Graph types#
Class |
Type |
Self-loops allowed |
Parallel edges allowed |
---|---|---|---|
Graph |
undirected |
Yes |
No |
DiGraph |
directed |
Yes |
No |
Notice that graphscope.nx not support MultiGraph and MultiDiGraph.
Graph#
Undirected graphs with self loops
- class graphscope.nx.Graph(incoming_graph_data=None, default_label=None, **attr)[source]#
Base class for undirected graphs.
A Graph that holds the metadata of a graph, and provides NetworkX-like Graph APIs.
It is worth noticing that the graph is actually stored by the Analytical Engine backend. In other words, the Graph object holds nothing but metadata of a graph.
Graph support nodes and edges with optional data, or attributes.
Graphs support undirected edges. Self loops are allowed but multiple (parallel) edges are not.
Nodes can be arbitrary int/str/float/bool objects with optional key/value attributes.
Edges are represented as links between nodes with optional key/value attributes.
Graph support node label if it’s created from a GraphScope graph object. nodes are identified by (label, id) tuple.
- Parameters:
incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be any format that is supported by the to_networkx_graph() function, currently including edge list, dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy sparse matrix or a GraphScope graph object.
default_label (default node label (optional, default: None)) – if incoming_graph_data is a GraphScope graph object, default label means the nodes of the label can be identified by id directly, other label nodes need to use (label, id) to identify.
attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.
See also
Examples
Create an empty graph structure (a “null graph”) with no nodes and no edges.
>>> G = nx.Graph()
G can be grown in several ways.
Nodes:
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3]) >>> G.add_nodes_from(range(100, 110)) >>> H = nx.path_graph(10) >>> G.add_nodes_from(H)
In addition to integers, strings/floats/bool can represent a node too.
>>> G.add_node('a node') >>> G.add_node(3.14) >>> G.add_node(True)
Edges:
G can also be grown by adding edges.
Add one edge,
>>> G.add_edge(1, 2)
a list of edges,
>>> G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes are added automatically. There are no errors when adding nodes or edges that already exist.
Attributes:
Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be string). By default these are empty, but can be added or changed using add_edge, add_node or direct manipulation of the attribute dictionaries named graph, node and edge respectively.
>>> G = nx.Graph(day="Friday") >>> G.graph {'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time='5pm') >>> G.add_nodes_from([3], time='2pm') >>> G.nodes[1] {'time': '5pm'} >>> G.nodes[1]['room'] = 714 # node must exist already to use G.nodes >>> del G.nodes[1]['room'] # remove attribute >>> list(G.nodes(data=True)) [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript notation, or G.edges.
>>> G.add_edge(1, 2, weight=4.7 ) >>> G.add_edges_from([(3, 4), (4, 5)], color='red') >>> G.add_edges_from([(1, 2, {'color': 'blue'}), (2, 3, {'weight': 8})]) >>> G[1][2]['weight'] = 4.7 >>> G.edges[1, 2]['weight'] = 4
Warning: we protect the graph data structure by making G.edges a read-only dict-like structure. However, you can assign to attributes in e.g. G.edges[1, 2]. Thus, use 2 sets of brackets to add/change data attributes: G.edges[1, 2][‘weight’] = 4
Shortcuts:
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph True >>> [n for n in G if n < 3] # iterate through nodes [1, 2] >>> len(G) # number of nodes in graph 5
Often the best way to traverse all edges of a graph is via the neighbors. The neighbors are reported as an adjacency-dict G.adj or G.adjacency()
>>> for n, nbrsdict in G.adjacency(): ... for nbr, eattr in nbrsdict.items(): ... if 'weight' in eattr: ... # Do something useful with the edges ... pass
But the edges() method is often more convenient:
>>> for u, v, weight in G.edges.data('weight'): ... if weight is not None: ... # Do something useful with the edges ... pass
Transformation
Create a graph with GraphScope graph object. First we init a GraphScope graph with two node labels: person and comment`
>>> g = graphscope.g(directed=False).add_vertice("persion.csv", label="person").add_vertice("comment.csv", label="comment")
create a graph with g, set default_label to ‘person’
>>> G = nx.Graph(g, default_label="person")
person label nodes can be identified by id directly, for comment label, we has to use tuple (“comment”, id) identify. Like, add a person label node and a comment label node
>>> G.add_node(0, type="person") >>> G.add_node(("comment", 0), type="comment")
print property of two nodes
>>> G.nodes[0] {"type", "person"} >>> G.nodes[("comment", 0)] {"type", "comment"}
Reporting:
Simple graph information is obtained using object-attributes and methods. Reporting typically provides views instead of containers to reduce memory usage. The views update as the graph is updated similarly to dict-views. The objects nodes, `edges and adj provide access to data attributes via lookup (e.g. nodes[n], `edges[u, v], adj[u][v]) and iteration (e.g. nodes.items(), nodes.data(‘color’), nodes.data(‘color’, default=’blue’) and similarly for edges) Views exist for nodes, edges, neighbors()/adj and degree.
For details on these and other miscellaneous methods, see below.
- __contains__(n)[source]#
Returns True if n is a node, False otherwise. Use: ‘n in G’.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> 1 in G True
- __getitem__(n)[source]#
Returns a dict of neighbors of node n. Use: ‘G[n]’.
- Parameters:
n (node) – A node in the graph.
- Returns:
adj_dict – The adjacency dictionary for nodes connected to n.
- Return type:
dictionary
Notes
G[n] is the same as G.adj[n] and similar to G.neighbors(n) (which is an iterator over G.adj[n])
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G[0] AtlasView({1: {}})
- __init__(incoming_graph_data=None, default_label=None, **attr)[source]#
Initialize a graph with graph, edges, name, or graph attributes
- Parameters:
incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, any NetworkX graph object or any GraphScope graph object. If the corresponding optional Python packages are installed the data can also be a 2D NumPy array, a SciPy sparse matrix
default_label (default node label (optional, default: "_")) – if incoming_graph_data is a GraphScope graph object, default label means the nodes of the label can be accessed by id directly, other label nodes need to use (label, id) to access.
attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.
See also
convert
Examples
>>> G = nx.Graph() # or DiGraph >>> G = nx.Graph(name='my graph') >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges >>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday") >>> G.graph {'day': 'Friday'}
Created from a GraphScope graph object
>>> g = graphscope.g(directed=False) # if transform to DiGraph, directed=True >>> g.add_vertices("person.csv", label="person").add_vertices("comment.csv", label="comment").add_edges(...) >>> G = nx.Graph(g, default_label="person") # or DiGraph
- __iter__()[source]#
Iterate over the nodes. Use: ‘for n in G’.
- Returns:
niter – An iterator over all nodes in the graph.
- Return type:
iterator
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> [n for n in G] [0, 1, 2, 3] >>> list(G) [0, 1, 2, 3]
- __len__()[source]#
Returns the number of nodes in the graph. Use: ‘len(G)’.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
See also
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> len(G) 4
- add_edge(u_of_edge, v_of_edge, **attr)[source]#
Add an edge between u and v.
The nodes u and v will be automatically added if they are not already in the graph.
Edge attributes can be specified with keywords or by directly accessing the edge’s attribute dictionary. See examples below.
- Parameters:
u (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int/string/float/tuple/bool hashable Python objects.
v (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int/string/float/tuple/bool hashable Python objects.
attr (keyword arguments, optional) – Edge data can be assigned using keyword arguments.
See also
add_edges_from
add a collection of edges
Notes
Adding an edge that already exists updates the edge data.
Many networkx algorithms designed for weighted graphs use an edge attribute (by default weight) to hold a numerical value.
Examples
The following all add the edge e=(1, 2) to graph G:
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> e = (1, 2) >>> G.add_edge(1, 2) # explicit two-node form >>> G.add_edge(*e) # single edge as tuple of two nodes >>> G.add_edges_from([(1, 2)]) # add edges from iterable container
Associate data to edges using keywords:
>>> G.add_edge(1, 2, weight=3) >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> G.add_edge(1, 2) >>> G[1][2].update({0: 5}) >>> G.edges[1, 2].update({0: 5})
- add_edges_from(ebunch_to_add, **attr)[source]#
Add all the edges in ebunch_to_add.
- Parameters:
ebunch_to_add (container of edges) – Each edge given in the container will be added to the graph. The edges must be given as as 2-tuples (u, v) or 3-tuples (u, v, d) where d is a dictionary containing edge data.
attr (keyword arguments, optional) – Edge data can be assigned using keyword arguments.
See also
add_edge
add a single edge
add_weighted_edges_from
convenient way to add weighted edges
Notes
Adding the same edge twice has no effect but any edge data will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over attributes specified via keyword arguments.
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples >>> e = zip(range(0, 3), range(1, 4)) >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3) >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
- add_node(node_for_adding, **attr)[source]#
Add a single node node_for_adding and update node attributes.
- Parameters:
node_for_adding (node) – A node can be int, float, str, tuple or bool object.
attr (keyword arguments, optional) – Set or change node attributes using key=value.
See also
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_node(1) >>> G.add_node(2) >>> G.number_of_nodes() 2
Use keywords set/change node attributes:
>>> G.add_node(1, size=10) >>> G.add_node(3, weight=0.4, type='apple')
Notes
nx.Graph support int, float, str, tuple or bool object of nodes.
- add_nodes_from(nodes_for_adding, **attr)[source]#
Add multiple nodes.
- Parameters:
nodes_for_adding (iterable container) – A container of nodes (list, dict, set, etc.). OR A container of (node, attribute dict) tuples. Node attributes are updated using the attribute dict.
attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. Node attributes specified in nodes as a tuple take precedence over attributes specified via keyword arguments.
See also
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_nodes_from("Hello") >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)]) >>> G.add_nodes_from(K3) >>> sorted(G.nodes(), key=str) [0, 1, 2, 'H', 'e', 'l', 'o']
Use keywords to update specific node attributes for every node.
>>> G.add_nodes_from([1, 2], size=10) >>> G.add_nodes_from([3, 4], weight=0.4)
Use (node, attrdict) tuples to update attributes for specific nodes.
>>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})]) >>> G.nodes[1]["size"] 11 >>> H = nx.Graph() >>> H.add_nodes_from(G.nodes(data=True)) >>> H.nodes[1]["size"] 11
- add_weighted_edges_from(ebunch_to_add, weight='weight', **attr)[source]#
Add weighted edges in ebunch_to_add with specified weight attr
- Parameters:
ebunch_to_add (container of edges) – Each edge given in the list or container will be added to the graph. The edges must be given as 3-tuples (u, v, w) where w is a number.
weight (string, optional (default= 'weight')) – The attribute name for the edge weights to be added.
attr (keyword arguments, optional (default= no attributes)) – Edge attributes to add/update for all edges.
See also
add_edge
add a single edge
add_edges_from
add multiple edges
Notes
Adding the same edge twice for Graph/DiGraph simply updates the edge data.
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
- property adj#
Graph adjacency object holding the neighbors of each node.
This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So G.adj[3][2][‘color’] = ‘blue’ sets the color of the edge (3, 2) to “blue”.
Iterating over G.adj behaves like a dict. Useful idioms include for nbr, datadict in G.adj[n].items():.
The neighbor information is also provided by subscripting the graph. So for nbr, foovalue in G[node].data(‘foo’, default=1): works.
For directed graphs, G.adj holds outgoing (successor) info.
- adjacency()[source]#
Returns an iterator over (node, adjacency dict) tuples for all nodes.
For directed graphs, only outgoing neighbors/adjacencies are included.
- Returns:
adj_iter – An iterator over (node, adjacency dictionary) for all nodes in the graph.
- Return type:
iterator
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> [(n, nbrdict) for n, nbrdict in G.adjacency()] [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
- adjlist_inner_dict_factory#
alias of
NeighborDict
- adjlist_outer_dict_factory#
alias of
AdjListDict
- clear()[source]#
Remove all nodes and edges from the graph.
This also removes the name, and all graph, node, and edge attributes.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.clear() >>> list(G.nodes) [] >>> list(G.edges) []
- clear_edges()[source]#
Remove all edges from the graph without altering nodes.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.clear_edges() >>> list(G.nodes) [0, 1, 2, 3] >>> list(G.edges) []
- copy(as_view=False)[source]#
Returns a copy of the graph.
The copy method by default returns an independent deep copy of the graph and attributes.
If as_view is True then a view is returned instead of a copy.
Notes
All copies reproduce the graph structure, but data attributes may be handled in different ways. There are three types of copies of a graph that people might want.
Deepcopy – A “deepcopy” copies the graph structure as well as all data attributes and any objects they might contain in Engine backend. The entire graph object is new so that changes in the copy do not affect the original object.
Fresh Data – For fresh data, the graph structure is copied while new empty data attribute dicts are created. The resulting graph is independent of the original and it has no edge, node or graph attributes. Fresh copies are not enabled. Instead use:
>>> H = G.__class__() >>> H.add_nodes_from(G) >>> H.add_edges_from(G.edges)
View – Inspired by dict-views, graph-views act like read-only versions of the original graph, providing a copy of the original structure without requiring any memory for copying the information.
- Parameters:
as_view (bool, optional (default=False)) – If True, the returned graph-view provides a read-only view of the original graph without actually copying any data.
- Returns:
G – A copy of the graph.
- Return type:
See also
to_directed
return a directed copy of the graph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> H = G.copy()
- property degree#
A DegreeView for the Graph as G.degree or G.degree().
The node degree is the number of edges adjacent to the node. The weighted node degree is the sum of the edge weights for edges incident to that node.
This object provides an iterator for (node, degree) as well as lookup for the degree for a single node.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
weight (string or None, optional (default=None)) – The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.
- Returns:
If a single node is requested
deg (int) – Degree of the node
OR if multiple nodes are requested
nd_view (A DegreeView object capable of iterating (node, degree) pairs)
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.degree[0] # node 0 has degree 1 1 >>> list(G.degree([0, 1, 2])) [(0, 1), (1, 2), (2, 2)]
- edge_attr_dict_factory#
alias of
NeighborAttrDict
- edge_subgraph(edges)[source]#
Returns a independent deep copy subgraph induced by the specified edges.
The induced subgraph contains each edge in edges and each node incident to any one of those edges.
- Parameters:
edges (iterable) – An iterable of edges in this graph.
- Returns:
G – An edge-induced subgraph of this graph with the same edge attributes.
- Return type:
Notes
Unlike NetworkX return a view, here return a independent deep copy subgraph.
Examples
>>> G = nx.path_graph(5) # or DiGraph >>> H = G.edge_subgraph([(0, 1), (3, 4)]) >>> list(H.nodes) [0, 1, 3, 4] >>> list(H.edges) [(0, 1), (3, 4)]
- property edges#
An EdgeView of the Graph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The EdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, G.edges[u, v][‘color’] provides the value of the color attribute for edge (u, v) while for (u, v, c) in G.edges.data(‘color’, default=’red’): iterates through all the edges yielding the color attribute with default ‘red’ if no color attribute exists.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
data (string or bool, optional (default=False)) – The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v).
default (value, optional (default=None)) – Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
edges – A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v][‘foo’].
- Return type:
EdgeView
Notes
Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.add_edge(2, 3, weight=5) >>> [e for e in G.edges] [(0, 1), (1, 2), (2, 3)] >>> G.edges.data() # default data is {} (empty dict) EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]) >>> G.edges.data("weight", default=1) EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]) >>> G.edges([0, 3]) # only edges incident to these nodes EdgeDataView([(0, 1), (3, 2)]) >>> G.edges(0) # only edges incident to a single node (use G.adj[0]?) EdgeDataView([(0, 1)])
- get_edge_data(u, v, default=None)[source]#
Returns the attribute dictionary associated with edge (u, v).
This is identical to G[u][v] except the default is returned instead of an exception if the edge doesn’t exist.
- Parameters:
u (nodes) –
v (nodes) –
default (any Python object (default=None)) – Value to return if the edge (u, v) is not found.
- Returns:
edge_dict – The edge attribute dictionary.
- Return type:
dictionary
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G[0][1] {}
Warning: Assigning to G[u][v] is not permitted. But it is safe to assign attributes G[u][v][‘foo’]
>>> G[0][1]["weight"] = 7 >>> G[0][1]["weight"] 7 >>> G[1][0]["weight"] 7
>>> G = nx.path_graph(4) # or DiGraph >>> G.get_edge_data(0, 1) # default edge data is {} {} >>> e = (0, 1) >>> G.get_edge_data(*e) # tuple form {} >>> G.get_edge_data("a", "b", default=0) # edge not in graph, return 0 0
- graph_attr_dict_factory#
alias of
dict
- graph_cache_factory#
alias of
Cache
- property graph_type#
The type of the graph object.
- Returns:
the type of the graph.
- Return type:
type (types_pb2.GraphType)
- has_edge(u, v)[source]#
Returns True if the edge (u, v) is in the graph.
This is the same as v in G[u] without KeyError exceptions.
- Parameters:
u (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int, str, float, tuple, bool hashable Python objects.
v (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int, str, float, tuple, bool hashable Python objects.
- Returns:
edge_ind – True if edge is in the graph, False otherwise.
- Return type:
bool
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.has_edge(0, 1) # using two nodes True >>> e = (0, 1) >>> G.has_edge(*e) # e is a 2-tuple (u, v) True >>> e = (0, 1, {"weight": 7}) >>> G.has_edge(*e[:2]) # e is a 3-tuple (u, v, data_dictionary) True
The following syntax are equivalent:
>>> G.has_edge(0, 1) True >>> 1 in G[0] # though this gives KeyError if 0 not in G True
- has_node(n)[source]#
Returns True if the graph contains the node n.
Identical to n in G
- Parameters:
n (node) –
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.has_node(0) True
It is more readable and simpler to use
>>> 0 in G True
- property key#
Key of the coresponding engine graph.
- property name#
String identifier of the graph.
This graph attribute appears in the attribute dict G.graph keyed by the string “name”. as well as an attribute (technically a property) G.name. This is entirely user controlled.
- nbunch_iter(nbunch=None)[source]#
Returns an iterator over nodes contained in nbunch that are also in the graph.
The nodes in nbunch are checked for membership in the graph and if not are silently ignored.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
- Returns:
niter – An iterator over nodes in nbunch that are also in the graph. If nbunch is None, iterate over all nodes in the graph.
- Return type:
iterator
- Raises:
NetworkXError – If nbunch is not a node or sequence of nodes. If a node in nbunch is not hashable.
See also
Notes
When nbunch is an iterator, the returned iterator yields values directly from nbunch, becoming exhausted when nbunch is exhausted.
To test whether nbunch is a single node, one can use “if nbunch in self:”, even after processing with this routine.
If nbunch is not a node or a (possibly empty) sequence/iterator or None, a
NetworkXError
is raised. Also, if any object in nbunch is not hashable, aNetworkXError
is raised.
- neighbors(n)[source]#
Returns an iterator over all neighbors of node n.
This is identical to iter(G[n])
- Parameters:
n (node) – A node in the graph
- Returns:
neighbors – An iterator over all neighbors of node n
- Return type:
iterator
- Raises:
NetworkXError – If the node n is not in the graph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> [n for n in G.neighbors(0)] [1]
Notes
Alternate ways to access the neighbors are
G.adj[n]
orG[n]
:>>> G = nx.Graph() # or DiGraph >>> G.add_edge("a", "b", weight=7) >>> G["a"] AtlasView({'b': {'weight': 7}}) >>> G = nx.path_graph(4) >>> [n for n in G[0]] [1]
- node_attr_dict_factory#
alias of
NodeAttrDict
- node_dict_factory#
alias of
NodeDict
- property nodes#
A NodeView of the Graph as G.nodes or G.nodes().
Can be used as G.nodes for data lookup and for set-like operations. Can also be used as G.nodes(data=’color’, default=None) to return a NodeDataView which reports specific node data but no set operations. It presents a dict-like interface as well with G.nodes.items() iterating over (node, nodedata) 2-tuples and G.nodes[3][‘foo’] providing the value of the foo attribute for node 3. In addition, a view G.nodes.data(‘foo’) provides a dict-like interface to the foo attribute of each node. G.nodes.data(‘foo’, default=1) provides a default for nodes that do not have attribute foo.
- Parameters:
data (string or bool, optional (default=False)) – The node attribute returned in 2-tuple (n, ddict[data]). If True, return entire node attribute dict as (n, ddict). If False, return just the nodes n.
default (value, optional (default=None)) – Value used for nodes that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
Allows set-like operations over the nodes as well as node attribute dict lookup and calling to get a NodeDataView. A NodeDataView iterates over (n, data) and has no set operations. A NodeView iterates over n and includes set operations.
When called, if data is False, an iterator over nodes. Otherwise an iterator of 2-tuples (node, attribute value) where the attribute is specified in data. If data is True then the attribute becomes the entire data dictionary.
- Return type:
NodeView
Notes
If your node data is not needed, it is simpler and equivalent to use the expression
for n in G
, orlist(G)
.Examples
There are two simple ways of getting a list of all nodes in the graph:
>>> G = nx.path_graph(3) >>> list(G.nodes) [0, 1, 2] >>> list(G) [0, 1, 2]
To get the node data along with the nodes:
>>> G.add_node(1, time="5pm") >>> G.nodes[0]["foo"] = "bar" >>> list(G.nodes(data=True)) [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})] >>> list(G.nodes.data()) [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
>>> list(G.nodes(data="foo")) [(0, 'bar'), (1, None), (2, None)] >>> list(G.nodes.data("foo")) [(0, 'bar'), (1, None), (2, None)]
>>> list(G.nodes(data="time")) [(0, None), (1, '5pm'), (2, None)] >>> list(G.nodes.data("time")) [(0, None), (1, '5pm'), (2, None)]
>>> list(G.nodes(data="time", default="Not Available")) [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')] >>> list(G.nodes.data("time", default="Not Available")) [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
If some of your nodes have an attribute and the rest are assumed to have a default attribute value you can create a dictionary from node/attribute pairs using the default keyword argument to guarantee the value is never None:
>>> G = nx.Graph() >>> G.add_node(0) >>> G.add_node(1, weight=2) >>> G.add_node(2, weight=3) >>> dict(G.nodes(data="weight", default=1)) {0: 1, 1: 2, 2: 3}
- number_of_edges(u=None, v=None)[source]#
Returns the number of edges between two nodes.
- Parameters:
u (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.
v (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.
- Returns:
nedges – The number of edges in the graph. If nodes u and v are specified return the number of edges between those nodes. If the graph is directed, this only returns the number of edges from u to v.
- Return type:
int
See also
Examples
For undirected graphs, this method counts the total number of edges in the graph:
>>> G = nx.path_graph(4) >>> G.number_of_edges() 3
If you specify two nodes, this counts the total number of edges joining the two nodes:
>>> G.number_of_edges(0, 1) 1
For directed graphs, this method can count the total number of directed edges from u to v:
>>> G = nx.DiGraph() >>> G.add_edge(0, 1) >>> G.add_edge(1, 0) >>> G.number_of_edges(0, 1) 1
- number_of_nodes()[source]#
Returns the number of nodes in the graph.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.number_of_nodes() 3
- property op#
The DAG op of this graph.
- order()[source]#
Returns the number of nodes in the graph.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.order() 3
- remove_edge(u, v)[source]#
Remove the edge between u and v.
- Parameters:
u (nodes) – Remove the edge between nodes u and v.
v (nodes) – Remove the edge between nodes u and v.
- Raises:
NetworkXError – If there is not an edge between u and v.
See also
remove_edges_from
remove a collection of edges
Examples
>>> G = nx.path_graph(4) # or DiGraph, etc >>> G.remove_edge(0, 1) >>> e = (1, 2) >>> G.remove_edge(*e) # unpacks e from an edge tuple >>> e = (2, 3, {"weight": 7}) # an edge with attribute data >>> G.remove_edge(*e[:2]) # select first part of edge tuple
- remove_edges_from(ebunch)[source]#
Remove all edges specified in ebunch.
- Parameters:
ebunch (list or container of edge tuples) –
Each edge given in the list or container will be removed from the graph. The edges can be:
2-tuples (u, v) edge between u and v.
3-tuples (u, v, k) where k is ignored.
See also
remove_edge
remove a single edge
Notes
Will fail silently if an edge in ebunch is not in the graph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> ebunch = [(1, 2), (2, 3)] >>> G.remove_edges_from(ebunch)
- remove_node(n)[source]#
Remove node n.
Removes the node n and all adjacent edges. Attempting to remove a non-existent node will raise an exception.
- Parameters:
n (node) – A node in the graph
- Raises:
NetworkXError – If n is not in the graph.
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> list(G.edges) [(0, 1), (1, 2)] >>> G.remove_node(1) >>> list(G.edges) []
- remove_nodes_from(nodes_for_removing)[source]#
Remove multiple nodes.
- Parameters:
nodes_for_removing (iterable container) – A container of nodes (list, dict, set, etc.). If a node in the container is not in the graph it is silently ignored.
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> e = list(G.nodes) >>> e [0, 1, 2] >>> G.remove_nodes_from(e) >>> list(G.nodes) []
- property schema#
Schema of the graph.
- Returns:
the schema of the graph
- Return type:
GraphSchema
- property session#
Get the session of graph.
- Returns:
Return session that the graph belongs to.
- property session_id#
Get session’s id of graph.
- Returns:
Return session id that the graph belongs to.
- Return type:
str
- set_edge_data(u, v, data)[source]#
Set edge data of edge (u, v).
- Parameters:
u (nodes) – Nodes can be int, str, float, tuple, bool hashable Python objects.
v (nodes) – Nodes can be int, str, float, tuple, bool hashable Python objects.
data (dict) – Edge data to set to edge (u, v)
See also
set_node_data
set node data of node
Notes
the method is called when to set_items in AdjEdgeAttr
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_edge(1, 2) >>> dd = {'foo': 'bar'} >>> G[1][2] = dd # call G.set_edge_data(1, 2, dd) >>> G[1][2] {'foo': 'bar'}
- set_node_data(n, data)[source]#
Set data of node.
- Parameters:
n (node) – node can be int, str, float, tuple, bool hashable Python object which is existed in graph.
data (dict) – data to set to n
See also
set_edge_data
set data of edge
Notes
the method is called when to set_items in NodeAttr
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_node(1) >>> dd = {'weight': 3} >>> G.nodes[1] = dd # call G.set_node_data(1, dd) >>> G.nodes[1] {'weight': 3}
- property signature#
Generate a signature of the current graph
- size(weight=None)[source]#
Returns the number of edges or total of all edge weights.
- Parameters:
weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1.
- Returns:
size – The number of edges or (if weight keyword is provided) the total weight sum.
If weight is None, returns an int. Otherwise a float (or more general numeric if the weights are more general).
- Return type:
numeric
See also
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.size() 3
>>> G = nx.Graph() # or DiGraph >>> G.add_edge("a", "b", weight=2) >>> G.add_edge("b", "c", weight=4) >>> G.size() 2 >>> G.size(weight="weight") 6.0
- subgraph(nodes)[source]#
Returns a independent deep copy subgraph induced on nodes.
The induced subgraph of the graph contains the nodes in nodes and the edges between those nodes.
- Parameters:
nodes (list, iterable) – A container of nodes which will be iterated through once.
- Returns:
G – A subgraph of the graph.
- Return type:
Notes
Unlike NetowrkX return a view, here return a independent deep copy subgraph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> H = G.subgraph([0, 1, 2]) >>> list(H.edges) [(0, 1), (1, 2)]
- to_directed(as_view=False)[source]#
Returns a directed representation of the graph.
- Parameters:
as_view (bool, optional (default=False)) – If True return a view of the original directed graph.
- Returns:
G – A directed graph with the same name, same nodes, and with each edge (u, v, data) replaced by two directed edges (u, v, data) and (v, u, data).
- Return type:
Notes
This by default returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.
Examples
>>> G = nx.Graph() >>> G.add_edge(0, 1) >>> H = G.to_directed() >>> list(H.edges) [(0, 1), (1, 0)]
If already directed, return a (deep) copy
>>> G = nx.DiGraph() >>> G.add_edge(0, 1) >>> H = G.to_directed() >>> list(H.edges) [(0, 1)]
- to_undirected(as_view=False)[source]#
Returns an undirected copy of the graph.
- Parameters:
as_view (bool (optional, default=False)) – If True return a view of the original undirected graph.
- Returns:
G – A deepcopy of the graph.
- Return type:
See also
Notes
This returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.
Examples
>>> G = nx.path_graph(2) >>> H = G.to_directed() >>> list(H.edges) [(0, 1), (1, 0)] >>> G2 = H.to_undirected() >>> list(G2.edges) [(0, 1)]
- to_undirected_class()[source]#
Returns the class to use for empty undirected copies.
If you subclass the base classes, use this to designate what directed class to use for to_directed() copies.
- update(edges=None, nodes=None)[source]#
Update the graph using nodes/edges/graphs as input.
Like dict.update, this method takes a graph as input, adding the graph’s nodes and edges to this graph. It can also take two inputs: edges and nodes. Finally it can take either edges or nodes. To specify only nodes the keyword nodes must be used.
The collections of edges and nodes are treated similarly to the add_edges_from/add_nodes_from methods. When iterated, they should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
- Parameters:
edges (Graph object, collection of edges, or None) – The first parameter can be a graph or some edges. If it has attributes nodes and edges, then it is taken to be a Graph-like object and those attributes are used as collections of nodes and edges to be added to the graph. If the first parameter does not have those attributes, it is treated as a collection of edges and added to the graph. If the first argument is None, no edges are added.
nodes (collection of nodes, or None) – The second parameter is treated as a collection of nodes to be added to the graph unless it is None. If edges is None and nodes is None an exception is raised. If the first parameter is a Graph, then nodes is ignored.
Examples
>>> G = nx.path_graph(5) >>> G.update(nx.complete_graph(range(4, 10))) >>> from itertools import combinations >>> edges = ( ... (u, v, {"power": u * v}) ... for u, v in combinations(range(10, 20), 2) ... if u * v < 225 ... ) >>> nodes = [1000] # for singleton, use a container >>> G.update(edges, nodes)
See also
add_edges_from
add multiple edges to a graph
add_nodes_from
add multiple nodes to a graph
DiGraph#
Directed graphs with self loops
- class graphscope.nx.DiGraph(incoming_graph_data=None, default_label=None, **attr)[source]#
Base class for directed graphs.
A DiGraph that holds the metadata of a graph, and provides NetworkX-like DiGraph APIs.
It is worth noticing that the graph is actually stored by the Analytical Engine backend. In other words, the Graph object holds nothing but metadata of a graph
DiGraph support nodes and edges with optional data, or attributes.
DiGraphs support directed edges. Self loops are allowed but multiple (parallel) edges are not.
Nodes can be arbitrary int/str/float/bool objects with optional key/value attributes.
Edges are represented as links between nodes with optional key/value attributes.
DiGraph support node label if it’s created from a GraphScope graph object. nodes are identified by (label, id) tuple.
- Parameters:
incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be any format that is supported by the to_networkx_graph() function, currently including edge list, dict of dicts, dict of lists, NetworkX graph, NumPy matrix or 2d ndarray, Pandas DataFrame, SciPy sparse matrix, or a GraphScope graph object.
default_label (default node label (optional, default: None)) – if incoming_graph_data is a GraphScope graph object, default label means the nodes of the label can be identified by id directly, other label nodes need to use (label, id) to identify.
attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.
See also
Examples
Create an empty graph structure (a “null graph”) with no nodes and no edges.
>>> G = nx.DiGraph()
G can be grown in several ways.
Nodes:
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3]) >>> G.add_nodes_from(range(100, 110)) >>> H = nx.path_graph(10) >>> G.add_nodes_from(H)
In addition integers, strings can represent a node.
>>> G.add_node('a node')
Edges:
G can also be grown by adding edges.
Add one edge,
>>> G.add_edge(1, 2)
a list of edges,
>>> G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes are added automatically. There are no errors when adding nodes or edges that already exist.
Attributes:
Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but can be added or changed using add_edge, add_node or direct manipulation of the attribute dictionaries named graph, node and edge respectively.
>>> G = nx.DiGraph(day="Friday") >>> G.graph {'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time='5pm') >>> G.add_nodes_from([3], time='2pm') >>> G.nodes[1] {'time': '5pm'} >>> G.nodes[1]['room'] = 714 >>> del G.nodes[1]['room'] # remove attribute >>> list(G.nodes(data=True)) [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript notation, or G.edges.
>>> G.add_edge(1, 2, weight=4.7 ) >>> G.add_edges_from([(3, 4), (4, 5)], color='red') >>> G.add_edges_from([(1, 2, {'color':'blue'}), (2, 3, {'weight':8})]) >>> G[1][2]['weight'] = 4.7 >>> G.edges[1, 2]['weight'] = 4
Warning: we protect the graph data structure by making G.edges[1, 2] a read-only dict-like structure. However, you can assign to attributes in e.g. G.edges[1, 2]. Thus, use 2 sets of brackets to add/change data attributes: G.edges[1, 2][‘weight’] = 4 (For multigraphs: MG.edges[u, v, key][name] = value).
Shortcuts:
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph True >>> [n for n in G if n < 3] # iterate through nodes [1, 2] >>> len(G) # number of nodes in graph 5
Often the best way to traverse all edges of a graph is via the neighbors. The neighbors are reported as an adjacency-dict G.adj or G.adjacency()
>>> for n, nbrsdict in G.adjacency(): ... for nbr, eattr in nbrsdict.items(): ... if 'weight' in eattr: ... # Do something useful with the edges ... pass
But the edges reporting object is often more convenient:
>>> for u, v, weight in G.edges(data='weight'): ... if weight is not None: ... # Do something useful with the edges ... pass
Transformation
Create a graph with GraphScope graph object. First we init a GraphScope graph with two node labels: person and comment`
>>> g = graphscope.g(directed=True).add_vertice("persion.csv", label="person").add_vertice("comment.csv", label="comment")
create a graph with g, set default_label to ‘person’
>>> G = nx.DiGraph(g, default_label="person")
person label nodes can be identified by id directly, for comment label, we has to use tuple (“comment”, id) identify. Like, add a person label node and a comment label node
>>> G.add_node(0, type="person") >>> G.add_node(("comment", 0), type="comment")
print property of two nodes
>>> G.nodes[0] {"type", "person"} >>> G.nodes[("comment", 0)] {"type", "comment"}
Reporting:
Simple graph information is obtained using object-attributes and methods. Reporting usually provides views instead of containers to reduce memory usage. The views update as the graph is updated similarly to dict-views. The objects nodes, `edges and adj provide access to data attributes via lookup (e.g. nodes[n], `edges[u, v], adj[u][v]) and iteration (e.g. nodes.items(), nodes.data(‘color’), nodes.data(‘color’, default=’blue’) and similarly for edges) Views exist for nodes, edges, neighbors()/adj and degree.
For details on these and other miscellaneous methods, see below.
- __contains__(n)#
Returns True if n is a node, False otherwise. Use: ‘n in G’.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> 1 in G True
- __getitem__(n)#
Returns a dict of neighbors of node n. Use: ‘G[n]’.
- Parameters:
n (node) – A node in the graph.
- Returns:
adj_dict – The adjacency dictionary for nodes connected to n.
- Return type:
dictionary
Notes
G[n] is the same as G.adj[n] and similar to G.neighbors(n) (which is an iterator over G.adj[n])
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G[0] AtlasView({1: {}})
- __init__(incoming_graph_data=None, default_label=None, **attr)[source]#
Initialize a graph with graph, edges, name, or graph attributes
- Parameters:
incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, any NetworkX graph object or any GraphScope graph object. If the corresponding optional Python packages are installed the data can also be a 2D NumPy array, a SciPy sparse matrix
default_label (default node label (optional, default: "_")) – if incoming_graph_data is a GraphScope graph object, default label means the nodes of the label can be accessed by id directly, other label nodes need to use (label, id) to access.
attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.
See also
convert
Examples
>>> G = nx.Graph() # or DiGraph >>> G = nx.Graph(name='my graph') >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges >>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday") >>> G.graph {'day': 'Friday'}
Created from a GraphScope graph object
>>> g = graphscope.g(directed=False) # if transform to DiGraph, directed=True >>> g.add_vertices("person.csv", label="person").add_vertices("comment.csv", label="comment").add_edges(...) >>> G = nx.Graph(g, default_label="person") # or DiGraph
- __iter__()#
Iterate over the nodes. Use: ‘for n in G’.
- Returns:
niter – An iterator over all nodes in the graph.
- Return type:
iterator
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> [n for n in G] [0, 1, 2, 3] >>> list(G) [0, 1, 2, 3]
- __len__()#
Returns the number of nodes in the graph. Use: ‘len(G)’.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
See also
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> len(G) 4
- add_edge(u_of_edge, v_of_edge, **attr)#
Add an edge between u and v.
The nodes u and v will be automatically added if they are not already in the graph.
Edge attributes can be specified with keywords or by directly accessing the edge’s attribute dictionary. See examples below.
- Parameters:
u (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int/string/float/tuple/bool hashable Python objects.
v (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int/string/float/tuple/bool hashable Python objects.
attr (keyword arguments, optional) – Edge data can be assigned using keyword arguments.
See also
add_edges_from
add a collection of edges
Notes
Adding an edge that already exists updates the edge data.
Many networkx algorithms designed for weighted graphs use an edge attribute (by default weight) to hold a numerical value.
Examples
The following all add the edge e=(1, 2) to graph G:
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> e = (1, 2) >>> G.add_edge(1, 2) # explicit two-node form >>> G.add_edge(*e) # single edge as tuple of two nodes >>> G.add_edges_from([(1, 2)]) # add edges from iterable container
Associate data to edges using keywords:
>>> G.add_edge(1, 2, weight=3) >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> G.add_edge(1, 2) >>> G[1][2].update({0: 5}) >>> G.edges[1, 2].update({0: 5})
- add_edges_from(ebunch_to_add, **attr)#
Add all the edges in ebunch_to_add.
- Parameters:
ebunch_to_add (container of edges) – Each edge given in the container will be added to the graph. The edges must be given as as 2-tuples (u, v) or 3-tuples (u, v, d) where d is a dictionary containing edge data.
attr (keyword arguments, optional) – Edge data can be assigned using keyword arguments.
See also
add_edge
add a single edge
add_weighted_edges_from
convenient way to add weighted edges
Notes
Adding the same edge twice has no effect but any edge data will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over attributes specified via keyword arguments.
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples >>> e = zip(range(0, 3), range(1, 4)) >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3) >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
- add_node(node_for_adding, **attr)#
Add a single node node_for_adding and update node attributes.
- Parameters:
node_for_adding (node) – A node can be int, float, str, tuple or bool object.
attr (keyword arguments, optional) – Set or change node attributes using key=value.
See also
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_node(1) >>> G.add_node(2) >>> G.number_of_nodes() 2
Use keywords set/change node attributes:
>>> G.add_node(1, size=10) >>> G.add_node(3, weight=0.4, type='apple')
Notes
nx.Graph support int, float, str, tuple or bool object of nodes.
- add_nodes_from(nodes_for_adding, **attr)#
Add multiple nodes.
- Parameters:
nodes_for_adding (iterable container) – A container of nodes (list, dict, set, etc.). OR A container of (node, attribute dict) tuples. Node attributes are updated using the attribute dict.
attr (keyword arguments, optional (default= no attributes)) – Update attributes for all nodes in nodes. Node attributes specified in nodes as a tuple take precedence over attributes specified via keyword arguments.
See also
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_nodes_from("Hello") >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)]) >>> G.add_nodes_from(K3) >>> sorted(G.nodes(), key=str) [0, 1, 2, 'H', 'e', 'l', 'o']
Use keywords to update specific node attributes for every node.
>>> G.add_nodes_from([1, 2], size=10) >>> G.add_nodes_from([3, 4], weight=0.4)
Use (node, attrdict) tuples to update attributes for specific nodes.
>>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})]) >>> G.nodes[1]["size"] 11 >>> H = nx.Graph() >>> H.add_nodes_from(G.nodes(data=True)) >>> H.nodes[1]["size"] 11
- add_weighted_edges_from(ebunch_to_add, weight='weight', **attr)#
Add weighted edges in ebunch_to_add with specified weight attr
- Parameters:
ebunch_to_add (container of edges) – Each edge given in the list or container will be added to the graph. The edges must be given as 3-tuples (u, v, w) where w is a number.
weight (string, optional (default= 'weight')) – The attribute name for the edge weights to be added.
attr (keyword arguments, optional (default= no attributes)) – Edge attributes to add/update for all edges.
See also
add_edge
add a single edge
add_edges_from
add multiple edges
Notes
Adding the same edge twice for Graph/DiGraph simply updates the edge data.
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
- property adj#
Graph adjacency object holding the neighbors of each node.
This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So G.adj[3][2][‘color’] = ‘blue’ sets the color of the edge (3, 2) to “blue”.
Iterating over G.adj behaves like a dict. Useful idioms include for nbr, datadict in G.adj[n].items():.
The neighbor information is also provided by subscripting the graph. So for nbr, foovalue in G[node].data(‘foo’, default=1): works.
For directed graphs, G.adj holds outgoing (successor) info.
- adjacency()#
Returns an iterator over (node, adjacency dict) tuples for all nodes.
For directed graphs, only outgoing neighbors/adjacencies are included.
- Returns:
adj_iter – An iterator over (node, adjacency dictionary) for all nodes in the graph.
- Return type:
iterator
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> [(n, nbrdict) for n, nbrdict in G.adjacency()] [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
- adjlist_inner_dict_factory#
alias of
NeighborDict
- adjlist_outer_dict_factory#
alias of
AdjListDict
- clear()#
Remove all nodes and edges from the graph.
This also removes the name, and all graph, node, and edge attributes.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.clear() >>> list(G.nodes) [] >>> list(G.edges) []
- clear_edges()#
Remove all edges from the graph without altering nodes.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.clear_edges() >>> list(G.nodes) [0, 1, 2, 3] >>> list(G.edges) []
- copy(as_view=False)#
Returns a copy of the graph.
The copy method by default returns an independent deep copy of the graph and attributes.
If as_view is True then a view is returned instead of a copy.
Notes
All copies reproduce the graph structure, but data attributes may be handled in different ways. There are three types of copies of a graph that people might want.
Deepcopy – A “deepcopy” copies the graph structure as well as all data attributes and any objects they might contain in Engine backend. The entire graph object is new so that changes in the copy do not affect the original object.
Fresh Data – For fresh data, the graph structure is copied while new empty data attribute dicts are created. The resulting graph is independent of the original and it has no edge, node or graph attributes. Fresh copies are not enabled. Instead use:
>>> H = G.__class__() >>> H.add_nodes_from(G) >>> H.add_edges_from(G.edges)
View – Inspired by dict-views, graph-views act like read-only versions of the original graph, providing a copy of the original structure without requiring any memory for copying the information.
- Parameters:
as_view (bool, optional (default=False)) – If True, the returned graph-view provides a read-only view of the original graph without actually copying any data.
- Returns:
G – A copy of the graph.
- Return type:
See also
to_directed
return a directed copy of the graph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> H = G.copy()
- property degree#
A DegreeView for the Graph as G.degree or G.degree().
The node degree is the number of edges adjacent to the node. The weighted node degree is the sum of the edge weights for edges incident to that node.
This object provides an iterator for (node, degree) as well as lookup for the degree for a single node.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
weight (string or None, optional (default=None)) – The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.
- Returns:
If a single node is requested
deg (int) – Degree of the node
OR if multiple nodes are requested
nd_iter (iterator) – The iterator returns two-tuples of (node, degree).
See also
Examples
>>> G = nx.DiGraph() >>> nx.add_path(G, [0, 1, 2, 3]) >>> G.degree(0) # node 0 with degree 1 1 >>> list(G.degree([0, 1, 2])) [(0, 1), (1, 2), (2, 2)]
- edge_attr_dict_factory#
alias of
NeighborAttrDict
- edge_subgraph(edges)#
Returns a independent deep copy subgraph induced by the specified edges.
The induced subgraph contains each edge in edges and each node incident to any one of those edges.
- Parameters:
edges (iterable) – An iterable of edges in this graph.
- Returns:
G – An edge-induced subgraph of this graph with the same edge attributes.
- Return type:
Notes
Unlike NetworkX return a view, here return a independent deep copy subgraph.
Examples
>>> G = nx.path_graph(5) # or DiGraph >>> H = G.edge_subgraph([(0, 1), (3, 4)]) >>> list(H.nodes) [0, 1, 3, 4] >>> list(H.edges) [(0, 1), (3, 4)]
- property edges#
An OutEdgeView of the DiGraph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The OutEdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, G.edges[u, v][‘color’] provides the value of the color attribute for edge (u, v) while for (u, v, c) in G.edges.data(‘color’, default=’red’): iterates through all the edges yielding the color attribute with default ‘red’ if no color attribute exists.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
data (string or bool, optional (default=False)) – The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v).
default (value, optional (default=None)) – Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
edges – A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v][‘foo’].
- Return type:
OutEdgeView
Notes
Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.
Examples
>>> G = nx.DiGraph() >>> nx.add_path(G, [0, 1, 2]) >>> G.add_edge(2, 3, weight=5) >>> [e for e in G.edges] [(0, 1), (1, 2), (2, 3)] >>> G.edges.data() # default data is {} (empty dict) OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]) >>> G.edges.data("weight", default=1) OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]) >>> G.edges([0, 2]) # only edges incident to these nodes OutEdgeDataView([(0, 1), (2, 3)]) >>> G.edges(0) # only edges incident to a single node (use G.adj[0]?) OutEdgeDataView([(0, 1)])
- get_edge_data(u, v, default=None)#
Returns the attribute dictionary associated with edge (u, v).
This is identical to G[u][v] except the default is returned instead of an exception if the edge doesn’t exist.
- Parameters:
u (nodes) –
v (nodes) –
default (any Python object (default=None)) – Value to return if the edge (u, v) is not found.
- Returns:
edge_dict – The edge attribute dictionary.
- Return type:
dictionary
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G[0][1] {}
Warning: Assigning to G[u][v] is not permitted. But it is safe to assign attributes G[u][v][‘foo’]
>>> G[0][1]["weight"] = 7 >>> G[0][1]["weight"] 7 >>> G[1][0]["weight"] 7
>>> G = nx.path_graph(4) # or DiGraph >>> G.get_edge_data(0, 1) # default edge data is {} {} >>> e = (0, 1) >>> G.get_edge_data(*e) # tuple form {} >>> G.get_edge_data("a", "b", default=0) # edge not in graph, return 0 0
- graph_attr_dict_factory#
alias of
dict
- graph_cache_factory#
alias of
Cache
- property graph_type#
The type of the graph object.
- Returns:
the type of the graph.
- Return type:
type (types_pb2.GraphType)
- has_edge(u, v)#
Returns True if the edge (u, v) is in the graph.
This is the same as v in G[u] without KeyError exceptions.
- Parameters:
u (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int, str, float, tuple, bool hashable Python objects.
v (nodes) – Nodes can be, for example, strings or numbers. Nodes must be int, str, float, tuple, bool hashable Python objects.
- Returns:
edge_ind – True if edge is in the graph, False otherwise.
- Return type:
bool
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.has_edge(0, 1) # using two nodes True >>> e = (0, 1) >>> G.has_edge(*e) # e is a 2-tuple (u, v) True >>> e = (0, 1, {"weight": 7}) >>> G.has_edge(*e[:2]) # e is a 3-tuple (u, v, data_dictionary) True
The following syntax are equivalent:
>>> G.has_edge(0, 1) True >>> 1 in G[0] # though this gives KeyError if 0 not in G True
- has_node(n)#
Returns True if the graph contains the node n.
Identical to n in G
- Parameters:
n (node) –
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.has_node(0) True
It is more readable and simpler to use
>>> 0 in G True
- has_predecessor(u, v)[source]#
Returns True if node u has predecessor v.
This is true if graph has the edge u<-v.
- has_successor(u, v)[source]#
Returns True if node u has predecessor v.
This is true if graph has the edge u<-v.
- property in_degree#
An InDegreeView for (node, in_degree) or in_degree for single node.
The node in_degree is the number of edges pointing to the node. The weighted node degree is the sum of the edge weights for edges incident to that node.
This object provides an iteration over (node, in_degree) as well as lookup for the degree for a single node.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
weight (string or None, optional (default=None)) – The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.
- Returns:
If a single node is requested
deg (int) – In-degree of the node
OR if multiple nodes are requested
nd_iter (iterator) – The iterator returns two-tuples of (node, in-degree).
See also
Examples
>>> G = nx.DiGraph() >>> nx.add_path(G, [0, 1, 2, 3]) >>> G.in_degree(0) # node 0 with degree 0 0 >>> list(G.in_degree([0, 1, 2])) [(0, 0), (1, 1), (2, 1)]
- property in_edges#
An InEdgeView of the Graph as G.in_edges or G.in_edges().
in_edges(self, nbunch=None, data=False, default=None):
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
data (string or bool, optional (default=False)) – The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v).
default (value, optional (default=None)) – Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
in_edges – A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v][‘foo’].
- Return type:
InEdgeView
See also
- property key#
Key of the coresponding engine graph.
- property name#
String identifier of the graph.
This graph attribute appears in the attribute dict G.graph keyed by the string “name”. as well as an attribute (technically a property) G.name. This is entirely user controlled.
- nbunch_iter(nbunch=None)#
Returns an iterator over nodes contained in nbunch that are also in the graph.
The nodes in nbunch are checked for membership in the graph and if not are silently ignored.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
- Returns:
niter – An iterator over nodes in nbunch that are also in the graph. If nbunch is None, iterate over all nodes in the graph.
- Return type:
iterator
- Raises:
NetworkXError – If nbunch is not a node or sequence of nodes. If a node in nbunch is not hashable.
See also
Notes
When nbunch is an iterator, the returned iterator yields values directly from nbunch, becoming exhausted when nbunch is exhausted.
To test whether nbunch is a single node, one can use “if nbunch in self:”, even after processing with this routine.
If nbunch is not a node or a (possibly empty) sequence/iterator or None, a
NetworkXError
is raised. Also, if any object in nbunch is not hashable, aNetworkXError
is raised.
- neighbors(n)#
Returns an iterator over successor nodes of n.
A successor of n is a node m such that there exists a directed edge from n to m.
- Parameters:
n (node) – A node in the graph
- Raises:
NetworkXError – If n is not in the graph.
See also
Notes
neighbors() and successors() are the same.
- node_attr_dict_factory#
alias of
NodeAttrDict
- node_dict_factory#
alias of
NodeDict
- property nodes#
A NodeView of the Graph as G.nodes or G.nodes().
Can be used as G.nodes for data lookup and for set-like operations. Can also be used as G.nodes(data=’color’, default=None) to return a NodeDataView which reports specific node data but no set operations. It presents a dict-like interface as well with G.nodes.items() iterating over (node, nodedata) 2-tuples and G.nodes[3][‘foo’] providing the value of the foo attribute for node 3. In addition, a view G.nodes.data(‘foo’) provides a dict-like interface to the foo attribute of each node. G.nodes.data(‘foo’, default=1) provides a default for nodes that do not have attribute foo.
- Parameters:
data (string or bool, optional (default=False)) – The node attribute returned in 2-tuple (n, ddict[data]). If True, return entire node attribute dict as (n, ddict). If False, return just the nodes n.
default (value, optional (default=None)) – Value used for nodes that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
Allows set-like operations over the nodes as well as node attribute dict lookup and calling to get a NodeDataView. A NodeDataView iterates over (n, data) and has no set operations. A NodeView iterates over n and includes set operations.
When called, if data is False, an iterator over nodes. Otherwise an iterator of 2-tuples (node, attribute value) where the attribute is specified in data. If data is True then the attribute becomes the entire data dictionary.
- Return type:
NodeView
Notes
If your node data is not needed, it is simpler and equivalent to use the expression
for n in G
, orlist(G)
.Examples
There are two simple ways of getting a list of all nodes in the graph:
>>> G = nx.path_graph(3) >>> list(G.nodes) [0, 1, 2] >>> list(G) [0, 1, 2]
To get the node data along with the nodes:
>>> G.add_node(1, time="5pm") >>> G.nodes[0]["foo"] = "bar" >>> list(G.nodes(data=True)) [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})] >>> list(G.nodes.data()) [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
>>> list(G.nodes(data="foo")) [(0, 'bar'), (1, None), (2, None)] >>> list(G.nodes.data("foo")) [(0, 'bar'), (1, None), (2, None)]
>>> list(G.nodes(data="time")) [(0, None), (1, '5pm'), (2, None)] >>> list(G.nodes.data("time")) [(0, None), (1, '5pm'), (2, None)]
>>> list(G.nodes(data="time", default="Not Available")) [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')] >>> list(G.nodes.data("time", default="Not Available")) [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
If some of your nodes have an attribute and the rest are assumed to have a default attribute value you can create a dictionary from node/attribute pairs using the default keyword argument to guarantee the value is never None:
>>> G = nx.Graph() >>> G.add_node(0) >>> G.add_node(1, weight=2) >>> G.add_node(2, weight=3) >>> dict(G.nodes(data="weight", default=1)) {0: 1, 1: 2, 2: 3}
- number_of_edges(u=None, v=None)#
Returns the number of edges between two nodes.
- Parameters:
u (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.
v (nodes, optional (default=all edges)) – If u and v are specified, return the number of edges between u and v. Otherwise return the total number of all edges.
- Returns:
nedges – The number of edges in the graph. If nodes u and v are specified return the number of edges between those nodes. If the graph is directed, this only returns the number of edges from u to v.
- Return type:
int
See also
Examples
For undirected graphs, this method counts the total number of edges in the graph:
>>> G = nx.path_graph(4) >>> G.number_of_edges() 3
If you specify two nodes, this counts the total number of edges joining the two nodes:
>>> G.number_of_edges(0, 1) 1
For directed graphs, this method can count the total number of directed edges from u to v:
>>> G = nx.DiGraph() >>> G.add_edge(0, 1) >>> G.add_edge(1, 0) >>> G.number_of_edges(0, 1) 1
- number_of_nodes()#
Returns the number of nodes in the graph.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.number_of_nodes() 3
- property op#
The DAG op of this graph.
- order()#
Returns the number of nodes in the graph.
- Returns:
nnodes – The number of nodes in the graph.
- Return type:
int
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> G.order() 3
- property out_degree#
An OutDegreeView for (node, out_degree)
The node out_degree is the number of edges pointing out of the node. The weighted node degree is the sum of the edge weights for edges incident to that node.
This object provides an iterator over (node, out_degree) as well as lookup for the degree for a single node.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
weight (string or None, optional (default=None)) – The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.
- Returns:
If a single node is requested
deg (int) – Out-degree of the node
OR if multiple nodes are requested
nd_iter (iterator) – The iterator returns two-tuples of (node, out-degree).
Examples
>>> G = nx.DiGraph() >>> nx.add_path(G, [0, 1, 2, 3]) >>> G.out_degree(0) # node 0 with degree 1 1 >>> list(G.out_degree([0, 1, 2])) [(0, 1), (1, 1), (2, 1)]
- property out_edges#
An OutEdgeView of the DiGraph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The OutEdgeView provides set-like operations on the edge-tuples as well as edge attribute lookup. When called, it also provides an EdgeDataView object which allows control of access to edge attributes (but does not provide set-like operations). Hence, G.edges[u, v][‘color’] provides the value of the color attribute for edge (u, v) while for (u, v, c) in G.edges.data(‘color’, default=’red’): iterates through all the edges yielding the color attribute with default ‘red’ if no color attribute exists.
- Parameters:
nbunch (single node, container, or all nodes (default= all nodes)) – The view will only report edges incident to these nodes.
data (string or bool, optional (default=False)) – The edge attribute returned in 3-tuple (u, v, ddict[data]). If True, return edge attribute dict in 3-tuple (u, v, ddict). If False, return 2-tuple (u, v).
default (value, optional (default=None)) – Value used for edges that don’t have the requested attribute. Only relevant if data is not True or False.
- Returns:
edges – A view of edge attributes, usually it iterates over (u, v) or (u, v, d) tuples of edges, but can also be used for attribute lookup as edges[u, v][‘foo’].
- Return type:
OutEdgeView
Notes
Nodes in nbunch that are not in the graph will be (quietly) ignored. For directed graphs this returns the out-edges.
Examples
>>> G = nx.DiGraph() >>> nx.add_path(G, [0, 1, 2]) >>> G.add_edge(2, 3, weight=5) >>> [e for e in G.edges] [(0, 1), (1, 2), (2, 3)] >>> G.edges.data() # default data is {} (empty dict) OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]) >>> G.edges.data("weight", default=1) OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]) >>> G.edges([0, 2]) # only edges incident to these nodes OutEdgeDataView([(0, 1), (2, 3)]) >>> G.edges(0) # only edges incident to a single node (use G.adj[0]?) OutEdgeDataView([(0, 1)])
- property pred#
Graph adjacency object holding the predecessors of each node.
This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So G.pred[2][3][‘color’] = ‘blue’ sets the color of the edge (3, 2) to “blue”.
Iterating over G.pred behaves like a dict. Useful idioms include for nbr, datadict in G.pred[n].items():. A data-view not provided by dicts also exists: for nbr, foovalue in G.pred[node].data(‘foo’): A default can be set via a default argument to the data method.
- predecessors(n)[source]#
Returns an iterator over predecessor nodes of n.
A predecessor of n is a node m such that there exists a directed edge from m to n.
- Parameters:
n (node) – A node in the graph
- Raises:
NetworkXError – If n is not in the graph.
See also
- remove_edge(u, v)#
Remove the edge between u and v.
- Parameters:
u (nodes) – Remove the edge between nodes u and v.
v (nodes) – Remove the edge between nodes u and v.
- Raises:
NetworkXError – If there is not an edge between u and v.
See also
remove_edges_from
remove a collection of edges
Examples
>>> G = nx.path_graph(4) # or DiGraph, etc >>> G.remove_edge(0, 1) >>> e = (1, 2) >>> G.remove_edge(*e) # unpacks e from an edge tuple >>> e = (2, 3, {"weight": 7}) # an edge with attribute data >>> G.remove_edge(*e[:2]) # select first part of edge tuple
- remove_edges_from(ebunch)#
Remove all edges specified in ebunch.
- Parameters:
ebunch (list or container of edge tuples) –
Each edge given in the list or container will be removed from the graph. The edges can be:
2-tuples (u, v) edge between u and v.
3-tuples (u, v, k) where k is ignored.
See also
remove_edge
remove a single edge
Notes
Will fail silently if an edge in ebunch is not in the graph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> ebunch = [(1, 2), (2, 3)] >>> G.remove_edges_from(ebunch)
- remove_node(n)#
Remove node n.
Removes the node n and all adjacent edges. Attempting to remove a non-existent node will raise an exception.
- Parameters:
n (node) – A node in the graph
- Raises:
NetworkXError – If n is not in the graph.
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> list(G.edges) [(0, 1), (1, 2)] >>> G.remove_node(1) >>> list(G.edges) []
- remove_nodes_from(nodes_for_removing)#
Remove multiple nodes.
- Parameters:
nodes_for_removing (iterable container) – A container of nodes (list, dict, set, etc.). If a node in the container is not in the graph it is silently ignored.
See also
Examples
>>> G = nx.path_graph(3) # or DiGraph >>> e = list(G.nodes) >>> e [0, 1, 2] >>> G.remove_nodes_from(e) >>> list(G.nodes) []
- reverse(copy=True)[source]#
Returns the reverse of the graph.
The reverse is a graph with the same nodes and edges but with the directions of the edges reversed.
- Parameters:
copy (bool optional (default=True)) – If True, return a new DiGraph holding the reversed edges. If False, the reverse graph is created using a view of the original graph.
- property schema#
Schema of the graph.
- Returns:
the schema of the graph
- Return type:
GraphSchema
- property session#
Get the session of graph.
- Returns:
Return session that the graph belongs to.
- property session_id#
Get session’s id of graph.
- Returns:
Return session id that the graph belongs to.
- Return type:
str
- set_edge_data(u, v, data)#
Set edge data of edge (u, v).
- Parameters:
u (nodes) – Nodes can be int, str, float, tuple, bool hashable Python objects.
v (nodes) – Nodes can be int, str, float, tuple, bool hashable Python objects.
data (dict) – Edge data to set to edge (u, v)
See also
set_node_data
set node data of node
Notes
the method is called when to set_items in AdjEdgeAttr
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_edge(1, 2) >>> dd = {'foo': 'bar'} >>> G[1][2] = dd # call G.set_edge_data(1, 2, dd) >>> G[1][2] {'foo': 'bar'}
- set_node_data(n, data)#
Set data of node.
- Parameters:
n (node) – node can be int, str, float, tuple, bool hashable Python object which is existed in graph.
data (dict) – data to set to n
See also
set_edge_data
set data of edge
Notes
the method is called when to set_items in NodeAttr
Examples
>>> G = nx.Graph() # or DiGraph >>> G.add_node(1) >>> dd = {'weight': 3} >>> G.nodes[1] = dd # call G.set_node_data(1, dd) >>> G.nodes[1] {'weight': 3}
- property signature#
Generate a signature of the current graph
- size(weight=None)#
Returns the number of edges or total of all edge weights.
- Parameters:
weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1.
- Returns:
size – The number of edges or (if weight keyword is provided) the total weight sum.
If weight is None, returns an int. Otherwise a float (or more general numeric if the weights are more general).
- Return type:
numeric
See also
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> G.size() 3
>>> G = nx.Graph() # or DiGraph >>> G.add_edge("a", "b", weight=2) >>> G.add_edge("b", "c", weight=4) >>> G.size() 2 >>> G.size(weight="weight") 6.0
- subgraph(nodes)#
Returns a independent deep copy subgraph induced on nodes.
The induced subgraph of the graph contains the nodes in nodes and the edges between those nodes.
- Parameters:
nodes (list, iterable) – A container of nodes which will be iterated through once.
- Returns:
G – A subgraph of the graph.
- Return type:
Notes
Unlike NetowrkX return a view, here return a independent deep copy subgraph.
Examples
>>> G = nx.path_graph(4) # or DiGraph >>> H = G.subgraph([0, 1, 2]) >>> list(H.edges) [(0, 1), (1, 2)]
- property succ#
Graph adjacency object holding the neighbors of each node.
This object is a read-only dict-like structure with node keys and neighbor-dict values. The neighbor-dict is keyed by neighbor to the edge-data-dict. So G.adj[3][2][‘color’] = ‘blue’ sets the color of the edge (3, 2) to “blue”.
Iterating over G.adj behaves like a dict. Useful idioms include for nbr, datadict in G.adj[n].items():.
The neighbor information is also provided by subscripting the graph. So for nbr, foovalue in G[node].data(‘foo’, default=1): works.
For directed graphs, G.adj holds outgoing (successor) info.
- successors(n)[source]#
Returns an iterator over successor nodes of n.
A successor of n is a node m such that there exists a directed edge from n to m.
- Parameters:
n (node) – A node in the graph
- Raises:
NetworkXError – If n is not in the graph.
See also
Notes
neighbors() and successors() are the same.
- to_directed(as_view=False)#
Returns a directed representation of the graph.
- Parameters:
as_view (bool, optional (default=False)) – If True return a view of the original directed graph.
- Returns:
G – A directed graph with the same name, same nodes, and with each edge (u, v, data) replaced by two directed edges (u, v, data) and (v, u, data).
- Return type:
Notes
This by default returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.
Examples
>>> G = nx.Graph() >>> G.add_edge(0, 1) >>> H = G.to_directed() >>> list(H.edges) [(0, 1), (1, 0)]
If already directed, return a (deep) copy
>>> G = nx.DiGraph() >>> G.add_edge(0, 1) >>> H = G.to_directed() >>> list(H.edges) [(0, 1)]
- to_undirected(as_view=False)#
Returns an undirected copy of the graph.
- Parameters:
as_view (bool (optional, default=False)) – If True return a view of the original undirected graph.
- Returns:
G – A deepcopy of the graph.
- Return type:
See also
Notes
This returns a “deepcopy” of the edge, node, and graph attributes which attempts to completely copy all of the data and references.
Examples
>>> G = nx.path_graph(2) >>> H = G.to_directed() >>> list(H.edges) [(0, 1), (1, 0)] >>> G2 = H.to_undirected() >>> list(G2.edges) [(0, 1)]
- to_undirected_class()#
Returns the class to use for empty undirected copies.
If you subclass the base classes, use this to designate what directed class to use for to_directed() copies.
- update(edges=None, nodes=None)#
Update the graph using nodes/edges/graphs as input.
Like dict.update, this method takes a graph as input, adding the graph’s nodes and edges to this graph. It can also take two inputs: edges and nodes. Finally it can take either edges or nodes. To specify only nodes the keyword nodes must be used.
The collections of edges and nodes are treated similarly to the add_edges_from/add_nodes_from methods. When iterated, they should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
- Parameters:
edges (Graph object, collection of edges, or None) – The first parameter can be a graph or some edges. If it has attributes nodes and edges, then it is taken to be a Graph-like object and those attributes are used as collections of nodes and edges to be added to the graph. If the first parameter does not have those attributes, it is treated as a collection of edges and added to the graph. If the first argument is None, no edges are added.
nodes (collection of nodes, or None) – The second parameter is treated as a collection of nodes to be added to the graph unless it is None. If edges is None and nodes is None an exception is raised. If the first parameter is a Graph, then nodes is ignored.
Examples
>>> G = nx.path_graph(5) >>> G.update(nx.complete_graph(range(4, 10))) >>> from itertools import combinations >>> edges = ( ... (u, v, {"power": u * v}) ... for u, v in combinations(range(10, 20), 2) ... if u * v < 225 ... ) >>> nodes = [1000] # for singleton, use a container >>> G.update(edges, nodes)
See also
add_edges_from
add multiple edges to a graph
add_nodes_from
add multiple nodes to a graph