Data Visualization

---
name: data-visualization
description: Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
user-invocable: false
---

# Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

## Chart Selection Guide

### Choose by Data Relationship

| What You're Showing | Best Chart | Alternatives |
|---|---|---|
| **Trend over time** | Line chart | Area chart (if showing cumulative or composition) |
| **Comparison across categories** | Vertical bar chart | Horizontal bar (many categories), lollipop chart |
| **Ranking** | Horizontal bar chart | Dot plot, slope chart (comparing two periods) |
| **Part-to-whole composition** | Stacked bar chart | Treemap (hierarchical), waffle chart |
| **Composition over time** | Stacked area chart | 100% stacked bar (for proportion focus) |
| **Distribution** | Histogram | Box plot (comparing groups), violin plot, strip plot |
| **Correlation (2 variables)** | Scatter plot | Bubble chart (add 3rd variable as size) |
| **Correlation (many variables)** | Heatmap (correlation matrix) | Pair plot |
| **Geographic patterns** | Choropleth map | Bubble map, hex map |
| **Flow / process** | Sankey diagram | Funnel chart (sequential stages) |
| **Relationship network** | Network graph | Chord diagram |
| **Performance vs. target** | Bullet chart | Gauge (single KPI only) |
| **Multiple KPIs at once** | Small multiples | Dashboard with separate charts |

### When NOT to Use Certain Charts

- **Pie charts**: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
- **3D charts**: Never. They distort perception and add no information.
- **Dual-axis charts**: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
- **Stacked bar (many categories)**: Hard to compare middle segments. Use small multiples or grouped bars instead.
- **Donut charts**: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.

## Design Principles

### Color

- **Use color purposefully**: Color should encode data, not decorate
- **Highlight the story**: Use a bright accent color for the key insight; grey everything else
- **Sequential data**: Use a single-hue gradient (light to dark) for ordered values
- **Diverging data**: Use a two-hue gradient with neutral midpoint for data with a meaningful center
- **Categorical data**: Use distinct hues, maximum 6-8 before it gets confusing
- **Avoid red/green only**: 8% of men are red-green colorblind. Use blue/orange as primary pair

### Typography

- **Title states the insight**: "Revenue grew 23% YoY" beats "Revenue by Month"
- **Subtitle adds context**: Date range, filters applied, data source
- **Axis labels are readable**: Never rotated 90 degrees if avoidable. Shorten or wrap instead
- **Data labels add precision**: Use on key points, not every single bar
- **Annotation highlights**: Call out specific points with text annotations

### Layout

- **Reduce chart junk**: Remove gridlines, borders, backgrounds that don't carry information
- **Sort meaningfully**: Categories sorted by value (not alphabetically) unless there's a natural order (months, stages)
- **Appropriate aspect ratio**: Time series wider than tall (3:1 to 2:1); comparisons can be squarer
- **White space is good**: Don't cram charts together. Give each visualization room to breathe

### Accuracy

- **Bar charts start at zero**: Always. A bar from 95 to 100 exaggerates a 5% difference
- **Line charts can have non-zero baselines**: When the range of variation is meaningful
- **Consistent scales across panels**: When comparing multiple charts, use the same axis range
- **Show uncertainty**: Error bars, confidence intervals, or ranges when data is uncertain
- **Label your axes**: Never make the reader guess what the numbers mean

## Accessibility Considerations

### Color Blindness

- Never rely on color alone to distinguish data series
- Add pattern fills, different line styles (solid, dashed, dotted), or direct labels
- Test with a colorblind simulator (e.g., Coblis, Sim Daltonism)
- Use the colorblind-friendly palette: `sns.color_palette("colorblind")`

### Screen Readers

- Include alt text describing the chart's key finding
- Provide a data table alternative alongside the visualization
- Use semantic titles and labels

### General Accessibility

- Sufficient contrast between data elements and background
- Text size minimum 10pt for labels, 12pt for titles
- Avoid conveying information only through spatial position (add labels)
- Consider printing: does the chart work in black and white?

### Accessibility Checklist

Before sharing a visualization:
- [ ] Chart works without color (patterns, labels, or line styles differentiate series)
- [ ] Text is readable at standard zoom level
- [ ] Title describes the insight, not just the data
- [ ] Axes are labeled with units
- [ ] Legend is clear and positioned without obscuring data
- [ ] Data source and date range are noted