Sankey charts are a type of visualization that can help you understand and communicate the flow of data through a complex system. They are named after Matthew Henry Phineas Riall Sankey, a civil engineer who first used them to illustrate the energy efficiency of steam engines in the 1860s. Today, sankey charts are widely used in many fields, including business, engineering, economics, and science, to show how different quantities change as they move from one category to another.
In this article, we’ll explore the benefits of using sankey charts and how to create them using popular data visualization tools like Tableau, Power BI, and Python libraries like Matplotlib and Plotly.
Benefits of Using Sankey Charts
Better understand data flow
Sankey charts are particularly useful when you’re dealing with a large amount of data, as they provide a more intuitive way of visualizing complex flows than other tools. By displaying connections between different data categories with varying widths, sankey charts highlight the direction, magnitude, and interactions between flows. This makes it easier to identify patterns, see which categories have the most impact, and pinpoint where losses or gains in data occur.
Communicate complex information clearly
Sankey charts are not only informative but also visually appealing, which can make your data more engaging and easier to understand for a wide audience. They can help capture attention and convey ideas more efficiently than text or tables, as they allow viewers to effortlessly follow the flow and relationships between categories.
Compare multiple flows on a single chart
Unlike other types of charts, sankey charts can easily display multiple flow relationships on a single visual. This allows you to compare different sets of data side-by-side or at the same time, making it easier to identify relationships, patterns, or differences between categories.
Provide context to the data
Sankey charts often come with additional elements such as backgrounds or icons that help provide context to the data being visualized. This can include geographical locations, labels, or a time series, which can help create a more comprehensive view of the data and its underlying context.
Creating Sankey Charts with Popular Tools
Tableau
If you’re using Tableau for data visualization, creating a sankey chart is quite straightforward. Start by importing your data into Tableau, then drag your data fields to the Marks card, assigning the source and target fields for the flow and the direction.
Next, drag the source field to the Columns shelf, and the target field to the Rows shelf. Finally, set the Mark type to “Line” and choose “Sankey” from the drop-down menu, along with any formatting options you’d like to apply.
Power BI
In Power BI, creating a sankey chart follows a similar process. Begin by importing your data, then place the source and target fields on the visualization pane. Choose the “Sankey” chart type from the available options, and you’ll have a basic sankey chart with your data.
You can customize the chart by adding additional fields for tooltips, colors, and other visual elements to enhance the representation of your data.
Python with Matplotlib and Plotly
For those who prefer programming or want more control over the design, Python libraries like Matplotlib and Plotly provide excellent tools for creating sankey charts.
Matplotlib, using the pygal library, offers a simple Sankey class and allows you to easily manipulate and style the chart. Below is a basic outline of how to create a sankey chart using Matplotlib:
“`python
import matplotlib.pyplot as plt
import matplotlib.sankey as sankey
Define nodes
nodes = [‘Node A’, ‘Node B’, ‘Node C’, ‘Node D’]
Define flow
flows = [[10, 15, 20, 5], [25, 12, 8]]
Create a Sankey diagram
sankey = sankey.Sankey(orientation=’ld’)
sankey.add(‘Source 1’, [‘Node B’, ‘Node C’, ‘Node D’, ‘NodeA’],
[10, 15, 20, 5],
label=[‘B’, ‘C’, ‘D’],
connector={‘color’: ‘grey’},
headwidth=6,
headlength=18)
sankey.add(‘Source 2’, [‘Node B’, ‘Node D’, ‘NodeC’, ‘Node A’],
[25, 12, 8],
label=[‘B’, ‘C’],
connector={‘color’: ‘grey’},
path=[‘B->C’, ‘D’, ‘D->C’])
sankey.finish()
Display the diagram
plt.show()
“`
For more advanced usage, you can customize colors, labels, and other attributes to create professional-looking sankey charts.
Plotly also offers a straightforward method for creating sankey diagrams, with built-in style options and export capabilities as static images or interactive web-based visualizations.
Applications
Business analysis
Sankey charts can help businesses understand the flow of goods, revenue, or customers through different processes or systems, such as supply chains, sales pipelines, or marketing campaigns.
Energy and engineering
Electrical engineers use sankey charts to visualize electrical power distribution and losses in electrical systems, while energy analysts might apply them to view energy flows within power networks or buildings.
Environmental studies
Environmental scientists can utilize sankey charts to track the movement of pollutants or resources through ecosystems or human impact on the environment.
Social sciences
Social scientists may find sankey charts useful to analyze the flow of people, ideas, or resources within social networks or in the context of migration patterns.
Biology
Biology researchers often use sankey charts to map the flow of molecules like proteins or nutrients through complex biochemical pathways or to visualize the spread of diseases.
Sankey charts are a versatile tool that can be applied across various fields to help understand, communicate, and make decisions based on the flow of data in a visually intuitive and dynamic way. By using the right tools and techniques, you can create effective sankey charts that provide valuable insights and support informed decision-making.
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