Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Converting to and from other data formats

To NetworkX Graph

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 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

Functions to convert NetworkX graphs to and from numpy/scipy matrices.

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())

See Also

nx_pygraphviz, nx_pydot

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.