NetworkX

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to_networkx_graph

# 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)
```

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.