Data Visualization

Overview

Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making.

When to Use

• Exploratory data analysis and pattern discovery
• Communicating insights to stakeholders
• Comparing distributions and relationships
• Presenting findings in reports and dashboards
• Identifying outliers and anomalies visually
• Creating publication-ready charts and graphs

Visualization Types

• Distributions: Histograms, KDE, violin plots
• Relationships: Scatter plots, line plots, heatmaps
• Comparisons: Bar charts, box plots, ridge plots
• Compositions: Pie charts, stacked bars, treemaps
• Temporal: Line plots, area charts, time series
• Multivariate: Pair plots, correlation heatmaps

Design Principles

• Choose appropriate chart type for data
• Minimize ink-to-data ratio
• Use color purposefully
• Label clearly and completely
• Maintain consistent scales
• Consider accessibility

Implementation with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.gridspec import GridSpec

# Set style
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 6)

# Generate sample data
np.random.seed(42)
n = 500
data = pd.DataFrame({
    'age': np.random.uniform(20, 70, n),
    'income': np.random.exponential(50000, n),
    'education_years': np.random.uniform(12, 20, n),
    'category': np.random.choice(['A', 'B', 'C'], n),
    'region': np.random.choice(['North', 'South', 'East', 'West'], n),
    'satisfaction': np.random.uniform(1, 5, n),
    'purchased': np.random.choice([0, 1], n),
})

print(data.head())

# 1. Distribution Plots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Histogram
axes[0, 0].hist(data['age'], bins=30, color='skyblue', edgecolor='black')
axes[0, 0].set_title('Age Distribution (Histogram)')
axes[0, 0].set_xlabel('Age')
axes[0, 0].set_ylabel('Frequency')

# KDE plot
data['income'].plot(kind='kde', ax=axes[0, 1], color='green', linewidth=2)
axes[0, 1].set_title('Income Distribution (KDE)')
axes[0, 1].set_xlabel('Income')

# Box plot
sns.boxplot(data=data, y='satisfaction', x='category', ax=axes[1, 0], palette='Set2')
axes[1, 0].set_title('Satisfaction by Category (Box Plot)')

# Violin plot
sns.violinplot(data=data, y='age', x='category', ax=axes[1, 1], palette='Set2')
axes[1, 1].set_title('Age by Category (Violin Plot)')

plt.tight_layout()
plt.show()

# 2. Relationship Plots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Scatter plot
axes[0, 0].scatter(data['age'], data['income'], alpha=0.5, s=30)
axes[0, 0].set_title('Age vs Income (Scatter Plot)')
axes[0, 0].set_xlabel('Age')
axes[0, 0].set_ylabel('Income')

# Scatter with regression line
sns.regplot(x='age', y='income', data=data, ax=axes[0, 1], scatter_kws={'alpha': 0.5})
axes[0, 1].set_title('Age vs Income (with Regression Line)')

# Joint plot alternative
ax_hex = axes[1, 0]
hexbin = ax_hex.hexbin(data['age'], data['income'], gridsize=15, cmap='YlOrRd')
ax_hex.set_title('Age vs Income (Hex Bin)')
ax_hex.set_xlabel('Age')
ax_hex.set_ylabel('Income')

# Bubble plot
scatter = axes[1, 1].scatter(
    data['age'], data['income'], s=data['satisfaction']*50,
    c=data['satisfaction'], cmap='viridis', alpha=0.6, edgecolors='black'
)
axes[1, 1