Seaborn Palettes: How to Prevent Color Recycling When Using More Than 6 Colors

Table of Contents#

1. Understanding Seaborn Palettes
2. Why Color Recycling Happens
3. How to Prevent Color Recycling: 4 Proven Methods
   • Method 1: Use Qualitative Palettes with More Colors
   • Method 2: Create Custom Qualitative Palettes
   • Method 3: Leverage Sequential/Diverging Colormaps (With Caution)
   • Method 4: Adjust Hue, Saturation, and Lightness
4. Best Practices for Categorical Color Use
5. Troubleshooting: When Colors Still Clash
6. Conclusion
7. References

1. Understanding Seaborn Palettes#

Seaborn palettes are pre-defined sets of colors tailored for different types of data. To avoid color recycling, it’s critical to first understand the three main palette types:

Qualitative Palettes#

Designed for unordered categorical data (e.g., product types, countries, job roles). Colors are distinct and evenly spaced to avoid implying order. Examples: deep, muted, pastel, bright, dark, colorblind.

Limitation: Most default qualitative palettes in Seaborn have only 6–8 distinct colors. For example, deep and muted have 8 colors; beyond that, Seaborn repeats the sequence.

Sequential Palettes#

For ordered/numeric data (e.g., temperature, sales revenue, time). Colors progress from light to dark (or vice versa) to show magnitude. Examples: viridis, plasma, inferno, magma, Blues.

Diverging Palettes#

For numeric data with a midpoint (e.g., profit/loss, temperature anomalies). Colors diverge from a neutral midpoint to two extremes. Examples: coolwarm, RdBu, BrBG.

Color recycling is most problematic with qualitative palettes, as they’re the go-to for categorical data. Let’s explore why this happens.

2. Why Color Recycling Happens#

Seaborn’s default qualitative palettes are intentionally limited in size to ensure colors are visually distinct. For example:

• sns.color_palette("deep") returns 8 colors.
• sns.color_palette("muted") returns 8 colors.
• sns.color_palette("pastel") returns 8 colors.

When you plot more categories than the palette’s color count, Seaborn cycles back to the start of the palette. For example, if you plot 10 categories with deep, the 9th category will reuse the 1st color, and the 10th will reuse the 2nd.

Example: Color Recycling in Action

import seaborn as sns  
import matplotlib.pyplot as plt  
 
# Sample data with 10 categories  
categories = [f"Category {i}" for i in range(1, 11)]  
values = [15, 22, 18, 25, 19, 30, 22, 28, 21, 24]  
 
# Use default "deep" palette (8 colors)  
sns.barplot(x=categories, y=values)  
plt.title("Default 'deep' Palette (8 Colors) – Recycling Occurs!")  
plt.xticks(rotation=45)  
plt.show()  

Result: Categories 9 and 10 will reuse the colors of Categories 1 and 2, making the plot ambiguous.

3. How to Prevent Color Recycling: 4 Proven Methods#

Let’s dive into actionable solutions to generate more than 6–8 distinct colors for categorical data.

Method 1: Use Qualitative Palettes with More Colors#

Seaborn includes lesser-known qualitative palettes with 10+ distinct colors. These are ideal for quick fixes when you need more categories without custom work.

Key Palettes to Use#

Example: Using tab20 for 15 Categories#

import seaborn as sns  
import matplotlib.pyplot as plt  
 
# Set palette to "tab20" (20 colors)  
sns.set_palette("tab20")  
 
# Plot 15 categories  
categories = [f"Item {i}" for i in range(1, 16)]  
values = [10 + i*2 for i in range(15)]  
 
sns.barplot(x=categories, y=values)  
plt.title("'tab20' Palette (20 Colors) – No Recycling!")  
plt.xticks(rotation=90)  
plt.show()  

Why it works: tab20 uses 20 distinct hues, so 15 categories will all have unique colors.

Example: husl for Custom N Colors#

The husl palette generates colors using the HUSL color space (Hue, Saturation, Lightness), ensuring uniform perceptual spacing. Use sns.husl_palette(n_colors=N) to generate exactly N distinct colors:

# Generate 12 distinct colors with husl  
palette = sns.husl_palette(n_colors=12, s=0.8, l=0.6)  # s=saturation, l=lightness  
sns.set_palette(palette)  
 
# Plot 12 categories  
sns.barplot(x=[f"Group {i}" for i in range(12)], y=[i*3 for i in range(12)])  
plt.title("12 Custom Colors with husl_palette")  
plt.xticks(rotation=45)  
plt.show()  

Method 2: Create Custom Qualitative Palettes#

For full control over colors (e.g., brand consistency, accessibility), create a custom palette using hex codes, RGB values, or named colors.

Step 1: Choose Colors#

Use tools like Coolors or ColorBrewer to pick distinct hex codes. For example, a custom palette for 10 categories might look like:
["#FF6B6B", "#4ECDC4", "#FFD166", "#06D6A0", "#118AB2", "#073B4C", "#EF476F", "#FF8C42", "#6A0572", "#393E46"].

Step 2: Load the Custom Palette in Seaborn#

Use sns.color_palette() to register your custom colors:

custom_palette = [  
    "#FF6B6B", "#4ECDC4", "#FFD166", "#06D6A0", "#118AB2",  
    "#073B4C", "#EF476F", "#FF8C42", "#6A0572", "#393E46"  
]  
 
# Set the custom palette  
sns.set_palette(custom_palette)  
 
# Verify the palette (optional)  
sns.palplot(custom_palette)  # Plots color swatches  
plt.title("Custom Palette with 10 Distinct Colors")  
plt.show()  

Pro Tip: Generate Colors Programmatically#

For large N (e.g., 50 categories), use matplotlib.colors to generate a gradient or sample from a color space:

from matplotlib.colors import LinearSegmentedColormap  
 
# Generate 20 colors from a custom gradient  
cmap = LinearSegmentedColormap.from_list("custom_gradient", ["#003f5c", "#58508d", "#bc5090", "#ff6361", "#ffa600"])  
custom_palette = cmap(np.linspace(0, 1, 20))  # Sample 20 colors  
 
sns.set_palette(custom_palette)  

Method 3: Leverage Sequential/Diverging Colormaps (With Caution)#

Sequential and diverging palettes are designed for numeric data, but they can be repurposed for ordered categorical data (e.g., low/medium/high priority, time periods). Use sns.color_palette(palette_name, n_colors=N) to sample N distinct colors from a colormap.

Example: Sampling from viridis for 10 Ordered Categories#

# Sample 10 colors from the "viridis" sequential colormap  
palette = sns.color_palette("viridis", n_colors=10)  
sns.set_palette(palette)  
 
# Plot ordered categories (e.g., "Q1" to "Q10")  
categories = [f"Q{i}" for i in range(1, 11)]  
values = [i*5 for i in range(10)]  
 
sns.barplot(x=categories, y=values)  
plt.title("10 Colors Sampled from 'viridis' (Sequential)")  
plt.xticks(rotation=45)  
plt.show()  

Caveat: Avoid sequential/diverging palettes for unordered categories (e.g., "Apple", "Banana", "Cherry"). They imply magnitude, which can mislead readers.

Method 4: Adjust Hue, Saturation, and Lightness#

For palettes like husl or hsv, tweak saturation (s) and lightness (l) to make colors more distinct. This is especially useful when recycling is unavoidable (e.g., 30+ categories).

Example: husl with Adjusted Saturation#

# Generate 15 colors with lower saturation for better readability  
palette = sns.husl_palette(n_colors=15, s=0.6, l=0.7)  # s=0.6 (less saturated), l=0.7 (lighter)  
sns.set_palette(palette)  
 
sns.barplot(x=[f"Label {i}" for i in range(15)], y=[i*2 for i in range(15)])  
plt.title("HUSL Palette with Adjusted Saturation/Lightness")  
plt.xticks(rotation=90)  
plt.show()  

4. Best Practices for Categorical Color Use#

To ensure your colors are effective:

• Prioritize Accessibility: Use tools like WebAIM Contrast Checker to ensure readability. Seaborn’s colorblind palette is designed for viewers with color vision deficiencies.
• Limit Categories When Possible: If you have 50+ categories, consider grouping small categories into an "Other" bucket. Too many colors overwhelm readers.
• Test with Grayscale: Print your plot in grayscale to ensure colors don’t blend (critical for publications).
• Use sns.palplot() to Preview: Always check your palette with sns.palplot(sns.color_palette()) before plotting to spot clashing colors.

5. Troubleshooting: When Colors Still Clash#

If colors are hard to distinguish:

• Reduce Saturation: Overly bright colors (e.g., hsv) can blur together. Use s=0.5–0.7 in husl_palette.
• Increase Lightness Spread: In husl_palette, vary l (e.g., l=(0.4, 0.8) for a range of lightness).
• Combine with Shapes/Patterns: For overlapping categories, add markers (e.g., sns.scatterplot(style=category)) or hatches (e.g., plt.bar(..., hatch="/")).

6. Conclusion#

Color recycling in Seaborn is a common hurdle, but it’s easily avoidable with the right tools. By using extended palettes like tab20, creating custom color sets, or sampling from colormaps, you can visualize 10, 20, or even 50+ categories with distinct colors. Remember to prioritize accessibility and test palettes for readability—your audience will thank you for clear, professional visualizations.

7. References#

• Seaborn Palette Documentation
• Matplotlib Colormaps
• ColorBrewer 2.0 (Color selection tool)
• HUSL Color Space (Perceptually uniform color generation)
• Coolors (Custom palette creation)