Wrong colors can mislead your audience. A chart with poor color choices confuses rather than clarifies. Viewers struggle to distinguish categories, miss important patterns, or draw incorrect conclusions—all because of color.
Color isn't decoration in data visualization. It's communication. The right palette guides attention, encodes meaning, and makes patterns obvious. The wrong palette creates visual noise.
Consider two pie charts showing the same market share data. One uses random bright colors: magenta, lime green, cyan. The other uses a professional categorical palette with distinct, balanced hues. Same data, completely different impact. The professional version communicates clearly. The other looks like a carnival.
This guide provides 15 proven color palettes organized by purpose:
- Categorical palettes for different groups without order
- Sequential palettes for showing low-to-high values
- Diverging palettes for data with a meaningful center point
Each palette includes hex codes ready to copy, usage guidelines, and example applications. You'll also learn which palettes are colorblind-safe—critical since 8% of men have color vision deficiency.
Stop guessing colors. Start using science-backed palettes that work.
Color Palette Types Explained
Before selecting specific palettes, understand the three main types and when to use each.
Categorical (Qualitative) Palettes
Purpose: Distinguish different groups with no inherent order
Characteristics:
- Maximum color difference between categories
- No implied sequence or ranking
- Each color is equally prominent
- Works for 3-10 distinct categories
When to use:
- Pie charts showing market segments
- Bar charts comparing products
- Line charts with different departments
- Any data where groups are separate, not ordered
Example: Sales by region (North, South, East, West)—no region is "higher" than another, they're just different.
Sequential Palettes
Purpose: Show progression from low to high values
Characteristics:
- Single hue with varying intensity
- Light colors = low values
- Dark colors = high values
- Creates visual hierarchy
When to use:
- Heat maps showing intensity
- Choropleth maps (geographic data)
- Any continuous data with one direction
- Representing magnitude or concentration
Example: Population density by state—lighter blue for sparse areas, darker blue for dense areas.
Diverging Palettes
Purpose: Highlight deviation from a central point
Characteristics:
- Two contrasting hues meeting at neutral center
- Negative values = one color
- Positive values = opposite color
- Center point is meaningful (zero, average, threshold)
When to use:
- Data with positive and negative values
- Showing above/below average
- Temperature anomalies (warmer/cooler)
- Financial gains vs losses
Example: Profit/loss by product—red for losses, white for break-even, green for profits.
Quick Reference
| Data Type | Palette Type | Example |
|---|---|---|
| Different groups, no order | Categorical | Product categories |
| Low to high, one direction | Sequential | Sales volume |
| Negative to positive, center matters | Diverging | Year-over-year change |
5 Best Categorical Palettes
These palettes excel at distinguishing between unordered groups.
1. ColorBrewer Set2
Hex Codes:
- #66c2a5 (teal)
- #fc8d62 (orange)
- #8da0cb (purple-blue)
- #e78ac3 (pink)
- #a6d854 (lime)
- #ffd92f (yellow)
- #e5c494 (beige)
- #b3b3b3 (gray)
Characteristics:
- Muted, professional tones
- High distinguishability
- Works well in academic contexts
- Print-friendly
Best for: Research papers, academic presentations, subdued business reports
Why it works: Balanced saturation prevents any color from dominating. Professional appearance suitable for formal contexts.
2. Tableau 10
Hex Codes:
- #4e79a7 (blue)
- #f28e2c (orange)
- #e15759 (red)
- #76b7b2 (teal)
- #59a14f (green)
- #edc949 (yellow)
- #af7aa1 (purple)
- #ff9da7 (pink)
- #9c755f (brown)
- #bab0ab (gray)
Characteristics:
- Vibrant yet professional
- Industry-standard palette
- 10 distinct colors
- Well-tested for accessibility
Best for: Business dashboards, marketing reports, general-purpose visualization
Why it works: Tableau tested these colors extensively. They remain distinct even when viewed quickly.
3. D3 Category10
Hex Codes:
- #1f77b4 (blue)
- #ff7f0e (orange)
- #2ca02c (green)
- #d62728 (red)
- #9467bd (purple)
- #8c564b (brown)
- #e377c2 (pink)
- #7f7f7f (gray)
- #bcbd22 (olive)
- #17becf (cyan)
Characteristics:
- Web standard for visualization
- Higher saturation than Set2
- Good contrast
- Widely recognized
Best for: Web applications, interactive visualizations, developer tools
Why it works: D3.js is the industry standard for web-based data visualization. These colors are optimized for screen display.
4. IBM Design Language
Hex Codes:
- #6929c4 (purple)
- #1192e8 (blue)
- #005d5d (teal)
- #9f1853 (magenta)
- #fa4d56 (red)
- #570408 (dark red)
- #198038 (green)
- #002d9c (navy)
Characteristics:
- Corporate-friendly
- High contrast
- Accessible design
- Modern appearance
Best for: Enterprise software, corporate presentations, SaaS dashboards
Why it works: IBM's design team created these colors specifically for data visualization with accessibility in mind.
5. Google Charts Default
Hex Codes:
- #3366CC (blue)
- #DC3912 (red)
- #FF9900 (orange)
- #109618 (green)
- #990099 (purple)
- #0099C6 (cyan)
- #DD4477 (pink)
- #66AA00 (lime)
Characteristics:
- Familiar colors
- Clean and simple
- High recognition
- Works across devices
Best for: Internal reports, familiar audiences, quick visualizations
Why it works: Most people have seen these colors in Google products. Familiarity aids comprehension.
5 Best Sequential Palettes
These palettes show intensity or magnitude progressing in one direction.
1. Blues (ColorBrewer)
Hex Codes (light to dark):
- #f7fbff
- #deebf7
- #c6dbef
- #9ecae1
- #6baed6
- #4292c6
- #2171b5
- #08519c
- #08306b
Best for: Heat maps, density maps, any positive-only intensity data
Why it works: Blue is universally understood as "calm to intense." No cultural bias issues. Works in both print and digital.
2. YlOrRd (Yellow-Orange-Red)
Hex Codes (light to dark):
- #ffffcc
- #ffeda0
- #fed976
- #feb24c
- #fd8d3c
- #fc4e2a
- #e31a1c
- #bd0026
- #800026
Best for: Temperature data, urgency levels, attention-grabbing metrics
Why it works: Mimics natural heat perception. Yellow = mild, Red = intense. Immediately intuitive.
3. Viridis
Hex Codes (light to dark):
- #fde725
- #b5de2b
- #6ece58
- #35b779
- #1f9e89
- #26828e
- #31688e
- #3e4989
- #482878
- #440154
Characteristics:
- Perceptually uniform
- Colorblind-safe
- Scientific standard
- Prints well in grayscale
Best for: Scientific publications, any visualization requiring accessibility, heat maps
Why it works: Designed mathematically to have uniform perceptual changes. What you see matches the data accurately.
4. Plasma
Hex Codes (light to dark):
- #f0f921
- #fdb42f
- #ed7953
- #cc4778
- #9c179e
- #7201a8
- #46039f
- #0d0887
Best for: High-contrast needs, presentations on projectors, data with wide ranges
Why it works: Maximum perceptual contrast while maintaining color order. Stands out clearly.
5. Inferno
Hex Codes (light to dark):
- #fcffa4
- #fca50a
- #dd513a
- #a71e5d
- #7a0c7f
- #420a68
- #140e36
- #000004
Best for: Dark mode interfaces, dramatic visualizations, scientific data
Why it works: Dark background friendly. Creates striking visuals while maintaining data integrity.
5 Best Diverging Palettes
These palettes show deviation from a meaningful center point.
1. RdBu (Red-Blue)
Hex Codes (negative to positive):
- #67001f (dark red)
- #b2182b (red)
- #d6604d (light red)
- #f4a582 (pink)
- #fddbc7 (light pink)
- #f7f7f7 (white - center)
- #d1e5f0 (light blue)
- #92c5de (sky blue)
- #4393c3 (blue)
- #2166ac (dark blue)
Best for: Temperature anomalies, comparison to baseline, standard diverging needs
Why it works: Red and blue are perceptual opposites. Clear visual separation with neutral center.
2. BrBG (Brown-Blue-Green)
Hex Codes:
- #543005 (dark brown)
- #8c510a (brown)
- #bf812d (tan)
- #dfc27d (light tan)
- #f6e8c3 (cream)
- #f5f5f5 (white - center)
- #c7eae5 (light green)
- #80cdc1 (teal)
- #35978f (green-blue)
- #01665e (dark teal)
Best for: Environmental data, land use changes, earth science
Why it works: Natural color associations. Brown = dry/arid, Green = lush/moist.
3. PiYG (Pink-Yellow-Green)
Hex Codes:
- #8e0152 (dark magenta)
- #c51b7d (magenta)
- #de77ae (pink)
- #f1b6da (light pink)
- #fde0ef (very light pink)
- #f7f7f7 (white - center)
- #e6f5d0 (very light green)
- #b8e186 (light green)
- #7fbc41 (green)
- #4d9221 (dark green)
Best for: Softer contrast needs, alternative to red-blue, general diverging
Why it works: Less harsh than red-blue while maintaining clear divergence.
4. PRGn (Purple-Green)
Hex Codes:
- #40004b (dark purple)
- #762a83 (purple)
- #9970ab (light purple)
- #c2a5cf (lavender)
- #e7d4e8 (very light purple)
- #f7f7f7 (white - center)
- #d9f0d3 (very light green)
- #a6dba0 (light green)
- #5aae61 (green)
- #1b7837 (dark green)
Characteristics:
- Colorblind-safe diverging option
- Clear distinction
- Professional appearance
Best for: Accessibility-critical visualizations, presentations to diverse audiences
Why it works: Purple and green remain distinguishable for most colorblind users.
5. RdYlGn (Red-Yellow-Green)
Hex Codes:
- #a50026 (dark red)
- #d73027 (red)
- #f46d43 (orange-red)
- #fdae61 (orange)
- #fee08b (light yellow)
- #ffffbf (yellow - center)
- #d9ef8b (yellow-green)
- #a6d96a (light green)
- #66bd63 (green)
- #1a9850 (dark green)
Caution: Red-green combinations are problematic for colorblind users.
Best for: Traffic light metaphors (with caution), performance indicators (when accessibility not critical)
Why it works: Familiar semantics—red=bad, yellow=neutral, green=good. But use only when you're certain about audience accessibility.
Colorblind-Safe Palettes
Approximately 8% of men and 0.5% of women have color vision deficiency. Red-green colorblindness (deuteranopia) is most common.
Why Colorblind Safety Matters
- Your chart may be viewed by 1 in 12 male viewers who can't distinguish red from green
- Professional obligation to accessible design
- Better design for everyone (clear distinctions help all users)
Recommended Colorblind-Safe Options
Categorical:
- Tableau 10 (designed with accessibility)
- IBM Design Language
- Colorbrewer Set2
Sequential:
- Viridis (mathematically designed for colorblindness)
- Cividis (specifically for colorblind users)
- Blues (single hue avoids confusion)
Diverging:
- PRGn (purple-green)
- Blue-Orange diverging
- Brown-Teal combinations
Colors to Avoid Together
- Red and green (most problematic)
- Green and brown (similar appearance to deuteranopes)
- Blue and purple (can blend for tritanopes)
- Pink and gray (low contrast for some)
Testing Your Palettes
Before publishing, test your visualization:
- Coblis (color-blindness-simulator.com) - Upload your chart image
- ColorOracle - Desktop app that simulates colorblindness
- Chrome DevTools - Rendering > Emulate vision deficiencies
- Viz Palette - Interactive testing tool
Pro Tip: If your chart relies solely on color to convey meaning, add patterns, labels, or shapes as secondary encoding.
How to Apply Colors in CleanChart
CleanChart makes applying professional color palettes straightforward.
Using Built-in Palettes
- Upload your data
- Select chart type
- Navigate to Color Settings
- Choose from pre-loaded palettes: Tableau 10, ColorBrewer options, Viridis family, Custom options
- Preview changes in real-time
- Export with your chosen colors
Custom Hex Code Input
For specific brand or preference colors:
- Click Custom Color option
- Enter hex codes directly (e.g., #4e79a7)
- Assign to specific data series or categories
- Save as custom palette for reuse
Maintaining Consistency
When creating multiple charts for the same report:
- Save your palette: Create a custom palette in CleanChart
- Document colors: Keep a reference of hex codes used
- Reuse across charts: Apply same palette to maintain visual consistency
- Consider context: Same colors should mean same things throughout your report
Common Color Mistakes
Avoid these frequent errors that undermine your visualizations.
1. Too Many Colors (Rainbow Effect)
Problem: Using 10+ colors in a single chart creates visual chaos.
Solution: Limit to 5-7 colors maximum. Group minor categories into "Other."
2. Low Contrast Backgrounds
Problem: Light colors on light backgrounds or dark on dark backgrounds.
Solution: Ensure sufficient contrast. White or very light gray backgrounds work best.
3. Red-Green for Important Distinctions
Problem: 8% of male viewers can't distinguish these colors.
Solution: Use blue-orange or purple-green alternatives.
4. Sequential Palette for Categorical Data
Problem: Implying order where none exists (light blue, medium blue, dark blue for unordered categories).
Solution: Use categorical palette with distinct hues.
5. Inconsistent Colors Across Report
Problem: "Sales" is blue in chart 1, orange in chart 2, green in chart 3.
Solution: Same data = same color throughout entire report or presentation.
6. Ignoring Color Psychology
Problem: Using red for positive metrics or green for negative.
Solution: Align colors with cultural expectations. Red typically signals warning/negative.
7. 3D Effects and Gradients Within Data Points
Problem: 3D pie charts distort perception. Gradient fills obscure true values.
Solution: Flat, solid colors. Let data speak, not effects.
Resources and Tools
Build your color palette toolkit with these resources.
ColorBrewer 2.0
URL: colorbrewer2.org
The gold standard for data visualization palettes. Academic-backed, tested for effectiveness. Export hex codes directly.
Coolors.co
URL: coolors.co
Generate and explore color schemes. Export in multiple formats. Great for creating custom palettes.
Adobe Color
URL: color.adobe.com
Explore color relationships. Accessibility tools built-in. Sync with Adobe products.
Viz Palette
URL: projects.susielu.com/viz-palette
Test palettes specifically for data visualization. See how colors work together in actual chart contexts.
Frequently Asked Questions
How many colors should I use maximum?
Short answer: 5-7 colors for most charts.
Details:
- Categorical charts: Maximum 7-10 distinct colors before confusion
- Sequential/diverging: 5-9 steps typically sufficient
- Human perception limits: We struggle to distinguish more than 7 categories
- If you need more: Consider grouping, filtering, or small multiples
What's the best background color for charts?
Recommendation: White (#FFFFFF) or very light gray (#F5F5F5)
Why:
- Maximum contrast with data colors
- Professional appearance
- Print-friendly
- Reduces eye strain
Avoid: Dark backgrounds unless specifically designing for dark mode interfaces.
Should I use brand colors in data viz?
It depends:
Yes, when:
- Brand colors have sufficient contrast
- They don't conflict with data meaning
- Accessibility is maintained
- You adapt them specifically for visualization
No, when:
- Brand colors are too similar (multiple blues)
- They create accessibility issues
- Cultural color meanings conflict with data
Solution: Create a visualization-specific palette inspired by brand colors but optimized for data.
How do I test for colorblind accessibility?
Tools:
- Coblis - Upload image, see simulation
- Color Oracle - System-wide color filter
- Chrome DevTools - Built-in vision deficiency emulator
- Stark Plugin - Figma/Sketch accessibility checker
Quick test: Convert your chart to grayscale. If you can still distinguish all categories, color isn't the only encoding—good sign.
Can I use gradients in charts?
Generally avoid for data encoding.
Problems with gradients:
- Harder to judge exact values
- Can create false patterns
- Printing issues
Acceptable uses:
- Background decoration (subtle)
- Sequential heat maps (careful implementation)
- Aesthetic enhancement that doesn't encode data
Best colors for dark mode charts?
Adjust your approach:
- Reduce saturation slightly (prevent eye strain)
- Use lighter versions of colors
- Ensure sufficient contrast against dark background
- Test Viridis, Plasma, or Inferno (designed for this)
- Avoid pure white text/lines (use #E0E0E0)
Recommended dark mode categorical: Muted versions of standard palettes with increased brightness.
Related Articles
Deepen your data visualization knowledge:
- Colorblind-Accessible Charts - Design for all viewers
- Chart Types Explained - Complete guide to chart selection
- CSV to Chart Tutorial - Start visualizing your data
- Data Visualization for Beginners - Foundation concepts
Apply These Palettes Today
Color is a powerful tool in data visualization—when used correctly. The 15 palettes in this guide have been tested, proven, and widely adopted because they work.
Your action plan:
- Identify your data type: Categorical, sequential, or diverging?
- Select appropriate palette: Match palette type to data type
- Consider accessibility: Is your palette colorblind-safe?
- Test before publishing: Use simulation tools
- Maintain consistency: Same colors mean same things
Stop guessing. Start using palettes that communicate clearly.