Search⌘ K
AI Features
Home
Courses
Designing Graphical Causal Bayesian Networks in Python

Plotting Graphs in Python

Explore how to plot various graph structures in Python using the NetworkX library and Matplotlib. Learn to customize node positions, labels, and colors, and create star and tree graphs such as company organigrams. This lesson helps you visualize and understand network structures essential for Bayesian network modeling.

We'll cover the following...

Plot a simple graph

To plot the graph, we can use the nx.draw() method from the networkx library.

The nx.draw() function visualizes graphs in NetworkX, where the first parameter is the graph object. The second key parameter, often pos, specifies node positions as a dictionary mapping nodes to (x, y) coordinates.

Let's say we want to plot the following graph using Python:

Members of the family represented by nodes.
Members of the family represented by nodes.

Here's the complete code for plotting the graph:

Python 3.8
import networkx as nx
import matplotlib.pyplot as plt
# Create an empty graph
G = nx.Graph()
# Add nodes to the graph with custom labels
G.add_node("Parent 1")
G.add_node("Parent 2")
G.add_node("Child 1")
G.add_node("Child 2")
# Add edges between nodes
G.add_edge("Parent 1", "Child 1")
G.add_edge("Parent 1", "Child 2")
G.add_edge("Parent 2", "Child 1")
G.add_edge("Parent 2", "Child 2")
# Draw the graph with larger nodes
nx.draw(G, with_labels=True, node_color="lightblue", node_size=4500, font_weight="bold")
# Show the plot
plt.savefig('output/FamilyGraph')
plt.show()

  • Line 2: We import matplotlib library as plt.

  • Line 20: We use the ...