data-visualization

name	Data Visualization
description	Create effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholders

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].set_title('Age vs Income (Bubble Plot)')
axes[1, 1].set_xlabel('Age')
axes[1, 1].set_ylabel('Income')
plt.colorbar(scatter, ax=axes[1, 1], label='Satisfaction')

plt.tight_layout()
plt.show()

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

# Bar plot
category_counts = data['category'].value_counts()
axes[0, 0].bar(category_counts.index, category_counts.values, color='skyblue', edgecolor='black')
axes[0, 0].set_title('Category Distribution (Bar Chart)')
axes[0, 0].set_ylabel('Count')

# Grouped bar plot
grouped_data = data.groupby(['category', 'region']).size().unstack()
grouped_data.plot(kind='bar', ax=axes[0, 1], edgecolor='black')
axes[0, 1].set_title('Category by Region (Grouped Bar)')
axes[0, 1].set_ylabel('Count')
axes[0, 1].legend(title='Region')

# Stacked bar plot
grouped_data.plot(kind='bar', stacked=True, ax=axes[1, 0], edgecolor='black')
axes[1, 0].set_title('Category by Region (Stacked Bar)')
axes[1, 0].set_ylabel('Count')

# Horizontal bar plot
region_counts = data['region'].value_counts()
axes[1, 1].barh(region_counts.index, region_counts.values, color='lightcoral', edgecolor='black')
axes[1, 1].set_title('Region Distribution (Horizontal Bar)')
axes[1, 1].set_xlabel('Count')

plt.tight_layout()
plt.show()

# 4. Correlation and Heatmaps
numeric_cols = data[['age', 'income', 'education_years', 'satisfaction']].corr()

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Correlation heatmap
sns.heatmap(numeric_cols, annot=True, fmt='.2f', cmap='coolwarm', center=0,
            square=True, ax=axes[0], cbar_kws={'label': 'Correlation'})
axes[0].set_title('Correlation Matrix Heatmap')

# Clustermap alternative
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import pdist, squareform

# Create a simpler heatmap for category averages
category_avg = data.groupby('category')[['age', 'income', 'education_years', 'satisfaction']].mean()
sns.heatmap(category_avg.T, annot=True, fmt='.1f', cmap='YlGnBu', ax=axes[1],
            cbar_kws={'label': 'Average Value'})
axes[1].set_title('Average Values by Category')

plt.tight_layout()
plt.show()

# 5. Pair Plot
pair_cols = ['age', 'income', 'education_years', 'satisfaction']
plt.figure(figsize=(12, 10))
pair_plot = sns.pairplot(data[pair_cols], diag_kind='hist', corner=False)
pair_plot.fig.suptitle('Pair Plot Matrix', y=1.00)
plt.show()

# 6. Multi-dimensional Visualization
fig = plt.figure(figsize=(14, 6))
gs = GridSpec(2, 3, figure=fig)

# Subplots with different aspects
ax1 = fig.add_subplot(gs[0, 0])
ax1.scatter(data['age'], data['income'], c=data['satisfaction'], cmap='viridis', alpha=0.6)
ax1.set_title('Age vs Income (colored by Satisfaction)')
ax1.set_xlabel('Age')
ax1.set_ylabel('Income')

ax2 = fig.add_subplot(gs[0, 1])
for cat in data['category'].unique():
    subset = data[data['category'] == cat]
    ax2.scatter(subset['age'], subset['income'], label=cat, alpha=0.6)
ax2.set_title('Age vs Income (by Category)')
ax2.set_xlabel('Age')
ax2.set_ylabel('Income')
ax2.legend()

ax3 = fig.add_subplot(gs[0, 2])
sns.boxplot(data=data, x='region', y='income', ax=ax3, palette='Set2')
ax3.set_title('Income Distribution by Region')

ax4 = fig.add_subplot(gs[1, 0])
data.groupby('category')['satisfaction'].mean().plot(kind='bar', ax=ax4, color='skyblue', edgecolor='black')
ax4.set_title('Average Satisfaction by Category')
ax4.set_ylabel('Satisfaction')
ax4.set_xlabel('Category')

ax5 = fig.add_subplot(gs[1, 1:])
region_category = pd.crosstab(data['region'], data['category'])
region_category.plot(kind='bar', ax=ax5, edgecolor='black')
ax5.set_title('Region vs Category Distribution')
ax5.set_ylabel('Count')
ax5.set_xlabel('Region')
ax5.legend(title='Category')

plt.tight_layout()
plt.show()

# 7. Time Series Visualization (if temporal data)
dates = pd.date_range('2023-01-01', periods=len(data))
data['date'] = dates
data['cumulative_income'] = data['income'].cumsum()

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

# Line plot
axes[0].plot(data['date'], data['income'], linewidth=1, alpha=0.7, label='Income')
axes[0].fill_between(data['date'], data['income'], alpha=0.3)
axes[0].set_title('Income Over Time')
axes[0].set_ylabel('Income')
axes[0].grid(True, alpha=0.3)
axes[0].legend()

# Area plot
axes[1].plot(data['date'], data['cumulative_income'], linewidth=2, color='green')
axes[1].fill_between(data['date'], data['cumulative_income'], alpha=0.3, color='green')
axes[1].set_title('Cumulative Income Over Time')
axes[1].set_ylabel('Cumulative Income')
axes[1].set_xlabel('Date')
axes[1].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# 8. Composition Visualization
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Pie chart
category_counts = data['category'].value_counts()
colors = ['#ff9999', '#66b3ff', '#99ff99']
axes[0].pie(category_counts.values, labels=category_counts.index, autopct='%1.1f%%',
            colors=colors, startangle=90)
axes[0].set_title('Category Distribution (Pie Chart)')

# Donut chart
axes[1].pie(category_counts.values, labels=category_counts.index, autopct='%1.1f%%',
            colors=colors, startangle=90, wedgeprops=dict(width=0.5, edgecolor='white'))
axes[1].set_title('Category Distribution (Donut Chart)')

plt.tight_layout()
plt.show()

# 9. Dashboard-style Visualization
fig = plt.figure(figsize=(16, 10))
gs = GridSpec(3, 3, figure=fig, hspace=0.3, wspace=0.3)

# Key metrics
ax_metric = fig.add_subplot(gs[0, :])
ax_metric.axis('off')
metrics_text = f"""
Average Age: {data['age'].mean():.1f} | Average Income: ${data['income'].mean():.0f} |
Average Satisfaction: {data['satisfaction'].mean():.2f} | Purchase Rate: {(data['purchased'].mean()*100):.1f}%
"""
ax_metric.text(0.5, 0.5, metrics_text, ha='center', va='center', fontsize=12,
               bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.7))

# Subplots
ax1 = fig.add_subplot(gs[1, 0])
data['age'].hist(bins=20, ax=ax1, color='skyblue', edgecolor='black')
ax1.set_title('Age Distribution')

ax2 = fig.add_subplot(gs[1, 1])
category_counts.plot(kind='bar', ax=ax2, color='lightcoral', edgecolor='black')
ax2.set_title('Category Counts')

ax3 = fig.add_subplot(gs[1, 2])
data.groupby('category')['satisfaction'].mean().plot(kind='bar', ax=ax3, color='lightgreen', edgecolor='black')
ax3.set_title('Avg Satisfaction by Category')

ax4 = fig.add_subplot(gs[2, :2])
sns.boxplot(data=data, x='region', y='income', ax=ax4, palette='Set2')
ax4.set_title('Income by Region')

ax5 = fig.add_subplot(gs[2, 2])
data['satisfaction'].value_counts().sort_index().plot(kind='bar', ax=ax5, color='orange', edgecolor='black')
ax5.set_title('Satisfaction Scores')

plt.suptitle('Data Analytics Dashboard', fontsize=16, fontweight='bold', y=0.995)
plt.show()

print("Visualization examples completed!")

Visualization Best Practices

• Choose chart type based on data type and question
• Use consistent color schemes
• Label axes clearly with units
• Include title and legend
• Avoid 3D charts when 2D suffices
• Make fonts large and readable
• Consider colorblind-friendly palettes

Common Chart Types

• Bar charts: Categorical comparisons
• Line plots: Trends over time
• Scatter plots: Relationships between variables
• Histograms: Distributions
• Heatmaps: Matrix data
• Box plots: Distribution with quartiles

Deliverables

• Exploratory visualizations
• Publication-ready charts
• Interactive dashboard mockups
• Statistical plots with annotations
• Trend analysis visualizations
• Comparative analysis charts
• Summary infographics