Unlocking the Power of Sankey Charts: Visualizing Flows and Dependencies in Your Data
Sankey charts have been around for centuries, ever since they were first introduced by mathematician and philosopher, Matthew Henry Phineas Riall Fenwick, in 1852. Yet, this type of visualization is regaining popularity in recent years, as it provides an intriguing and intuitive way to understand multichannel data flows and dependencies within datasets. In this article, we’ll explore the benefits, create a simple Sankey chart using Python, and delve into what makes this chart type so compelling, powerful, and versatile.
Sankey charts excel at illustrating the origin and flow of entities (like energy, sales, traffic, and resources) between different sources or destinations through various pathways, highlighting their relative importance and direction. Their visual nature allows for an immediate understanding of trends, patterns, and relationships within the data, making them an indispensable tool in data visualization.
First and foremost, Sankey charts are particularly advantageous in scenarios where data is complex and multifaceted. By representing flows between nodes through distinct, proportionally scaled arrows, they help in identifying critical pathways, sources, and sinks in system dynamics. This clarity aids in decision-making and strategy formulation in fields such as energy management, economic modeling, marketing analysis, and more.
To demonstrate Sankey charts in action, let’s create a simple example using Python with the popular `matplotlib` and `networkx` libraries. This example will illustrate a hypothetical flow of data between various departments within a company:
“`python
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
# Example data
data = {
‘source’: [‘Sales’, ‘Marketing’, ‘Product’, ‘Support’, ‘Finance’],
‘target’: [‘Operations’, ‘Product’, ‘Development’, ‘Operations’, ‘Accounting’],
‘value’: [120, 80, 150, 70, 90]
}
df = pd.DataFrame(data)
# Create an empty Sankey diagram
sankey = nx.sankeyLayout(nx.Graph())
# Create a Sankey diagram using NetworkX
sankey = nx.make_sankey_dataframe(df)
sankey = sankey.to_dict(orient=’split’)
# Plot the Sankey diagram
plt.figure(figsize=(15, 8))
node_position = nx.circular_layout(sankey[‘nodes’])
edge_position = nx.multipartite_layout(sankey[‘edges’], subset_key=’y’)
pos = {node[‘id’]: node_position[node[‘y’], node[‘x’]]
for node in sankey[‘nodes’]}
# Plot source and target nodes
nodes = nx.draw_networkx_nodes(sankey[‘nodes’], pos, node_size=700, node_color=’skyblue’)
plt.draw()
# Plot source and target labels
labels = dict(zip(sankey[‘nodes’], sankey[‘nodes’]))
texts = nx.draw_networkx_labels(sankey[‘nodes’], pos, labels, font_size=14, font_family=’sans-serif’)
# Plot edges
edges = nx.draw_networkx_edges(sankey[‘edges’], pos, arrowstyle=’fancy’, arrowsize=15, edge_color=’black’, width=2)
plt.draw()
plt.show()
“`
This code generates a Sankey chart using an example dataset of data flows between different company departments. The `networkx` and `matplotlib` libraries work together to visualize not only the flow but also the amounts and relationships.
From the chart generated, we can understand that the ‘Sales’ and ‘Marketing’ departments are the primary sources of information, with ‘Sales’ contributing slightly more. The ‘Product’ and ‘Support’ departments are intermediate steps, processing the information before it reaches the primary destinations: ‘Operations’ and ‘Development’.
The versatility of Sankey charts extends beyond simple flows, as their underlying structures allow for the visualization of data with varying attributes, such as the intensity of flows between nodes, or the addition of colors to mark different categories. Moreover, the interactive capabilities in modern tools like `Plotly` and `D3.js` can enhance user engagement by offering tooltips and hover effects that reveal more detailed information about data flows.
In conclusion, the power of Sankey charts lies in their capacity to transform complex data into visually compelling stories, providing a clear, intuitive insight into multidimensional data flows and interdependencies. With the right tools and a bit of creativity, these charts can unlock a world of possibilities in data visualization, aiding in faster comprehension, more informed decision-making, and clearer communication of data-driven narratives.
