📊 Data Visualization Report - Sample Data

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📊 Data Visualization Report

Generated on: 2025-10-31 (redacted) UTC

Summary

This report contains data visualizations generated from sample data using Python scientific computing libraries (NumPy, Pandas, Matplotlib, Seaborn, SciPy).

Generated Charts

• Chart Type: Bar Chart
• Data Source: Sample Data (8 categories)
• Chart File: sample_bar_chart.png
• Resolution: 300 DPI (publication quality)
• Dimensions: 12.0 x 7.0 inches
• Generated: 2025-10-31 (redacted) UTC

Artifacts

📈 Charts Artifact

• Artifact Name: data-charts
• Download URL: https://github.com/githubnext/gh-aw/actions/runs/18986589679
• Contents: High-resolution PNG image files of generated charts
• Retention: 30 days

📦 Source and Data Artifact

• Artifact Name: python-source-and-data
• Download URL: https://github.com/githubnext/gh-aw/actions/runs/18986589679
• Contents: Python scripts, data files (CSV), and documentation
• Retention: 30 days

Data Information

Dataset: sample_data.csv

Statistics:

• Total records: 8
• Categories: 8 unique (Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Theta)
• Value range: 45-83
• Mean value: 61.38
• Data format: CSV with columns category and value

Description: This sample dataset demonstrates bar chart visualization capabilities with categorical data across 8 categories and their corresponding numerical values.

Reproduction Instructions

To reproduce these visualizations locally:

1. Download the python-source-and-data artifact from the workflow run
2. Extract the archive to access Python scripts and data files
3. Install required dependencies:

   pip install numpy pandas matplotlib seaborn scipy

4. Run the chart generator:

   cd python/
   python3 generate_chart.py

5. Generated chart will be saved to charts/sample_bar_chart.png

Libraries Used

• NumPy: Array processing and numerical operations
• Pandas: Data manipulation and analysis (CSV data loading)
• Matplotlib: Chart generation and plotting framework
• Seaborn: Statistical data visualization with enhanced aesthetics
• SciPy: Scientific computing utilities

Chart Features

✅ High Quality: 300 DPI resolution suitable for publications
✅ Professional Styling: Seaborn "husl" color palette with whitegrid style
✅ Value Labels: Each bar displays its numerical value
✅ Grid Lines: Y-axis gridlines for easier value reading
✅ Clear Labels: Bold axis labels and descriptive title
✅ Timestamp: Generation timestamp included on chart

Cache Memory

Reusable helper functions have been saved to cache memory for future workflow runs:

• chart_utils.py: Common chart styling and utility functions

Workflow Run

• Repository: githubnext/gh-aw
• Run ID: 18986589679
• Run URL: https://github.com/githubnext/gh-aw/actions/runs/18986589679

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This report was automatically generated by the Python Data Visualization Generator workflow.

AI generated by Python Data Visualization Generator

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This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago.