data-visualization

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---
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.

## Python Visualization Code Patterns

### Setup and Style

```python
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
import pandas as pd
import numpy as np

# Professional style setup
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 150,
    'font.size': 11,
    'axes.titlesize': 14,
    'axes.titleweight': 'bold',
    'axes.labelsize': 11,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.titlesize': 16,
})

# Colorblind-friendly palettes
PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
PALETTE_SEQUENTIAL = 'YlOrRd'
PALETTE_DIVERGING = 'RdBu_r'
```

### Line Chart (Time Series)

```python
fig, ax = plt.subplots(figsize=(10, 6))

for label, group in df.groupby('category'):
    ax.plot(group['date'], group['value'], label=label, linewidth=2)

ax.set_title('Metric Trend by Category', fontweight='bold')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend(loc='upper left', frameon=True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Format dates on x-axis
fig.autofmt_xdate()

plt.tight_layout()
plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')
```

### Bar Chart (Comparison)

```python
fig, ax = plt.subplots(figsize=(10, 6))

# Sort by value for easy reading
df_sorted = df.sort_values('metric', ascending=True)

bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])

# Add value labels
for bar in bars:
    width = bar.get_width()
    ax.text(width + 0.5, bar.get_y() + bar.ge