---

name: visualization-builder description: Create effective, publication-ready data visualizations. Use when choosing chart types, designing dashboards, creating presentation visuals, or building interactive visualizations with best practices.

Visualization Builder

Quick Start

Create clear, impactful data visualizations that communicate insights effectively using visualization best practices and appropriate chart selection.

Context Requirements

1. Data: What you're visualizing
2. Message: Key insight to communicate
3. Audience: Technical vs business, detail level needed
4. Medium: Dashboard, presentation, report, interactive
5. Brand: Colors, style guidelines

Context Gathering

For Data:

"What data are you visualizing?

• Variables (categorical, continuous, time)
• Sample size and granularity
• Comparisons needed (categories, time periods, segments)

Example: 'Monthly revenue by product category, 12 months, 5 categories'"

For Message:

"What's the one key takeaway?

• Trend over time?
• Comparison between groups?
• Composition/breakdown?
• Relationship/correlation?
• Distribution?

Clear message → correct chart type."

For Audience:

"Who will see this?

• Technical: Can handle complexity, likes detail
• Executive: Needs simplicity, focuses on insight
• Public: Requires self-explanatory design

Audience drives detail level and annotation needs."

Workflow

Step 1: Choose Chart Type

Copyimport matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

def recommend_chart_type(data_type, message_type):
    """Recommend appropriate visualization"""
    
    recommendations = {
        ('time_series', 'trend'): 'Line chart',
        ('categories', 'comparison'): 'Bar chart (horizontal if many categories)',
        ('categories', 'composition'): 'Stacked bar or pie chart',
        ('two_variables', 'relationship'): 'Scatter plot',
        ('distribution', 'single'): 'Histogram or box plot',
        ('distribution', 'multiple'): 'Violin plot or overlaid histograms',
        ('geographic', 'comparison'): 'Choropleth map',
        ('hierarchical', 'composition'): 'Treemap or sunburst',
        ('flow', 'process'): 'Sankey diagram'
    }
    
    chart = recommendations.get((data_type, message_type))
    
    print(f"📊 Recommended Chart: {chart}")
    print(f"   For: {message_type} of {data_type}")
    
    return chart

# Example
recommend_chart_type('time_series', 'trend')

Step 2: Design Publication-Quality Visualization

Copydef create_professional_chart(df, title, y_label):
    """Create polished, publication-ready chart"""
    
    # Set style
    sns.set_style("whitegrid")
    plt.rcParams['font.family'] = 'sans-serif'
    plt.rcParams['font.size'] = 11
    
    fig, ax = plt.subplots(figsize=(12, 6))
    
    # Plot data
    colors = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#6A994E']
    
    for i, col in enumerate(df.columns[1:]):
        ax.plot(df['date'], df[col], 
               marker='o', linewidth=2.5, 
               markersize=6, color=colors[i % len(colors)],
               label=col)
    
    # Styling
    ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
    ax.set_xlabel('', fontsize=12)
    ax.set_ylabel(y_label, fontsize=12)
    
    # Grid
    ax.grid(True, alpha=0.3, linestyle='--')
    ax.set_axisbelow(True)
    
    # Legend
    ax.legend(frameon=True, loc='best', fontsize=10)
    
    # Format y-axis
    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x/1000:.0f}K'))
    
    # Annotations for key points
    max_idx = df[df.columns[1]].idxmax()
    max_val = df[df.columns[1]].max()
    ax.annotate(f'Peak: ${max_val/1000:.0f}K',
               xy=(df.loc[max_idx, 'date'], max_val),
               xytext=(10, 10), textcoords='offset points',
               fontsize=10, bbox=dict(boxstyle='round', fc='white', alpha=0.8),
               arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
    
    plt.tight_layout()
    return fig

# Example data
dates = pd.date_range('2024-01-01', periods=12, freq='M')
data = pd.DataFrame({
    'date': dates,
    'Product A': np.random.randint(80, 120, 12) * 1000,
    'Product B': np.random.randint(60, 100, 12) * 1000
})

fig = create_professional_chart(data, 'Monthly Revenue by Product', 'Revenue')
plt.savefig('revenue_chart.png', dpi=300, bbox_inches='tight')
print("✅ Professional chart created")

Step 3: Apply Visual Design Principles

Copydef apply_visual_hierarchy(ax, focus_element=None):
    """Emphasize key elements, de-emphasize secondary"""
    
    # De-emphasize gridlines
    ax.grid(True, alpha=0.2, color='gray', linestyle=':')
    
    # Emphasize focus element
    if focus_element:
        # Make other elements semi-transparent
        for line in ax.get_lines():
            if line.get_label() != focus_element:
                line.set_alpha(0.3)
                line.set_linewidth(1.5)
            else:
                line.set_linewidth(3)
                line.set_zorder(10)
    
    # Clean spines
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_color('#333333')
    ax.spines['bottom'].set_color('#333333')
    
    return ax

print("✅ Visual hierarchy principles applied")

Step 4: Add Context and Annotations

Copydef add_contextual_annotations(ax, insights, benchmarks=None):
    """Add explanatory text and reference lines"""
    
    # Add insight callouts
    for insight in insights:
        ax.annotate(
            insight['text'],
            xy=insight['position'],
            xytext=insight['text_position'],
            fontsize=9,
            bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.3),
            arrowprops=dict(arrowstyle='->', lw=1.5)
        )
    
    # Add benchmark lines
    if benchmarks:
        for benchmark in benchmarks:
            ax.axhline(benchmark['value'], 
                      color='red', linestyle='--', alpha=0.5,
                      label=benchmark['label'])
    
    # Add source
    fig = ax.get_figure()
    fig.text(0.99, 0.01, 'Source: Analytics Database | Generated: Jan 2025',
            ha='right', va='bottom', fontsize=8, color='gray')
    
    return ax

# Example usage
insights = [
    {
        'text': 'Holiday spike',
        'position': (dates[10], data['Product A'].iloc[10]),
        'text_position': (10, 20)
    }
]

benchmarks = [
    {'value': 90000, 'label': 'Target: $90K'}
]

print("✅ Contextual annotations added")

Step 5: Create Dashboard Layout

Copydef create_dashboard_layout(metrics_dict):
    """Multi-panel dashboard with proper spacing"""
    
    fig = plt.figure(figsize=(16, 10))
    gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)
    
    # Large chart top left
    ax1 = fig.add_subplot(gs[0:2, 0:2])
    ax1.set_title('Primary Metric Trend', fontsize=14, fontweight='bold')
    
    # Supporting charts
    ax2 = fig.add_subplot(gs[0, 2])
    ax2.set_title('Breakdown', fontsize=12)
    
    ax3 = fig.add_subplot(gs[1, 2])
    ax3.set_title('Comparison', fontsize=12)
    
    # KPI cards at bottom
    ax4 = fig.add_subplot(gs[2, 0])
    ax5 = fig.add_subplot(gs[2, 1])
    ax6 = fig.add_subplot(gs[2, 2])
    
    # Style KPI cards
    for ax, (metric, value) in zip([ax4, ax5, ax6], metrics_dict.items()):
        ax.text(0.5, 0.6, f"${value:,.0f}", 
               ha='center', va='center', fontsize=24, fontweight='bold')
        ax.text(0.5, 0.3, metric,
               ha='center', va='center', fontsize=12, color='gray')
        ax.axis('off')
        ax.set_facecolor('#f8f9fa')
    
    return fig, [ax1, ax2, ax3, ax4, ax5, ax6]

metrics = {'Revenue': 125000, 'Customers': 450, 'AOV': 278}
fig, axes = create_dashboard_layout(metrics)
plt.savefig('dashboard.png', dpi=300, bbox_inches='tight')
print("✅ Dashboard layout created")

Context Validation

• Chart type matches data and message
• Axes clearly labeled
• Legend is readable
• Key insights annotated
• Colors accessible (colorblind-safe)
• Source cited

Output Template

Copy[Professional visualization with:]

✓ Clear title
✓ Labeled axes with units
✓ Appropriate scale
✓ Legend (if multiple series)
✓ Key points annotated
✓ Benchmark lines if relevant
✓ Source attribution
✓ High-resolution export (300 DPI)
✓ Colorblind-friendly palette
✓ Consistent brand styling

Common Scenarios

Scenario 1: "Create exec presentation visual"

→ Simple, high-impact chart → Annotate key insight → Use brand colors → Large fonts, minimal detail → One message per chart

Scenario 2: "Build interactive dashboard"

→ Hierarchical layout (primary + supporting) → Consistent styling across panels → Clear navigation → Responsive design → Performance optimized

Scenario 3: "Show comparison between groups"

→ Use grouped/clustered bars → Order logically (alphabetical, by value) → Consistent color per group → Direct labels vs legend → Highlight largest difference

Scenario 4: "Display time series with seasonality"

→ Line chart with trend line → Add seasonal decomposition → Annotate key events → Show confidence bands → Include year-over-year comparison

Scenario 5: "Visualize complex relationships"

→ Start with scatter plot → Add trend line → Size/color encode 3rd variable → Filter to key segments → Interactive exploration if possible

Handling Missing Context

User says "make it look good": "I need specifics:

• What's the key message?
• Who's the audience?
• Where will this be shown?
• Any brand guidelines? These drive design choices."

Unclear chart type: "Let's decide together:

• Are you comparing values? → Bar chart
• Showing trend over time? → Line chart
• Showing composition? → Stacked bar/pie
• Showing relationship? → Scatter plot What type of question are you answering?"

Too much data for one chart: "Let's focus:

• What's the #1 insight?
• Can we filter to key segments?
• Should this be a dashboard instead?
• What can go in appendix? Less is more for clarity."

Advanced Options

Interactive Visualizations: Plotly, Altair for web-based exploration

Animation: Show change over time with animated transitions

Small Multiples: Repeat chart structure for comparison

Accessibility: Alt text, patterns in addition to color, screen reader compatible

Automated Chart Generation: Template-based chart creation from data