Data Visualization

AnthropicOfficialPublished by Anthropic

Skill
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Data and AI
Apache-2.0
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From the author

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.

— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive reference for generating data visualizations in Python. It includes a chart selection guide based on data relationships, code patterns for common chart types, and design principles for clarity and accessibility. It covers best practices for color usage, typography, layout, and ensuring visualizations are accessible to users with color blindness or those using screen readers.

What it does

Provides a decision matrix for selecting the appropriate chart type based on data relationships.
Offers reusable Python code patterns for Matplotlib, Seaborn, and Plotly.
Defines design principles for color, typography, and layout to improve chart readability.
Includes a checklist for ensuring data visualizations meet accessibility standards.
Provides helper functions for formatting currency and numeric labels on axes.

When to use it

When you need to choose the right chart type for a specific dataset.
When writing Python code to generate static or interactive visualizations.
When applying design principles to ensure charts are clear and informative.
When auditing a visualization for accessibility and color blindness compatibility.

Advantages

Reduces ambiguity in chart selection with a structured relationship guide.
Provides ready-to-use code templates for standard visualization tasks.
Promotes consistent design and accessibility standards across charts.
Includes specific guidance on avoiding common visualization pitfalls.

Usage examples

“How should I visualize a trend over time?”: The skill recommends using a line chart, or an area chart if showing cumulative composition, and provides a Python code pattern for plotting time series data.
“What are the best practices for using color in charts?”: The skill advises using color purposefully to encode data, using sequential or diverging gradients for ordered data, and avoiding red/green combinations for accessibility.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/data-visualization/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip data-visualization.zip -d .agents/skills

Documentation by Anthropic

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

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)

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)

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.get_height()/2,
            f'{width:,.0f}', ha='left', va='center', fontsize=10)

ax.set_title('Metric by Category (Ranked)', fontweight='bold')
ax.set_xlabel('Metric Value')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

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

Histogram (Distribution)

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

ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)

# Add mean and median lines
mean_val = df['value'].mean()
median_val = df['value'].median()
ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')

ax.set_title('Distribution of Values', fontweight='bold')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

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

Heatmap

fig, ax = plt.subplots(figsize=(10, 8))

# Pivot data for heatmap format
pivot = df.pivot_table(index='row_dim', columns='col_dim', values='metric', aggfunc='sum')

sns.heatmap(pivot, annot=True, fmt=',.0f', cmap='YlOrRd',
            linewidths=0.5, ax=ax, cbar_kws={'label': 'Metric Value'})

ax.set_title('Metric by Row Dimension and Column Dimension', fontweight='bold')
ax.set_xlabel('Column Dimension')
ax.set_ylabel('Row Dimension')

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

Small Multiples

categories = df['category'].unique()
n_cats = len(categories)
n_cols = min(3, n_cats)
n_rows = (n_cats + n_cols - 1) // n_cols

fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), sharex=True, sharey=True)
axes = axes.flatten() if n_cats > 1 else [axes]

for i, cat in enumerate(categories):
    ax = axes[i]
    subset = df[df['category'] == cat]
    ax.plot(subset['date'], subset['value'], color=PALETTE_CATEGORICAL[i % len(PALETTE_CATEGORICAL)])
    ax.set_title(cat, fontsize=12)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# Hide empty subplots
for j in range(i+1, len(axes)):
    axes[j].set_visible(False)

fig.suptitle('Trends by Category', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
plt.savefig('small_multiples.png', dpi=150, bbox_inches='tight')

Number Formatting Helpers

def format_number(val, format_type='number'):
    """Format numbers for chart labels."""
    if format_type == 'currency':
        if abs(val) >= 1e9:
            return f'${val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'${val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'${val/1e3:.1f}K'
        else:
            return f'${val:,.0f}'
    elif format_type == 'percent':
        return f'{val:.1f}%'
    elif format_type == 'number':
        if abs(val) >= 1e9:
            return f'{val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'{val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'{val/1e3:.1f}K'
        else:
            return f'{val:,.0f}'
    return str(val)

# Usage with axis formatter
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))

Interactive Charts with Plotly

import plotly.express as px
import plotly.graph_objects as go

# Simple interactive line chart
fig = px.line(df, x='date', y='value', color='category',
              title='Interactive Metric Trend',
              labels={'value': 'Metric Value', 'date': 'Date'})
fig.update_layout(hovermode='x unified')
fig.write_html('interactive_chart.html')
fig.show()

# Interactive scatter with hover data
fig = px.scatter(df, x='metric_a', y='metric_b', color='category',
                 size='size_metric', hover_data=['name', 'detail_field'],
                 title='Correlation Analysis')
fig.show()

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

Files

ATTRIBUTION.mdAdded by AutoGineer916 B
LICENSELicence file added by AutoGineer11.1 KiB
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Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs Python

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Attribution and licence

Published by

Anthropic

Publisher

Anthropic (Official)

Source

anthropics/knowledge-work-plugins · data/skills/data-visualization · commit e050c2e

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Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from data/LICENSE)
ATTRIBUTION.md (written by AutoGineer)

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2026-11-08

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