Converting to and from other data formats

To NetworkX Graph

This module provides functions to convert NetworkX graphs to and from other formats.

The preferred way of converting data to a NetworkX graph is through the graph constuctor. The constructor calls the to_networkx_graph() function which attempts to guess the input type and convert it automatically.

Examples

Create a 10 node random graph from a numpy matrix

>>> import numpy
>>> a=numpy.reshape(numpy.random.random_integers(0,1,size=100),(10,10))
>>> D=nx.DiGraph(a) 

or equivalently

>>> D=nx.to_networkx_graph(a,create_using=nx.DiGraph()) 

Create a graph with a single edge from a dictionary of dictionaries

>>> d={0: {1: 1}} # dict-of-dicts single edge (0,1)
>>> G=nx.Graph(d)

See Also

nx_pygraphviz, nx_pydot

to_networkx_graph(data[, create_using, ...])

Make a NetworkX graph from a known data structure.

Dictionaries

to_dict_of_dicts(G[, nodelist, edge_data])	Return adjacency representation of graph as a dictionary of dictionaries.
from_dict_of_dicts(d[, create_using, ...])	Return a graph from a dictionary of dictionaries.

Lists

to_dict_of_lists(G[, nodelist])	Return adjacency representation of graph as a dictionary of lists.
from_dict_of_lists(d[, create_using])	Return a graph from a dictionary of lists.
to_edgelist(G[, nodelist])	Return a list of edges in the graph.
from_edgelist(edgelist[, create_using])	Return a graph from a list of edges.

Numpy

to_numpy_matrix(G[, nodelist, dtype, order, ...])	Return the graph adjacency matrix as a NumPy matrix.
to_numpy_recarray(G[, nodelist, dtype, order])	Return the graph adjacency matrix as a NumPy recarray.
from_numpy_matrix(A[, create_using])	Return a graph from numpy matrix.

Scipy

to_scipy_sparse_matrix(G[, nodelist, dtype, ...])	Return the graph adjacency matrix as a SciPy sparse matrix.
from_scipy_sparse_matrix(A[, create_using])	Return a graph from scipy sparse matrix adjacency list.