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Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

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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 common data containers like numpy arrays, scipy sparse matrices, and pandas DataFrames.

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 array

>>> 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.