Flow Diagrams Unveiled: Harnessing the Power of Sankey Charts for Data Visualisation
In the realm of information visualization, Sankey diagrams have emerged as a powerful tool for representing complex flows and transitions between different data sets. Originating from the design of steamships, these charts have evolved into a staple in the visualization toolkit, offering a clear, intuitive way to visualize the distribution of data across related systems or among categories. This article delves into the creation and applications of Sankey charts, exploring how they can be harnessed to communicate data in a compelling and understandable manner.
Understanding Sankey Charts
At their core, Sankey charts are flow diagrams that employ a waterfall or flow-like aesthetic to visualize quantities. Each arrow or bar represents a particular flow or transfer between different systems, revealing the magnitude of the flow by the width of the arrow. This visual representation makes it possible to understand the flow of items or energy between processes, or between different stages in a system, with ease.
Creating Sankey Charts
Creating a Sankey chart involves several steps, though the complexity of each step largely depends on whether you are using software or manually creating the chart.
Software-Based Creation
Most modern data visualization software, including Tableau, Python’s Matplotlib and Seaborn libraries, and Microsoft Excel, offer tools for creating Sankey diagrams directly. These tools usually simplify the process by allowing users to input data and automatically calculating the widths of the arrows based on the data’s distribution.
Here’s a very basic example of creating a Sankey diagram in Python using Matplotlib and Seaborn.
“`python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
Sample data
data = {
‘from’: [‘A’, ‘B’, ‘C’, ‘D’],
‘to’: [‘E’, ‘F’, ‘G’, ‘H’],
‘amount’: [10, 20, 30, 40]
}
df = pd.DataFrame(data)
Sort data
df = df.sort_values(by=’amount’, ascending=False)
Creating the Sankey diagram
ax = sns.sankey()
ax.set_title(‘Example Sankey Diagram’)
sns.sankey(df, ax=ax)
plt.show()
“`
Manual Creation
For those interested in creating a more custom or detailed Sankey diagram, using vector-based graphic design software like Adobe Illustrator or Inkscape can be a more manual but powerful choice. This approach allows for precise control over the placement and appearance of each flow, but requires a bit more effort.
Applications of Sankey Charts
Sankey diagrams are widely used across various fields, including data science, environmental studies, supply chain management, and biology. Their ability to visualize the distribution and interconnection of data makes them particularly effective for:
- Energy Flow Analysis: In energy audits, Sankey diagrams are invaluable for visualizing the flow of energy between different stages of a system.
- Supply Chain Analysis: They help in understanding where and why certain resources are wasted or misallocated within complex supply chains.
- Financial Flows: For financial analysts, Sankey diagrams can be used to visualize the movement of funds among different investment products or within a company’s financial accounts.
- Biological Systems: In bioinformatics, Sankey diagrams are used to depict metabolic pathways and their associated energy and material flows.
Conclusion
Sankey charts are a versatile tool for data visualization, offering a unique way to communicate the intricate details of data flows. Their ability to translate complex data into an understandable and visually engaging format makes them a perfect choice for a wide range of applications. Whether analyzing energy use in a supply chain, tracing the flow of transactions through a financial portfolio, or mapping the metabolic pathways of a living organism, the Sankey chart remains a powerful instrument for grasping the data’s story. As data becomes more abundant and complex, the role of such visualization tools will only grow, making the practice of creating and interpreting Sankey charts an essential skill for data professionals.
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