2.2: NetworkX
- Page ID
- 46577
To represent graphs, we’ll use a package called NetworkX, which is the most commonly used network library in Python. You can read more about it at http://thinkcomplex.com/netx, but I’ll explain it as we go along.
We can create a directed graph by importing NetworkX (usually imported as nx
) and instantiating nx.DiGraph
:
import networkx as nx
G = nx.DiGraph()
At this point, G
is a DiGraph
object that contains no nodes and no edges. We can add nodes using the add_node
method:
G.add_node('Alice') G.add_node('Bob') G.add_node('Chuck')
Now we can use the nodes
method to get a list of nodes:
>>> list(G.nodes()) NodeView(('Alice', 'Bob', 'Chuck'))
The nodes
method returns a NodeView
, which can be used in a for loop or, as in this example, used to make a list.
Adding edges works pretty much the same way:
G.add_edge('Alice', 'Bob') G.add_edge('Alice', 'Chuck') G.add_edge('Bob', 'Alice') G.add_edge('Bob', 'Chuck')
And we can use edges
to get the list of edges:
>>> list(G.edges()) [('Alice', 'Bob'), ('Alice', 'Chuck'), ('Bob', 'Alice'), ('Bob', 'Chuck')]
NetworkX provides several functions for drawing graphs; draw_circular
arranges the nodes in a circle and connects them with edges:
nx.draw_circular(G, node_color=COLORS[0], node_size=2000, with_labels=True)
That’s the code I use to generate Figure 2.1.1. The option with_labels
causes the nodes to be labeled; in the next example we’ll see how to label the edges.
To generate Figure \(\PageIndex{1}\), I start with a dictionary that maps from each city name to its approximate longitude and latitude:
positions = dict(Albany=(-74, 43), Boston=(-71, 42), NYC=(-74, 41), Philly=(-75, 40))
Since this is an undirected graph, I instantiate nx.Graph
:
G = nx.Graph()
Then I can use add_nodes_from
to iterate the keys of positions
and add them as nodes:
G.add_nodes_from(positions)
Next I’ll make a dictionary that maps from each edge to the corresponding driving time:
drive_times = {('Albany', 'Boston'): 3, ('Albany', 'NYC'): 4, ('Boston', 'NYC'): 4, ('NYC', 'Philly'): 2}
Now I can use add_edges_from
, which iterates the keys of drive_times
and adds them as edges:
G.add_edges_from(drive_times)
Instead of draw_circular
, which arranges the nodes in a circle, I’ll use draw
, which takes the position dictionary as the second parameter:
nx.draw(G, positions,
node_color=COLORS[1],
node_shape='s',
node_size=2500,
with_labels=True)
draw
uses positions
to determine the locations of the nodes.
To add the edge labels, we use draw_networkx_edge_labels
:
nx.draw_networkx_edge_labels(G, positions, edge_labels=drive_times)
The edge_labels
parameter expects a dictionary that maps from each pair of nodes to a label; in this case, the labels are driving times between cities. And that’s how I generated Figure \(\PageIndex{1}\).
In both of these examples, the nodes are strings, but in general they can be any hashable type.