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 constructor. 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)

Dictionaries¶

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

Lists¶

`to_dict_of_lists`(G[, nodelist])	Returns adjacency representation of graph as a dictionary of lists.
`from_dict_of_lists`(d[, create_using])	Returns a graph from a dictionary of lists.
`to_edgelist`(G[, nodelist])	Returns a list of edges in the graph.
`from_edgelist`(edgelist[, create_using])	Returns 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 constructor. 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 as np
>>> a = np.random.randint(0, 2, size=(10, 10))
>>> D = nx.DiGraph(a)

or equivalently

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

Scipy¶

`to_scipy_sparse_matrix`(G[, nodelist, dtype, …])	Returns the graph adjacency matrix as a SciPy sparse matrix.
`from_scipy_sparse_matrix`(A[, …])	Creates a new graph from an adjacency matrix given as a SciPy sparse matrix.

Pandas¶

`to_pandas_adjacency`(G[, nodelist, dtype, …])	Returns the graph adjacency matrix as a Pandas DataFrame.
`from_pandas_adjacency`(df[, create_using])	Returns a graph from Pandas DataFrame.
`to_pandas_edgelist`(G[, source, target, …])	Returns the graph edge list as a Pandas DataFrame.
`from_pandas_edgelist`(df[, source, target, …])	Returns a graph from Pandas DataFrame containing an edge list.

`to_numpy_matrix`(G[, nodelist, dtype, order, …])	Returns the graph adjacency matrix as a NumPy matrix.
`to_numpy_array`(G[, nodelist, dtype, order, …])	Returns the graph adjacency matrix as a NumPy array.
`to_numpy_recarray`(G[, nodelist, dtype, order])	Returns the graph adjacency matrix as a NumPy recarray.
`from_numpy_matrix`(A[, parallel_edges, …])	Returns a graph from numpy matrix.
`from_numpy_array`(A[, parallel_edges, …])	Returns a graph from NumPy array.