"""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",
]
[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)
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.")
[docs]
@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
[docs]
@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
[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]
@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
[docs]
@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)
[docs]
@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
