matrix
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