data-visualization

name	data-visualization
description	Data visualization techniques
license	MIT
compatibility	opencode
metadata	{"audience":"data-analysts","category":"data-science"}

What I do

• Create effective data visualizations
• Design dashboards and reports
• Choose appropriate chart types
• Implement interactive visualizations
• Apply color theory and design principles
• Tell stories with data
• Build self-service analytics

When to use me

Use me when:

• Presenting data to stakeholders
• Building analytics dashboards
• Exploring data patterns
• Creating reports and infographics
• Communicating insights visually

Key Concepts

Chart Selection Guide

Relationship	Chart Type
Comparison	Bar, Column, Grouped
Distribution	Histogram, Box Plot
Composition	Pie, Stacked Bar, Treemap
Trend	Line, Area
Correlation	Scatter Plot
Geographic	Choropleth, Map

Python Visualization Stack

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Seaborn for statistical visualization
sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Distribution plot
sns.histplot(data=tips, x="total_bill", hue="day", 
             ax=axes[0], kde=True)

# Relationship plot
sns.scatterplot(data=tips, x="total_bill", y="tip", 
                hue="smoker", size="size", ax=axes[1])

plt.tight_layout()
plt.show()

# Interactive with Plotly
fig = px.scatter(tips, x="total_bill", y="tip", 
                  color="smoker", size="size",
                  title="Tips Analysis")
fig.show()

Dashboard Design Principles

• Clarity: Clear purpose, minimal clutter
• Hierarchy: Most important metrics prominent
• Consistency: Unified color, fonts, layout
• Interactivity: Allow exploration
• Responsiveness: Work on all devices

BI Tools

• Tableau: Enterprise, powerful
• Power BI: Microsoft ecosystem
• Looker: Data modeling (LookML)
• Metabase: Open-source, simple
• Grafana: Metrics and monitoring