data-visualization

SKILL.md

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