Creating Sankey Charts: A Comprehensive Guide to Data Flow Visualizations
In the ever-growing world of data analysis, it’s essential to have a clear understanding of how information moves through different systems and processes. Sankey charts are a powerful tool for visualizing complex data flows. They are highly suited for depicting the flow of materials, energy, cost, or any other kind of flow. In this tutorial, we will go through the steps to create Sankey charts, enabling you to visualize and comprehend intricate data flows.
**Understanding Sankey Charts**
Before diving into the creation process, it’s important to have a grasp of how Sankey charts work. These charts are a type of flow diagram where the magnitude of a flow is represented by the width of the arrows. This non-linear scale helps to show the relative size of flows and helps in identifying bottlenecks and inefficiencies.
**Tools for Creating Sankey Charts**
To create Sankey charts, you have various tools at your disposal, ranging from traditional software like Microsoft Excel and Adobe Illustrator to specialized tools such as Sankey diagram generators and programming libraries within data science frameworks.
For the sake of this tutorial, we will focus on creating Sankey charts using the Python library `matplotlib` with the `sankey` module.
**Step-by-Step Guide to Creating a Sankey Chart in Python**
1. **Install Necessary Libraries**
First, ensure that Python and the required libraries are installed on your system. You’ll need the following:
“`
pip install matplotlib seaborn pandas
“`
2. **Prepare Your Data**
You’ll need data that can be organized into flows. This data usually includes inputs, processing steps, and outputs. Ensure the data is in a Pandas DataFrame with specific columns representing the flow rates, process names, and labels for the flows.
3. **Load the Data**
Import your data into a Pandas DataFrame. Here’s an example structure of your data:
“`python
import pandas as pd
data = {
‘Process’: [‘A’, ‘B’, ‘C’, ‘A’, ‘D’, ‘E’, ‘E’, ‘F’],
‘Flow out’: [8, 3, 2, 15, 4, 3, 1, 0],
‘Flow in’: [15, 8, 1, 8, 10, 5, 5],
}
df = pd.DataFrame(data)
“`
4. **Create the Chart**
Now, let the Sankey diagram come to life using the following code:
“`python
import matplotlib.pyplot as plt
import matplotlib.sankey as sank
# Set up the figure and the axes
fig, ax = plt.subplots()
# Create the Sankey diagram instance
sankey = sank.Sankey(ax=ax, units=”tons”,)
# Add the rectangles for each process
sankey.add_rectangles(nodes=df[‘Process’], widths=0.15)
# Draw the flow arrows
sankey.draw_nodes(arrows=df[‘Flow out’], directions=df[‘Flow in’], width=0.8, color=”skyblue”)
# Set the width of the incoming and outgoing flows to be the same
sankey.set_flow_widths_equivalent()
# Set the limits of the Sankey chart
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)
# Plot the diagram
plt.show()
“`
5. **Customize the Chart**
Feel free to customize the chart further by adjusting colors, labels, and the direction of the flow arrows to fit the requirements of your data analysis and presentation style.
**Conclusion**
Sankey charts provide an efficient means of visualizing complex data flows. By following the steps outlined in this tutorial, you should now be able to create Sankey charts with Python and matplotlib to help your audience understand the dynamic nature of your data. Whether you are analyzing the energy usage in a manufacturing process, the information flow within a data processing pipeline, or the movement of resources within a company, Sankey charts are an excellent choice for data visualization.
