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
import networkx as nx
from collections.abc import Collection, Generator, Iterator

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


[docs]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) numpy matrix numpy ndarray scipy sparse matrix 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 e: raise nx.NetworkXError("Input is not a correct NetworkX graph.") from e # pygraphviz agraph if hasattr(data, "is_strict"): try: return nx.nx_agraph.from_agraph(data, create_using=create_using) except Exception as e: raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from e # 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 e: if multigraph_input is True: raise nx.NetworkXError( f"converting multigraph_input raised:\n{type(e)}: {e}" ) try: return from_dict_of_lists(data, create_using=create_using) except Exception as e: raise TypeError("Input is not known type.") from e # 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 e: msg = "Input is not a correct Pandas DataFrame adjacency matrix." raise nx.NetworkXError(msg) from e else: try: return nx.from_pandas_edgelist( data, edge_attr=True, create_using=create_using ) except Exception as e: msg = "Input is not a correct Pandas DataFrame edge-list." raise nx.NetworkXError(msg) from e except ImportError: warnings.warn("pandas not found, skipping conversion test.", ImportWarning) # numpy matrix or ndarray try: import numpy as np if isinstance(data, (np.matrix, np.ndarray)): try: return nx.from_numpy_matrix(data, create_using=create_using) except Exception as e: raise nx.NetworkXError( "Input is not a correct numpy matrix or array." ) from e except ImportError: warnings.warn("numpy not found, skipping conversion test.", ImportWarning) # scipy sparse matrix - any format try: import scipy if hasattr(data, "format"): try: return nx.from_scipy_sparse_matrix(data, create_using=create_using) except Exception as e: raise nx.NetworkXError( "Input is not a correct scipy sparse matrix type." ) from e except ImportError: warnings.warn("scipy not found, skipping conversion test.", ImportWarning) # 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 e: raise nx.NetworkXError("Input is not a valid edge list") from e raise nx.NetworkXError("Input is not a known data type for conversion.")
[docs]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
[docs]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
[docs]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
[docs]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
[docs]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)
[docs]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