📊 Data Visualization Report - Random Sample Data

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📊 Data Visualization Report

Generated on: November 1, 2024

Summary

This report contains data visualizations generated from randomly generated sample data using Python scientific computing libraries. The visualizations showcase various chart types and demonstrate different statistical patterns and relationships in the data.

Generated Visualizations

Chart 1: Product Revenue Bar Chart

This bar chart displays revenue comparison across five product categories (A through E). Each product shows varying revenue levels, with values clearly labeled on top of each bar. The chart uses a colorful palette to distinguish between products and includes gridlines for easier value reading.

Chart 2: Time Series Line Chart

This line chart visualizes a full year (365 days) of time series data with both daily values and a trend line. The data exhibits a clear upward trend overlaid with seasonal patterns, demonstrating typical business metrics behavior. The trend line (shown in red) helps identify the overall direction despite daily fluctuations.

Chart 3: Correlation Scatter Plot

This scatter plot displays 200 data points across three distinct clusters, showing a strong positive correlation between X and Y variables. The correlation line (r-value displayed in legend) demonstrates the linear relationship between variables. Different clusters are color-coded for easy identification, with white borders around points for better visibility.

Chart 4: Distribution Comparison

This multi-panel visualization compares three different statistical distributions side by side:

• Normal Distribution (blue): Shows the classic bell curve centered around mean ≈ 100
• Exponential Distribution (red): Displays the characteristic right-skewed pattern
• Uniform Distribution (green): Demonstrates evenly distributed values across the range

Each histogram includes a mean line for reference, making it easy to compare central tendencies across distributions.

Data Information

• Data Generation Method: Random sample data using NumPy with seed=42 for reproducibility
• Total Data Points: 1,570 across all datasets
• Datasets Created: 4 distinct datasets
  • Time series: 365 daily observations
  • Categorical: 5 product categories with sales and revenue
  • Scatter data: 200 points across 3 clusters
  • Distributions: 1,000 samples each for normal, exponential, and uniform distributions
• Variables: date, value, category, sales, revenue, x, y, cluster, normal, exponential, uniform
• Patterns Demonstrated:
  • Linear trends with seasonal variation
  • Categorical comparisons
  • Positive correlation (r ≈ 0.7-0.8)
  • Various probability distributions

Technical Details

Libraries Used

• NumPy (1.26.4): Array processing and numerical operations
• Pandas (2.2.0): Data manipulation and analysis
• Matplotlib (3.8.3): Chart generation and plotting
• Seaborn (0.13.2): Statistical data visualization with enhanced aesthetics
• SciPy (1.12.0): Scientific computing utilities (correlation analysis)

Chart Quality Settings

All visualizations were generated with the following high-quality settings:

• DPI: 300 (publication quality)
• Style: Seaborn "whitegrid" for clean, professional appearance
• Color Palette: "husl" for colorblind-friendly, visually distinct colors
• Figure Sizes: Optimized for each chart type (10x6 to 15x5 inches)
• Format: PNG with white background and no borders

Workflow Run

• Repository: githubnext/gh-aw
• Run ID: 18988502782
• Run URL: https://github.com/githubnext/gh-aw/actions/runs/18988502782

Files Generated

All source files and data are available as workflow artifacts:

• Data Files: 4 CSV files in /tmp/gh-aw/python/data/
• Chart Images: 4 PNG files (300 DPI) in /tmp/gh-aw/python/charts/
• Python Scripts: 5 Python scripts for data generation and visualization

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This report was automatically generated by the Python Data Visualization Generator workflow using GitHub Agentic Workflows.

AI generated by Python Data Visualization Generator