Controlling Node Colors in Sankey Diagrams with flipPlots: A Comprehensive Guide
Creating visually appealing Sankey diagrams with flipPlots is easy, but sometimes you might want more control over the color of your internal nodes. This article will guide you through customizing node colors in your Sankey diagrams, providing a clear and concise explanation with practical examples.
The Problem: Unwanted Default Node Colors
Let's say you're visualizing data flow with a Sankey diagram using flipPlots. You've meticulously crafted your data and have the diagram looking great, but you're not happy with the default color scheme of your internal nodes. The default colors might not be visually distinct enough or may not align with your chosen theme. This is where the need for customization arises.
Example Scenario: Customizing Node Colors
Imagine you have a dataset depicting energy flow in a city, with nodes representing different energy sources (solar, wind, coal, etc.). You want to visually differentiate these energy sources with distinct colors.
Original Code (without node color customization):
import flipPlots as fp
data = {
"nodes": ["Solar", "Wind", "Coal", "Hydro", "Energy Grid"],
"links": [
{"source": "Solar", "target": "Energy Grid", "value": 100},
{"source": "Wind", "target": "Energy Grid", "value": 80},
{"source": "Coal", "target": "Energy Grid", "value": 150},
{"source": "Hydro", "target": "Energy Grid", "value": 60},
]
}
fp.sankey(data)
This code will generate a Sankey diagram with the default color scheme for the nodes, which may not be suitable for your visual representation.
The Solution: Customizing Node Colors with node_colors
flipPlots offers a simple solution to this problem: the node_colors parameter. By providing a list of colors corresponding to your nodes in the node_colors parameter, you can directly control their colors in the Sankey diagram.
Modified Code with Custom Node Colors:
import flipPlots as fp
data = {
"nodes": ["Solar", "Wind", "Coal", "Hydro", "Energy Grid"],
"links": [
{"source": "Solar", "target": "Energy Grid", "value": 100},
{"source": "Wind", "target": "Energy Grid", "value": 80},
{"source": "Coal", "target": "Energy Grid", "value": 150},
{"source": "Hydro", "target": "Energy Grid", "value": 60},
]
}
# Define node colors
node_colors = ["#ffff00", "#00b050", "#808080", "#0080ff", "#800080"]
fp.sankey(data, node_colors=node_colors)
This code will generate the same Sankey diagram but will now display the nodes with the defined colors:
- Solar: #ffff00 (Yellow)
- Wind: #00b050 (Green)
- Coal: #808080 (Gray)
- Hydro: #0080ff (Blue)
- Energy Grid: #800080 (Purple)
Additional Tips and Considerations
- Color Palette: It's recommended to use a coherent and aesthetically pleasing color palette for your node colors. Explore online resources for color palettes and consider your target audience's perception of the chosen colors.
- Color Blindness: Be mindful of color blindness when selecting your palette. Ensure that the chosen colors are easily distinguishable for people with color vision deficiencies. Tools like Coblis can help you test your color combinations.
- Transparency: You can adjust the transparency of your node colors using an alpha value (e.g., "#ffff0080" for a semi-transparent yellow). This can add depth and visual interest to your Sankey diagram.
- Node Labels: If you want to label your nodes, flipPlots provides the
node_labelsparameter. Pair your custom node colors with descriptive labels to enhance clarity and understanding.
Conclusion
Controlling the colors of internal nodes in your flipPlots Sankey diagram is an essential step to creating clear and visually appealing visualizations. By utilizing the node_colors parameter and following these tips, you can create impactful Sankey diagrams that communicate your data effectively. Remember to consider your audience, color palettes, and color accessibility to ensure your diagrams are easily understood and interpreted.
