Skip to main content
`Ctrl`+`K`

# Source code for networkx.convert

    """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)

    See Also
    --------
    nx_agraph, nx_pydot
    """

    import warnings
    from collections.abc import Collection, Generator, Iterator

    import networkx as nx

    __all__ = [
        "to_networkx_graph",
        "from_dict_of_dicts",
        "to_dict_of_dicts",
        "from_dict_of_lists",
        "to_dict_of_lists",
        "from_edgelist",
        "to_edgelist",
    ]

    def to_networkx_graph(data, create_using=None, multigraph_input=False):
        """Make a NetworkX graph from a known data structure.

        The preferred way to call this is automatically
        from the class constructor

        >>> d = {0: {1: {"weight": 1}}}  # dict-of-dicts single edge (0,1)
        >>> G = nx.Graph(d)

        instead of the equivalent

        >>> G = nx.from_dict_of_dicts(d)

        Parameters
        ----------
        data : object to be converted

            Current known types are:
             any NetworkX graph
             dict-of-dicts
             dict-of-lists
             container (e.g. set, list, tuple) of edges
             iterator (e.g. itertools.chain) that produces edges
             generator of edges
             Pandas DataFrame (row per edge)
             2D numpy array
             scipy sparse array
             pygraphviz agraph

        create_using : NetworkX graph constructor, optional (default=nx.Graph)
            Graph type to create. If graph instance, then cleared before populated.

        multigraph_input : bool (default False)
            If True and  data is a dict_of_dicts,
            try to create a multigraph assuming dict_of_dict_of_lists.
            If data and create_using are both multigraphs then create
            a multigraph from a multigraph.

        """
        # NX graph
        if hasattr(data, "adj"):
            try:
                result = from_dict_of_dicts(
                    data.adj,
                    create_using=create_using,
                    multigraph_input=data.is_multigraph(),
                )
                # data.graph should be dict-like
                result.graph.update(data.graph)
                # data.nodes should be dict-like
                # result.add_node_from(data.nodes.items()) possible but
                # for custom node_attr_dict_factory which may be hashable
                # will be unexpected behavior
                for n, dd in data.nodes.items():
                    result._node[n].update(dd)
                return result
            except Exception as err:
                raise nx.NetworkXError("Input is not a correct NetworkX graph.") from err

        # dict of dicts/lists
        if isinstance(data, dict):
            try:
                return from_dict_of_dicts(
                    data, create_using=create_using, multigraph_input=multigraph_input
                )
            except Exception as err1:
                if multigraph_input is True:
                    raise nx.NetworkXError(
                        f"converting multigraph_input raised:\n{type(err1)}: {err1}"
                    )
                try:
                    return from_dict_of_lists(data, create_using=create_using)
                except Exception as err2:
                    raise TypeError("Input is not known type.") from err2

        # edgelists
        if isinstance(data, list | tuple | nx.reportviews.EdgeViewABC | Iterator):
            try:
                return from_edgelist(data, create_using=create_using)
            except:
                pass

        # pygraphviz  agraph
        if hasattr(data, "is_strict"):
            try:
                return nx.nx_agraph.from_agraph(data, create_using=create_using)
            except Exception as err:
                raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from err

        # Pandas DataFrame
        try:
            import pandas as pd

            if isinstance(data, pd.DataFrame):
                if data.shape[0] == data.shape[1]:
                    try:
                        return nx.from_pandas_adjacency(data, create_using=create_using)
                    except Exception as err:
                        msg = "Input is not a correct Pandas DataFrame adjacency matrix."
                        raise nx.NetworkXError(msg) from err
                else:
                    try:
                        return nx.from_pandas_edgelist(
                            data, edge_attr=True, create_using=create_using
                        )
                    except Exception as err:
                        msg = "Input is not a correct Pandas DataFrame edge-list."
                        raise nx.NetworkXError(msg) from err
        except ImportError:
            pass

        # numpy array
        try:
            import numpy as np

            if isinstance(data, np.ndarray):
                try:
                    return nx.from_numpy_array(data, create_using=create_using)
                except Exception as err:
                    raise nx.NetworkXError(
                        f"Failed to interpret array as an adjacency matrix."
                    ) from err
        except ImportError:
            pass

        # scipy sparse array - any format
        try:
            import scipy

            if hasattr(data, "format"):
                try:
                    return nx.from_scipy_sparse_array(data, create_using=create_using)
                except Exception as err:
                    raise nx.NetworkXError(
                        "Input is not a correct scipy sparse array type."
                    ) from err
        except ImportError:
            pass

        # Note: most general check - should remain last in order of execution
        # Includes containers (e.g. list, set, dict, etc.), generators, and
        # iterators (e.g. itertools.chain) of edges

        if isinstance(data, Collection | Generator | Iterator):
            try:
                return from_edgelist(data, create_using=create_using)
            except Exception as err:
                raise nx.NetworkXError("Input is not a valid edge list") from err

        raise nx.NetworkXError("Input is not a known data type for conversion.")

    @nx._dispatchable
    def to_dict_of_lists(G, nodelist=None):
        """Returns adjacency representation of graph as a dictionary of lists.

        Parameters
        ----------
        G : graph
           A NetworkX graph

        nodelist : list
           Use only nodes specified in nodelist

        Notes
        -----
        Completely ignores edge data for MultiGraph and MultiDiGraph.

        """
        if nodelist is None:
            nodelist = G

        d = {}
        for n in nodelist:
            d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
        return d

    @nx._dispatchable(graphs=None, returns_graph=True)
    def from_dict_of_lists(d, create_using=None):
        """Returns a graph from a dictionary of lists.

        Parameters
        ----------
        d : dictionary of lists
          A dictionary of lists adjacency representation.

        create_using : NetworkX graph constructor, optional (default=nx.Graph)
            Graph type to create. If graph instance, then cleared before populated.

        Examples
        --------
        >>> dol = {0: [1]}  # single edge (0,1)
        >>> G = nx.from_dict_of_lists(dol)

        or

        >>> G = nx.Graph(dol)  # use Graph constructor

        """
        G = nx.empty_graph(0, create_using)
        G.add_nodes_from(d)
        if G.is_multigraph() and not G.is_directed():
            # a dict_of_lists can't show multiedges.  BUT for undirected graphs,
            # each edge shows up twice in the dict_of_lists.
            # So we need to treat this case separately.
            seen = {}
            for node, nbrlist in d.items():
                for nbr in nbrlist:
                    if nbr not in seen:
                        G.add_edge(node, nbr)
                seen[node] = 1  # don't allow reverse edge to show up
        else:
            G.add_edges_from(
                ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
            )
        return G

    def to_dict_of_dicts(G, nodelist=None, edge_data=None):
        """Returns adjacency representation of graph as a dictionary of dictionaries.

        Parameters
        ----------
        G : graph
           A NetworkX graph

        nodelist : list
           Use only nodes specified in nodelist

        edge_data : scalar, optional
           If provided, the value of the dictionary will be set to `edge_data` for
           all edges. Usual values could be `1` or `True`. If `edge_data` is
           `None` (the default), the edgedata in `G` is used, resulting in a
           dict-of-dict-of-dicts. If `G` is a MultiGraph, the result will be a
           dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize
           handling edge data. `edge_data` should *not* be a container.

        Returns
        -------
        dod : dict
           A nested dictionary representation of `G`. Note that the level of
           nesting depends on the type of `G` and the value of `edge_data`
           (see Examples).

        See Also
        --------
        from_dict_of_dicts, to_dict_of_lists

        Notes
        -----
        For a more custom approach to handling edge data, try::

            dod = {
                n: {nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()}
                for n, nbrdict in G.adj.items()
            }

        where `custom` returns the desired edge data for each edge between `n` and
        `nbr`, given existing edge data `dd`.

        Examples
        --------
        >>> G = nx.path_graph(3)
        >>> nx.to_dict_of_dicts(G)
        {0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}}

        Edge data is preserved by default (``edge_data=None``), resulting
        in dict-of-dict-of-dicts where the innermost dictionary contains the
        edge data:

        >>> G = nx.Graph()
        >>> G.add_edges_from(
        ...     [
        ...         (0, 1, {"weight": 1.0}),
        ...         (1, 2, {"weight": 2.0}),
        ...         (2, 0, {"weight": 1.0}),
        ...     ]
        ... )
        >>> d = nx.to_dict_of_dicts(G)
        >>> d  # doctest: +SKIP
        {0: {1: {'weight': 1.0}, 2: {'weight': 1.0}},
         1: {0: {'weight': 1.0}, 2: {'weight': 2.0}},
         2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}}
        >>> d[1][2]["weight"]
        2.0

        If `edge_data` is not `None`, edge data in the original graph (if any) is
        replaced:

        >>> d = nx.to_dict_of_dicts(G, edge_data=1)
        >>> d
        {0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}}
        >>> d[1][2]
        1

        This also applies to MultiGraphs: edge data is preserved by default:

        >>> G = nx.MultiGraph()
        >>> G.add_edge(0, 1, key="a", weight=1.0)
        'a'
        >>> G.add_edge(0, 1, key="b", weight=5.0)
        'b'
        >>> d = nx.to_dict_of_dicts(G)
        >>> d  # doctest: +SKIP
        {0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}},
         1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}}
        >>> d[0][1]["b"]["weight"]
        5.0

        But multi edge data is lost if `edge_data` is not `None`:

        >>> d = nx.to_dict_of_dicts(G, edge_data=10)
        >>> d
        {0: {1: 10}, 1: {0: 10}}
        """
        dod = {}
        if nodelist is None:
            if edge_data is None:
                for u, nbrdict in G.adjacency():
                    dod[u] = nbrdict.copy()
            else:  # edge_data is not None
                for u, nbrdict in G.adjacency():
                    dod[u] = dod.fromkeys(nbrdict, edge_data)
        else:  # nodelist is not None
            if edge_data is None:
                for u in nodelist:
                    dod[u] = {}
                    for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
                        dod[u][v] = data
            else:  # nodelist and edge_data are not None
                for u in nodelist:
                    dod[u] = {}
                    for v in (v for v in G[u] if v in nodelist):
                        dod[u][v] = edge_data
        return dod

    @nx._dispatchable(graphs=None, returns_graph=True)
    def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
        """Returns a graph from a dictionary of dictionaries.

        Parameters
        ----------
        d : dictionary of dictionaries
          A dictionary of dictionaries adjacency representation.

        create_using : NetworkX graph constructor, optional (default=nx.Graph)
            Graph type to create. If graph instance, then cleared before populated.

        multigraph_input : bool (default False)
           When True, the dict `d` is assumed
           to be a dict-of-dict-of-dict-of-dict structure keyed by
           node to neighbor to edge keys to edge data for multi-edges.
           Otherwise this routine assumes dict-of-dict-of-dict keyed by
           node to neighbor to edge data.

        Examples
        --------
        >>> dod = {0: {1: {"weight": 1}}}  # single edge (0,1)
        >>> G = nx.from_dict_of_dicts(dod)

        or

        >>> G = nx.Graph(dod)  # use Graph constructor

        """
        G = nx.empty_graph(0, create_using)
        G.add_nodes_from(d)
        # does dict d represent a MultiGraph or MultiDiGraph?
        if multigraph_input:
            if G.is_directed():
                if G.is_multigraph():
                    G.add_edges_from(
                        (u, v, key, data)
                        for u, nbrs in d.items()
                        for v, datadict in nbrs.items()
                        for key, data in datadict.items()
                    )
                else:
                    G.add_edges_from(
                        (u, v, data)
                        for u, nbrs in d.items()
                        for v, datadict in nbrs.items()
                        for key, data in datadict.items()
                    )
            else:  # Undirected
                if G.is_multigraph():
                    seen = set()  # don't add both directions of undirected graph
                    for u, nbrs in d.items():
                        for v, datadict in nbrs.items():
                            if (u, v) not in seen:
                                G.add_edges_from(
                                    (u, v, key, data) for key, data in datadict.items()
                                )
                                seen.add((v, u))
                else:
                    seen = set()  # don't add both directions of undirected graph
                    for u, nbrs in d.items():
                        for v, datadict in nbrs.items():
                            if (u, v) not in seen:
                                G.add_edges_from(
                                    (u, v, data) for key, data in datadict.items()
                                )
                                seen.add((v, u))

        else:  # not a multigraph to multigraph transfer
            if G.is_multigraph() and not G.is_directed():
                # d can have both representations u-v, v-u in dict.  Only add one.
                # We don't need this check for digraphs since we add both directions,
                # or for Graph() since it is done implicitly (parallel edges not allowed)
                seen = set()
                for u, nbrs in d.items():
                    for v, data in nbrs.items():
                        if (u, v) not in seen:
                            G.add_edge(u, v, key=0)
                            G[u][v][0].update(data)
                        seen.add((v, u))
            else:
                G.add_edges_from(
                    ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
                )
        return G

    @nx._dispatchable(preserve_edge_attrs=True)
    def to_edgelist(G, nodelist=None):
        """Returns a list of edges in the graph.

        Parameters
        ----------
        G : graph
           A NetworkX graph

        nodelist : list
           Use only nodes specified in nodelist

        """
        if nodelist is None:
            return G.edges(data=True)
        return G.edges(nodelist, data=True)

    @nx._dispatchable(graphs=None, returns_graph=True)
    def from_edgelist(edgelist, create_using=None):
        """Returns a graph from a list of edges.

        Parameters
        ----------
        edgelist : list or iterator
          Edge tuples

        create_using : NetworkX graph constructor, optional (default=nx.Graph)
            Graph type to create. If graph instance, then cleared before populated.

        Examples
        --------
        >>> edgelist = [(0, 1)]  # single edge (0,1)
        >>> G = nx.from_edgelist(edgelist)

        or

        >>> G = nx.Graph(edgelist)  # use Graph constructor

        """
        G = nx.empty_graph(0, create_using)
        G.add_edges_from(edgelist)
        return G