Visualization Chooser — Visualization Type Selection Matrix Guide
A framework for selecting optimal visualizations based on data type and communication purpose.
Visualization Selection Matrix
Comparison
| Purpose |
Chart |
Suitable |
Example |
| Item comparison |
Bar chart |
5-15 categories |
Sales by product |
| Time trend comparison |
Line chart |
Continuous time, 2-5 series |
Monthly sales trend |
| Part-to-whole |
Stacked bar |
Ratio comparison |
Sales by channel share |
| Few ratios |
Pie chart |
2-5 items only |
Market share |
| Many ratios |
Treemap |
Hierarchical data |
Sales by category |
Distribution
| Purpose |
Chart |
Suitable |
Example |
| Single distribution |
Histogram |
Continuous variable |
Age distribution |
| Distribution comparison |
Box plot |
Group comparison |
Salary by department |
| Density comparison |
Violin plot |
Distribution shape matters |
Score distribution |
| Outlier emphasis |
Strip plot |
Small data |
Individual data points |
Relationship
| Purpose |
Chart |
Suitable |
Example |
| Two-variable relationship |
Scatter plot |
Continuous×Continuous |
Ad spend vs sales |
| Multi-variable correlation |
Heatmap |
Correlation matrix |
Inter-variable correlation |
| Trend line |
Regression plot |
Linear relationship |
Experience vs salary |
| Density scatter |
2D density |
Too many data points |
Location data |
| Bubble chart |
Scatter + size |
3 variables |
GDP/population/life expectancy by country |
Time
| Purpose |
Chart |
Suitable |
Example |
| Trend |
Line chart |
Continuous time series |
Daily stock price |
| Seasonality |
Decomposition chart |
Periodic patterns |
Monthly electricity usage |
| Event highlight |
Annotated line |
Specific time points |
Marketing campaign effect |
| Range |
Area chart |
Cumulative/ratio |
Traffic by channel |
Implementation Code Patterns
Font Configuration (Essential for non-Latin scripts)
import matplotlib.pyplot as plt
import platform
if platform.system() == 'Darwin': # macOS
plt.rcParams['font.family'] = 'AppleGothic'
elif platform.system() == 'Windows':
plt.rcParams['font.family'] = 'Malgun Gothic'
else: # Linux
plt.rcParams['font.family'] = 'NanumGothic'
plt.rcParams['axes.unicode_minus'] = False
Color Palettes
# Sequential (continuous values)
palette_sequential = 'YlOrRd'
# Categorical (discrete)
palette_categorical = ['#4C72B0', '#55A868', '#C44E52', '#8172B3', '#CCB974']
# Diverging (bipolar)
palette_diverging = 'RdBu_r'
# Accessibility-friendly
palette_colorblind = sns.color_palette('colorblind')
Dashboard Layout
fig, axes = plt.subplots(2, 3, figsize=(18, 10))
fig.suptitle('Sales Analysis Dashboard', fontsize=16, fontweight='bold')
# KPI Card (text-based)
axes[0,0].text(0.5, 0.5, f'Total Sales\n${total:,.0f}', ha='center', va='center', fontsize=20)
# Trend chart
axes[0,1].plot(dates, sales, '-o')
# Distribution
axes[0,2].boxplot([q1, q2, q3, q4])
# Comparison
axes[1,0].barh(categories, values)
# Correlation
sns.heatmap(corr_matrix, ax=axes[1,1], annot=True, cmap='RdBu_r')
# Pie
axes[1,2].pie(shares, labels=channels, autopct='%1.1f%%')
plt.tight_layout()
Visualization Anti-patterns
| Anti-pattern |
Problem |
Solution |
| 3D charts |
Distortion, hard to read |
Use 2D |
| Dual Y-axes |
Misleading comparisons |
Separate charts or normalize |
| Pie with >5 slices |
Cannot compare |
Switch to bar chart |
| Rainbow colors |
Hard to distinguish patterns |
Use sequential/categorical palettes |
| Y-axis not starting at 0 |
Exaggerates differences |
Start Y-axis from 0 |
| Information overload |
Misses the point |
Focus on one highlight |
| No legend |
Cannot interpret |
Clear legends/labels |
Interactive Visualization (Plotly)
import plotly.express as px
# Scatter + color + size + hover
fig = px.scatter(
df, x='ad_spend', y='sales',
color='category', size='customers',
hover_data=['product_name'],
title='Ad Spend vs Sales Analysis'
)
fig.show()
# Plotly → HTML export
fig.write_html('interactive_chart.html')
Executive Report Visualization Principles
1. One key message: One insight per chart
2. Title = Conclusion: "Sales declined 15%" (O) vs "Monthly Sales" (X)
3. Color = Meaning: Red=bad, Green=good, Gray=baseline
4. Annotations: Display key figures directly
5. Comparison baseline: Prior month, prior year, target, industry average
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