Sankey charts are one of the most powerful visualization tools used to represent the flow of goods, services, and information between different entities. They are known for their ability to easily show the relationships between multiple nodes and the flow of goods or services between them. In this article, we will discuss how to master the art of Sankey chart visualization and create stunning Sankey diagrams using Python’s popular data visualization library, Matplotlib.

Table of Contents

1. What are Sankey Charts?
2. When to Use Sankey Charts
3. Understanding Sankey Chart Variables

4. Creating a Sankey Chart in Python using Matplotlib
   a. Importing the Nodes and Links

   b. Setting up the Data Structure

   c. Creating the Sankey Chart

   d. Customization Options

5. Common Sankey Chart Applications
6. Best Practices for Sankey Chart Design

7. Conclusion

8. What are Sankey Charts?

Sankey charts are a type of flowchart that are used to represent the flow of goods, services, or information between different entities. They are named after the Austrian theoretical chemist and Nobel laureate, chemist Rudolf Sankey, who first proposed the concept in the late 1800s. Sankey charts are particularly useful in representing complex systems that involve multiple steps and multiple entities.

2. When to Use Sankey Charts

Sankey charts are ideal for visualizing the flow of goods, services, or information between different entities. Some of the best use cases for Sankey charts include:

• Analyzing supply chain processes
• Tracking the flow of goods or services between different regions or countries
• Representing the impact of policy changes on an economy
• Displaying the allocation of resources within a business

1. Understanding Sankey Chart Variables

Sankey charts rely on several variables that are used to represent the flow of goods, services, or information between different entities. These variables include:

• Outlet nodes: These are nodes that are connected to other nodes through edges. Outlet nodes represent the endpoints of a flow.
• Inlet nodes: These are nodes that receive edges from other nodes and are connected to one or more outlet nodes. Inlet nodes represent the starting points of a flow.
• Links: Links are the connections between inlet nodes and outlet nodes. Links represent the flow of goods, services, or information between different entities.
• Capacity: Capacity represents the maximum amount of goods, services, or information that can flow between two nodes.
• Direction: Direction represents the direction of flow from inlet nodes to outlet nodes.

1. Creating a Sankey Chart in Python using Matplotlib

Python’s Matplotlib library provides a convenient way to create Sankey charts. Here is an example of how to create a simple Sankey chart:
“`python
import matplotlib.pyplot as plt
import numpy as np

Set up the data structure

nodes = np.array([
[‘A’, ‘B’, ‘C’],
[‘A’, ‘D’, ‘E’],
[‘B’, ‘F’, ‘G’],
[‘C’, ‘F’, ‘G’],
[‘C’, ‘H’, ‘I’],
[‘E’, ‘I’],
])
links = np.array([
[0, 1, 0, 1, 0, 1],
[1, 0, 1, 0, 1, 0],
[1, 0, 0, 1, 1, 0],
[0, 1, 0, 0, 1, 0],
[0, 1, 1, 0, 0, 1],
[1, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 0, 1],
])

Create the Sankey chart

plt.figure(figsize=(10, 5))
g = nx.DiGraph()
g.addnodesfrom(nodes)
g.addedgesfrom(links)
pos = nx.springlayout(g)
nx.drawnetworkx(g, pos=pos, nodesize=2000, edgecolor=’b’, with_labels=True)
plt.title(‘Sankey Chart Example’)
plt
“`

SankeyMaster

SankeyMaster is your go-to tool for creating complex Sankey charts . Easily enter data and create Sankey charts that accurately reveal intricate data relationships.