# Unleashing Insights through Visual Dynamics: A Comprehensive Guide to Creating and Interpreting Sankey Charts
Sankey charts or Sankey diagrams, named after Thomas Sankey—an engineer who used this representation to illustrate energy and material flows in processes—have become a crucial tool for data visualization. They depict the quantitative values and flow dynamics associated with a system, making complex data patterns more accessible and easier to understand. In this article, we’ll explore the creation and interpretation of Sankey charts, highlighting their versatility and power in visualizing data dynamics.
## What are Sankey Charts?
At their core, Sankey diagrams display the transfer of quantities in a multi-step process or from one state to another. They are essentially flow diagrams that use different width arrows to represent the magnitude of the flow. These arrows, connected by nodes, make it easier to show the proportional representation of source, flow, and destination.
Sankey charts are ideal for scenarios where the magnitude of transitions or flows represents the importance of the data being represented. For example, they can be used to visualize energy consumption, the flow of money in financial transactions, or even the flow of visitors to a website from various sources.
## How to Create Sankey Charts
### Step 1: Define Your Data
The first step to creating a Sankey chart involves collecting your data accurately. This data should have at least three columns: the source, the path, and the target, with an associated magnitude for the flow between each pair of source and target.
Here’s a simple step-to-step guide on how you can create a Sankey chart using Python and the `networkx` and `matplotlib` libraries:
1. **Install Libraries**: If you haven’t already, install the libraries using pip or your preferred package manager.
“`bash
pip install networkx matplotlib pandas
“`
2. **Load Your Data**: Use pandas to read the data. Your dataset should have columns like `source`, `target`, `weight` (optional) for the magnitude, and any other variables you’re comparing.
“`python
import pandas as pd
data = pd.read_csv(‘data.csv’)
“`
3. **Convert Data**: Convert your data into a format that `networkx` can understand. This might involve creating a list of tuples representing your edges, which are then added to a network structure.
“`python
import networkx as nx
edge_list = list(zip(data[‘source’], data[‘target’], data[‘weight’]))
G = nx.MultiDiGraph()
G.add_weighted_edges_from(edge_list)
“`
4. **Visualize with Sankey Diagram**: Now that you have your network graph ready, you can visualize it in a Sankey diagram format.
“`python
import matplotlib.pyplot as plt
# To display the diagram using Sankey diagram library
edge_label = nx.get_edge_attributes(G,’weight’)
fig, ax = plt.subplots()
node_pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G,pos=node_pos)
c= nx.draw_networkx_edges(G,pos=node_pos,edgelist=G.edges(),edge_color=’b’,edge_cmap=plt.cm.Blues,width=6)
s=nx.draw_networkx_edges(G,pos=node_pos,edgelist=sorted([(t,s,w) for s,t,w in G.edges(data=True)], reverse=True),
edge_color=’r’,edge_cmap=plt.cm.Reds,width=6)
nx.draw_networkx_labels(G,pos=node_pos)
edges=nx.draw_networkx_edge_labels(G,pos=node_pos,edge_labels=edge_label)
plt.title(“Sankey Diagram”)
c.ax.figure.colorbar(c,cax=ax)
s=ax.inset_axes([0.5, 0.2, 0.5, 0.5])
s.set_facecolor(‘1.0’)
c.set_positions([[0.95,1-0.05*i,i,0] for i in range(c.orientation(0),c.orientation(1),2)])
c.remove()
_ = plt.legend((c.collections[0],s.collections[0]), (‘Edge color’, ‘Node color’), loc=”upper right”)
plt.show()
“`
### Step 2: Customize Your Chart
Once the basic structure is in place, you can customize various aspects such as color, edge, node appearance, to effectively communicate your insights.
### Step 3: Review and Iterate
Finally, review the chart’s clarity and effectiveness in conveying your intended message. Iterate as necessary to improve readability and impact.
## How to Interpret Sankey Charts
Interpreting Sankey diagrams boils down to understanding flow patterns, the magnitude of flows, and the relative importance of sources and destinations based on the width of the flows. Here are some key points to focus on:
– **Magnitude and Width**: The width of the flow lines represents the magnitude or volume of data being transferred. Thicker lines indicate larger quantities.
– **Direction**: The direction of the arrows reveals the flow direction. This is crucial for understanding the pathway of data.
– **Sources and Targets**: Identify the major sources and targets. This helps in understanding where the data originates and where it is going to.
– **Path Analysis**: Break down the flows into their constituent paths to see how the total flow is distributed from source to target. This can reveal important segments within your data.
– **Proportional Insights**: Pay attention to branch sizes to understand the relative importance of the branches in the flow. This can be indicative of efficiency, significance, or areas needing attention in the process.
By mastering the creation and interpretation of Sankey charts, you’re equipping yourself with a powerful visualization tool that can help elucidate complex data relationships, making it an indispensable skill in the era of big data visualization.
