Data Visualization Expert

Overview

Create publication-quality, accessible, and reader-friendly data visualizations that communicate insights effectively. This skill provides comprehensive guidance on visualization design principles, chart selection, color theory, dashboard design, and best practices for professional data communication.

When to Use This Skill

Invoke this skill when working on tasks that involve:

• Chart Creation: Creating any type of chart or graph
• Chart Selection: Choosing the right visualization for your data
• Color Design: Selecting color palettes and ensuring accessibility
• Dashboard Design: Creating executive dashboards or operational monitors
• Visual Design: Improving readability and aesthetic appeal
• Accessibility: Ensuring visualizations work for colorblind viewers
• Presentation Design: Preparing visualizations for slides or reports
• Data Communication: Making insights clear and actionable

Typical User Requests:

• "Create a bar chart comparing sales by region"
• "What's the best way to visualize this time series data?"
• "Make this chart more readable and professional"
• "Design a dashboard showing our key metrics"
• "Choose colors that work for colorblind people"
• "How should I present this data in a presentation?"
• "Create visualizations that tell a story"

Prerequisites

Before using this skill, ensure:

• Python 3.8+ with the following packages installed:
  • matplotlib>=3.5.0
  • seaborn>=0.12.0
  • pandas>=1.3.0
  • numpy>=1.20.0
• Japanese Font Support (optional, for Japanese text):
  • macOS: Built-in (Hiragino Sans)
  • Windows: Built-in (Yu Gothic, Meiryo)
  • Linux: Install fonts-noto-cjk or fonts-takao
• Input Data: CSV files with headers for chart creation scripts

Install dependencies:

pip install matplotlib seaborn pandas numpy

Core Visualization Workflow

Follow this systematic approach when creating visualizations:

1. Understand the Goal
   ↓
2. Analyze the Data
   ↓
3. Select Chart Type
   ↓
4. Choose Color Palette
   ↓
5. Apply Design Principles
   ↓
6. Add Context & Annotations
   ↓
7. Ensure Accessibility
   ↓
8. Review & Refine

Workflow 1: Choosing the Right Chart Type

Step 1: Identify Your Communication Goal

Ask yourself:

• What insight am I trying to convey?
• What question does this visualization answer?
• What action should viewers take?

Common Goals:

• Compare values across categories → Bar Chart, Lollipop Chart
• Show distribution → Histogram, Box Plot, Violin Plot
• Reveal relationship → Scatter Plot, Line Chart
• Display composition → Stacked Bar, Tree Map, Pie Chart (sparingly)
• Track change over time → Line Chart, Area Chart
• Show geographic patterns → Choropleth Map, Symbol Map
• Display hierarchical data → Tree Map, Sunburst, Tree Diagram

Step 2: Consider Your Data Characteristics

Data Type:

• Categorical: Bar chart, pie chart (2-5 categories only)
• Continuous: Line chart, scatter plot, histogram
• Time series: Line chart, area chart, sparkline
• Geographic: Map visualizations
• Hierarchical: Tree map, sunburst
• Multivariate: Scatter plot matrix, parallel coordinates, small multiples

Data Volume:

• Few data points (<20): Most chart types work
• Medium (20-100): Consider aggregation or small multiples
• Many (>100): Density plots, heatmaps, sampling, or interactive charts

Step 3: Consult the Chart Selection Guide

Reference: references/chart_selection_guide.md

Quick Selection:

COMPARISON → Bar Chart (vertical/horizontal)
  ├─ Few categories → Vertical bar
  ├─ Many categories or long names → Horizontal bar
  ├─ Multiple groups → Grouped bar
  └─ Parts + Total → Stacked bar

DISTRIBUTION → Histogram + KDE
  ├─ Quick summary → Box Plot
  ├─ Full shape → Violin Plot
  └─ Compare groups → Side-by-side box plots

RELATIONSHIP → Scatter Plot
  ├─ Continuous time → Line Chart
  ├─ Correlation → Heatmap
  └─ 3+ variables → Bubble chart or color/size encoding

COMPOSITION → Stacked Bar Chart
  ├─ Simple (2-3 parts) → Pie chart (acceptable)
  ├─ Over time → Stacked area
  ├─ Hierarchy → Tree map
  └─ Sequential changes → Waterfall

TIME SERIES → Line Chart
  ├─ Volume/cumulative → Area chart
  ├─ Multiple metrics → Line chart (max 5 lines)
  └─ Compact trend → Sparkline

Step 4: Avoid Common Mistakes

❌ Don't Use:

• Pie charts with >5 segments (use bar chart instead)
• 3D charts (distorts perception)
• Dual Y-axes (can mislead)
• Too many colors (limit to 5-8 for categorical data)
• Inconsistent time intervals

✅ Do Use:

• Simple, clear chart types
• Appropriate scales (start Y-axis at zero for bar charts)
• Direct labels when possible (instead of legends)
• Consistent formatting

Example: Choosing for Sales Analysis

Scenario: Compare sales across 5 product categories

Decision Process:

1. Goal: Compare values across categories
2. Data: 5 categorical values (products) with one numeric value (sales)
3. Chart Type: Bar Chart (vertical or horizontal)
4. Enhancements: Highlight top performer, add target line
5. Result: Clear comparison with immediate insight

Implementation:

python scripts/create_visualization.py \
  --type bar \
  --input sales_data.csv \
  --x product_category \
  --y sales_amount \
  --title "Q4 Product Sales Comparison" \
  --sort \
  --output product_sales.png

Workflow 2: Applying Color Best Practices

Step 1: Understand Color Purpose

Color Should:

• Encode information (categories, magnitude)
• Create visual hierarchy
• Guide attention to insights
• Maintain consistency across visualizations
• Work for all viewers (including colorblind)

Color Should NOT:

• Be purely decorative
• Use red/green for critical information (colorblind issue)
• Overwhelm with too many hues
• Lack sufficient contrast

Step 2: Select Appropriate Palette Type

Reference: references/visualization_principles.md → "Color Theory" Resource: assets/color_palettes.json

Palette Types:

1. Qualitative (Categorical Data):

• Use when: Showing distinct categories with no order
• Limit: 5-8 colors maximum
• Example: Product lines, regions, departments
• Recommended: Okabe-Ito (colorblind-safe), Tableau10

# Okabe-Ito palette (colorblind-safe)
colors = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
          '#0072B2', '#D55E00', '#CC79A7']

2. Sequential (Ordered Data):

• Use when: Showing low-to-high progression
• Example: Population density, temperature, sales volume
• Recommended: Single hue (light to dark blue, green, etc.)

3. Diverging (Data with Midpoint):

• Use when: Data has meaningful center (zero, average)
• Example: Positive/negative, above/below average, correlation (-1 to +1)
• Recommended: Red-Blue, Brown-Teal (colorblind-safe)

4. Semantic (Meaning-Based):

• Use when: Colors have conventional meanings
• Green = positive/success, Red = negative/danger, Yellow = warning
• Caution: Cultural differences exist

Step 3: Ensure Accessibility

Colorblind Considerations:

• 8% of males, 0.5% of females have color vision deficiency
• Most common: Red-green colorblindness

Best Practices:

1. Use colorblind-safe palettes (Okabe-Ito, Viridis)
2. Don't rely on color alone - add patterns, labels, or shapes
3. Test with simulator (Color Oracle, Coblis)
4. Ensure contrast - 4.5:1 for text, 3:1 for graphics (WCAG 2.1)

Safe Color Combinations:

• Blue + Orange (excellent contrast)
• Blue + Yellow
• Purple + Green
• Brown + Teal

Avoid:

• Red + Green (most problematic)
• Blue + Purple (for some types)
• Light green + Yellow

Step 4: Apply Color Strategically

Emphasis Pattern:

• Main data: Primary color (e.g., blue)
• Highlight: Accent color (e.g., red/orange)
• Background data: Gray (de-emphasize)

Example:

# All bars in gray except the maximum
colors = ['#CCCCCC'] * len(data)
colors[max_index] = '#E63946'  # Highlight in red

Example: Colorblind-Safe Dashboard

Scenario: Create dashboard viewable by all users

Solution:

1. Use Okabe-Ito palette for categories
2. Use Blue-Orange for comparisons
3. Add patterns to bars (diagonal lines, dots)
4. Include value labels directly on charts
5. Test with Color Oracle simulator

# Implementation
from visualization_templates import executive_summary_template

kpis = [
    {'title': 'Revenue', 'value': 1234567, 'change_pct': 12.5},
    {'title': 'Customers', 'value': 5432, 'change_pct': 5.2}
]

fig = executive_summary_template(kpis, title='Q4 Dashboard')
fig.savefig('accessible_dashboard.png', dpi=300)

Workflow 3: Designing Professional Dashboards

Step 1: Define Dashboard Purpose and Audience

Dashboard Types:

1. Strategic (Executive):

• Audience: C-level executives
• Content: High-level KPIs, trends, targets
• Update: Weekly/monthly
• Focus: Strategic decision-making

2. Operational:

• Audience: Managers, operations team
• Content: Real-time metrics, status indicators
• Update: Real-time to hourly
• Focus: Day-to-day operations

3. Analytical:

• Audience: Analysts, data teams
• Content: Detailed data, drill-down, filters
• Update: Daily or on-demand
• Focus: Deep exploration

Step 2: Apply Information Hierarchy

Reference: references/dashboard_design.md

F-Pattern Layout (Western Reading):

┌─────────────────────────────┐
│ 1. Most Important (Top-Left)│
│    ↓                        │
│ 2. Secondary (Middle)       │
│    ↓                        │
│ 3. Details (Bottom)         │
└─────────────────────────────┘

Layout Guidelines:

• Top Row: 3-5 KPI cards with sparklines
• Middle: Primary visualization (largest chart)
• Bottom: Supporting charts and tables
• Whitespace: 20-30% of dashboard should be empty space

Step 3: Design KPI Cards

Components:

• Metric Name: Clear, concise (10-12pt)
• Value: Large, bold (28-36pt)
• Change Indicator: Arrow + percentage with color
• Sparkline: Optional mini-trend (last 7-30 periods)
• Comparison: "vs last month/year"

Color Coding:

↑ Green: Positive change
↓ Red: Negative change
→ Gray: No significant change

Example:

from visualization_templates import kpi_card

fig = kpi_card(
    value=1234567,
    title='Monthly Revenue',
    change_pct=12.5,
    sparkline_data=[100, 105, 102, 108, 115, 120]
)

Step 4: Maintain Consistency

Style Guide Elements:

• Font: Single family (Arial, Helvetica, Roboto)
• Colors: Consistent palette across all charts
• Spacing: Regular margins (16px, 24px, 32px increments)
• Grid: 12-column system for alignment
• Chart styles: Same border, gridline, and label format

Example: Executive Dashboard

Scenario: Create monthly executive summary

Requirements:

• 4 key metrics (Revenue, Customers, Retention, NPS)
• 2 trend charts (Revenue over time, Regional breakdown)
• Suitable for presentation
• Print-friendly

Implementation:

from visualization_templates import executive_summary_template

kpis = [
    {
        'title': 'Revenue',
        'value': 1234567,
        'change_pct': 12.5,
        'sparkline': [100, 105, 102, 108, 115, 120]
    },
    {
        'title': 'Customers',
        'value': 5432,
        'change_pct': 5.2,
        'sparkline': [90, 92, 95, 97, 98, 100]
    },
    {
        'title': 'Retention',
        'value': 89.5,
        'change_pct': 1.2,
        'sparkline': [85, 86, 87, 88, 89, 89.5]
    },
    {
        'title': 'NPS',
        'value': 72,
        'change_pct': 8.0,
        'sparkline': [65, 67, 68, 70, 71, 72]
    }
]

fig = executive_summary_template(
    kpis=kpis,
    title='Q4 2024 Executive Summary',
    subtitle='October - December 2024',
    figsize=(16, 10)
)

fig.savefig('executive_summary.pdf', dpi=300, bbox_inches='tight')

Workflow 4: Creating Story-Driven Visualizations

Step 1: Define Your Narrative

Story Structure:

1. Context: Establish baseline or background
2. Insight: Reveal the key finding
3. Action: What should viewer do with this information?

Example:

• Context: "Historical sales have been flat for 3 years"
• Insight: "New product line drove 45% growth in Q4"
• Action: "Invest more resources in new product development"

Step 2: Use Visual Emphasis

Techniques:

1. Color Contrast:

• Key element: Bright color (red, orange)
• Others: Gray or muted colors

# Highlight one bar
colors = ['#CCCCCC'] * 5
colors[3] = '#E63946'  # Highlight 4th bar

2. Annotations:

• Add arrows, text boxes, or callouts
• Keep text concise (5-8 words)
• Position near relevant data

ax.annotate('45% increase',
            xy=(date, value),
            xytext=(10, 10),
            textcoords='offset points',
            fontsize=12,
            fontweight='bold',
            color='#E63946',
            arrowprops=dict(arrowstyle='->', color='#E63946'))

3. Size:

• Make important elements larger
• Reduce size of supporting elements

4. Position:

• Place critical insight in top-left (F-pattern)

Step 3: Add Context

Essential Elements:

• Title: Clear, descriptive, includes key insight
• Axes Labels: Include units (¥, %, etc.)
• Legends: When needed, but prefer direct labeling
• Source: Data source and date
• Notes: Important caveats or methodology

Good Title Examples:

❌ "Sales Over Time"
✅ "Sales Jumped 45% After New Product Launch in Q4"

❌ "Customer Satisfaction"
✅ "Customer Satisfaction Reaches All-Time High of 8.9/10"

Step 4: Progressive Disclosure

For Complex Stories:

1. Start with summary/conclusion
2. Show high-level view
3. Allow drill-down to details
4. Provide data table for reference

Dashboard Pattern:

Top: Summary statement + key number
Middle: Primary chart showing main insight
Bottom: Supporting details and breakdowns

Example: Product Launch Analysis

Scenario: Show impact of new product launch on revenue

Story:

• Context: Revenue flat for 12 months
• Event: New product launched in September
• Impact: 45% revenue increase in Q4
• Action: Scale up production and marketing

Visualization Approach:

1. Line chart showing 18-month revenue trend
2. Vertical line marking launch date
3. Annotation highlighting 45% increase
4. Color: Gray before launch, Blue after launch
5. Title: "New Product Drives 45% Revenue Growth in Q4"

import matplotlib.pyplot as plt
import pandas as pd

fig, ax = plt.subplots(figsize=(12, 6))

# Pre-launch (gray)
ax.plot(dates[:9], revenue[:9], color='#CCCCCC', linewidth=2, label='Before Launch')

# Post-launch (blue)
ax.plot(dates[8:], revenue[8:], color='#2E86AB', linewidth=3, label='After Launch', marker='o')

# Launch date line
ax.axvline(launch_date, color='#666666', linestyle='--', linewidth=2, alpha=0.7)
ax.text(launch_date, ax.get_ylim()[1], ' Product Launch', ha='left', va='top')

# Highlight annotation
ax.annotate('↑ 45% increase',
            xy=(dates[-1], revenue[-1]),
            xytext=(-50, 20),
            textcoords='offset points',
            fontsize=14,
            fontweight='bold',
            color='#E63946',
            arrowprops=dict(arrowstyle='->', lw=2, color='#E63946'))

ax.set_title('New Product Drives 45% Revenue Growth in Q4', fontweight='bold', fontsize=16)
ax.set_ylabel('Revenue (¥ Million)', fontweight='bold')
ax.legend()
ax.grid(alpha=0.3)
plt.tight_layout()

Workflow 5: Ensuring Accessibility and Readability

Step 1: Test for Colorblindness

Tools:

• Color Oracle (free desktop app)
• Coblis - Color Blindness Simulator (online)
• Chrome DevTools Accessibility features

Process:

1. Create visualization
2. Run through colorblind simulator
3. Check if information is still clear
4. If not, add patterns, labels, or adjust colors

Step 2: Check Contrast Ratios

WCAG 2.1 Standards:

• Normal text: 4.5:1 minimum
• Large text (18pt+): 3:1 minimum
• Graphics: 3:1 minimum

Tools:

• WebAIM Contrast Checker
• Colour Contrast Analyser

Common Issues:

• Light gray text on white background (insufficient)
• Yellow on white (insufficient)
• Light blue on white (borderline)

Step 3: Ensure Font Readability

Best Practices:

• Font Family: Sans-serif for digital (Arial, Helvetica, Roboto)
• Minimum Size: 10-11pt for body text, 8-9pt minimum for labels
• Hierarchy: Use 2-3 sizes maximum
  • Title: 16-20pt, bold
  • Labels: 12-14pt
  • Annotations: 10-12pt

Avoid:

• All caps (harder to read)
• Light font weights (<400)
• Decorative or script fonts

Step 4: Test on Target Medium

Considerations by Medium:

1. Presentation (Projector):

• Larger fonts (minimum 14pt)
• High contrast
• Simple charts
• Avoid fine details

2. Print:

• 300 DPI minimum
• Consider B&W printing
• Include data tables

3. Dashboard (Screen):

• Responsive design
• Interactive elements clear
• Touch targets 44×44px minimum (mobile)

4. Report (PDF):

• Embedded fonts
• Vector graphics (SVG) when possible
• Consistent with document style

Example: Accessible Presentation Chart

Scenario: Chart for conference presentation

Requirements:

• Visible from back of room
• Works on projector (often low contrast)
• Colorblind-safe
• Clear on photos/screenshots

Solution:

import matplotlib.pyplot as plt
import seaborn as sns

# Setup
plt.rcParams['font.size'] = 14  # Larger default
plt.rcParams['axes.titlesize'] = 20
plt.rcParams['axes.labelsize'] = 16

fig, ax = plt.subplots(figsize=(12, 7))

# Use high-contrast, colorblind-safe colors
colors = ['#0072B2', '#E69F00']  # Blue and Orange

# Create chart with clear distinction
ax.bar(categories, values, color=colors[0], edgecolor='black', linewidth=2)

# Large, bold labels
ax.set_title('Clear Title Visible From Back Row', fontweight='bold', fontsize=24, pad=20)
ax.set_ylabel('Value (Units)', fontweight='bold', fontsize=18)

# High contrast grid
ax.grid(axis='y', alpha=0.5, linewidth=1.5, color='#666666')

# Direct labels (no legend needed)
for i, (cat, val) in enumerate(zip(categories, values)):
    ax.text(i, val + 10, f'{val}', ha='center', fontsize=16, fontweight='bold')

# Clean style
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
fig.savefig('presentation_chart.png', dpi=300, facecolor='white')

Quick Start Examples

Example 1: Simple Bar Chart

python scripts/create_visualization.py \
  --type bar \
  --input sales.csv \
  --x product \
  --y revenue \
  --title "Product Revenue Comparison Q4 2024" \
  --ylabel "Revenue (¥ Million)" \
  --palette colorblind_safe \
  --sort \
  --output revenue_chart.png

Example 2: Time Series with Trend

python scripts/create_visualization.py \
  --type line \
  --input monthly_sales.csv \
  --x date \
  --y sales \
  --hue region \
  --title "Regional Sales Trends" \
  --markers \
  --palette tableau10 \
  --output sales_trend.png

Example 3: Scatter Plot with Trendline

python scripts/create_visualization.py \
  --type scatter \
  --input marketing.csv \
  --x spend \
  --y revenue \
  --size customer_count \
  --title "Marketing ROI Analysis" \
  --trendline \
  --output roi_analysis.png

Example 4: Executive KPI Cards

from visualization_templates import kpi_card

# Create individual KPI card
fig = kpi_card(
    value=1234567,
    title='Monthly Revenue',
    change_pct=12.5,
    sparkline_data=[100, 105, 102, 108, 115, 120],
    figsize=(3, 2.5)
)
fig.savefig('revenue_kpi.png', dpi=150, bbox_inches='tight')

Example 5: Full Dashboard

from visualization_templates import executive_summary_template

kpis = [
    {'title': 'Revenue', 'value': 1234567, 'change_pct': 12.5, 'sparkline': [100,105,108,115,120]},
    {'title': 'Customers', 'value': 5432, 'change_pct': 5.2, 'sparkline': [90,92,95,98,100]},
    {'title': 'Retention %', 'value': 89.5, 'change_pct': 1.2, 'sparkline': [85,86,88,89,89.5]},
    {'title': 'NPS', 'value': 72, 'change_pct': 8.0, 'sparkline': [65,68,70,71,72]}
]

fig = executive_summary_template(
    kpis=kpis,
    title='Q4 2024 Performance Dashboard',
    subtitle='October - December 2024',
    figsize=(16, 10)
)
fig.savefig('dashboard.pdf', dpi=300, bbox_inches='tight')

Resources

This skill includes the following resources:

References (Load on-demand for guidance)

1. visualization_principles.md - Core design principles

   • Clarity, accuracy, accessibility guidelines
   • Color theory and palettes
   • Typography and layout principles
   • Storytelling techniques

2. chart_selection_guide.md - Comprehensive chart type guide

   • Decision tree for chart selection
   • 30+ chart types with use cases
   • When to use and when to avoid specific charts
   • Code examples for each type

3. dashboard_design.md - Dashboard best practices

   • Layout principles and grid systems
   • KPI card design
   • Interactivity and filters
   • Performance optimization

Scripts (Executable tools)

1. create_visualization.py - Command-line visualization creator
   • Supports 6 chart types: bar, line, scatter, heatmap, distribution, dashboard
   • Professional styling built-in
   • Multiple color palettes
   • Export to PNG, PDF, SVG

Assets (Templates and resources for output)

1. visualization_templates.py - Ready-to-use Python templates

   • KPI cards
   • Executive summaries
   • Comparison charts
   • Trend analysis
   • Waterfall charts
   • Correlation heatmaps

2. color_palettes.json - Comprehensive color palette library

   • Qualitative (categorical data)
   • Sequential (ordered data)
   • Diverging (data with midpoint)
   • Business and industry-specific
   • Accessibility-focused palettes
   • Usage guidelines

Output

This skill produces the following outputs:

Output Type	Format	Description
Chart Images	PNG, PDF, SVG	Publication-quality visualizations (300 DPI default)
Dashboards	PNG, PDF	Multi-chart layouts with KPI cards
Code Snippets	Python	Ready-to-execute matplotlib/seaborn code

Output Characteristics

• Resolution: 300 DPI (configurable via --dpi flag)
• Color Mode: RGB with white background
• Font Embedding: Supported for PDF output
• File Naming: User-specified via --output parameter

Example Output Locations

# Single chart
python scripts/create_visualization.py --type bar ... --output ./charts/sales_comparison.png

# Dashboard
python scripts/create_visualization.py --type dashboard ... --output ./reports/q4_dashboard.pdf

Best Practices Checklist

Before Creating Visualization

• Understand the insight you want to communicate
• Know your audience and their needs
• Identify the appropriate chart type for your data
• Consider the medium (screen, print, presentation)

During Creation

• Use appropriate chart type for data and goal
• Apply colorblind-safe palette
• Ensure sufficient contrast (4.5:1 text, 3:1 graphics)
• Configure Japanese/international font support if needed (see section below)
• Start bar chart Y-axis at zero (unless good reason)
• Label axes with units
• Use direct labels instead of legends when possible
• Add clear, descriptive title (include key insight)
• Remove chartjunk (unnecessary decoration)
• Use consistent formatting across related charts

After Creation

• Test with colorblind simulator
• Check contrast ratios
• Verify readability at target size/distance
• Review for accuracy and honesty
• Add data source and date
• Get feedback from representative user

Japanese and International Font Support

Why This Matters

When creating visualizations with Japanese text (or other non-Latin scripts), incorrect font configuration causes:

• Character rendering as boxes (□□□)
• Mojibake (文字化け) / garbled text
• Minus signs displaying as boxes

Best Practice: Cross-Platform Font Configuration

Always configure fonts after applying matplotlib style, with platform-specific fallbacks:

import matplotlib.pyplot as plt
import platform

# Apply style FIRST
plt.style.use('seaborn-v0_8-whitegrid')

# Configure fonts AFTER style (so they don't get overwritten)
if platform.system() == 'Darwin':  # macOS
    japanese_fonts = ['Hiragino Sans', 'Hiragino Maru Gothic Pro',
                      'Arial Unicode MS', 'Yu Gothic', 'Meirio']
elif platform.system() == 'Windows':
    japanese_fonts = ['Yu Gothic', 'MS Gothic', 'Meiryo',
                      'IPAexGothic', 'IPAPGothic']
else:  # Linux
    japanese_fonts = ['Noto Sans CJK JP', 'Takao',
                      'IPAexGothic', 'IPAPGothic']

# Set font configuration
plt.rcParams['font.sans-serif'] = japanese_fonts
plt.rcParams['font.monospace'] = japanese_fonts  # For text boxes
plt.rcParams['axes.unicode_minus'] = False  # Prevent minus sign boxes

Critical Points

✅ DO:

• Set fonts after plt.style.use() to prevent overriding
• Include font.monospace for text boxes and annotations
• Set axes.unicode_minus = False to fix minus sign rendering
• Use platform-specific font lists for cross-platform compatibility
• Test on target platform before finalizing

❌ DON'T:

• Use family='monospace' in text() calls (it will cause mojibake)
• Set fonts before style (style will override your settings)
• Assume one font works on all platforms
• Forget to configure monospace fonts

Quick Template for Japanese Visualizations

import matplotlib.pyplot as plt
import platform

# Configure Japanese font support
def setup_japanese_fonts():
    plt.style.use('seaborn-v0_8-whitegrid')

    if platform.system() == 'Darwin':
        fonts = ['Hiragino Sans', 'Arial Unicode MS']
    elif platform.system() == 'Windows':
        fonts = ['Yu Gothic', 'Meiryo']
    else:
        fonts = ['Noto Sans CJK JP', 'Takao']

    plt.rcParams['font.sans-serif'] = fonts
    plt.rcParams['font.monospace'] = fonts
    plt.rcParams['axes.unicode_minus'] = False

# Use it
setup_japanese_fonts()

# Now create your charts
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_title('日本語タイトル')  # Will render correctly
ax.set_xlabel('横軸ラベル')
ax.set_ylabel('縦軸ラベル')
# ... rest of your visualization

Common Mistakes to Avoid

❌ Using pie charts for many categories → Use horizontal bar chart ❌ 3D effects → Use 2D charts with proper visual encoding ❌ Dual Y-axes → Use indexed values or separate charts ❌ Red-green color scheme → Use blue-orange or other colorblind-safe combinations ❌ Too many colors → Limit to 5-8 for categorical data ❌ Truncated Y-axis (bar charts) → Start at zero ❌ Missing labels/units → Always label axes and include units ❌ Cluttered layout → Use whitespace, simplify ❌ Forgetting Japanese font config → Set fonts after style with platform detection

Additional Resources

Books:

• "The Visual Display of Quantitative Information" by Edward Tufte
• "Storytelling with Data" by Cole Nussbaumer Knaflic
• "Information Dashboard Design" by Stephen Few

Online Tools:

• ColorBrewer 2.0 - Color palette generator
• Coolors.co - Color scheme generator
• WebAIM Contrast Checker - Accessibility testing
• Color Oracle - Colorblindness simulator

Python Libraries:

• Matplotlib - Foundation visualization library
• Seaborn - Statistical visualizations
• Plotly - Interactive charts
• Altair - Declarative visualization