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[, parallel_edges, ...]) |
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[, ...]) |
Creates a new graph from an adjacency matrix given as a SciPy sparse matrix. |

## Pandas¶

`to_pandas_dataframe` (G[, nodelist, ...]) |
Return the graph adjacency matrix as a Pandas DataFrame. |

`from_pandas_dataframe` (df, source, target[, ...]) |
Return a graph from Pandas DataFrame. |