📊 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 (NumPy, Pandas, Matplotlib, Seaborn, and SciPy). The visualizations demonstrate various chart types and data patterns including sales trends, temporal data, correlations, and statistical distributions.

Generated Visualizations

Chart 1: Sales by Category (Bar Chart)

This bar chart displays total sales across five different product categories (Electronics, Clothing, Food, Books, and Home). The data includes seasonal variations simulated with sinusoidal patterns, showing how different categories perform over a 12-month period. Each bar is labeled with the exact sales value for easy reference.

**Key (redacted)

• Data aggregated across 12 months for 5 categories
• Total of 60 sales records
• Seasonal patterns incorporated into the data generation

Chart 2: Temperature Trend Over Time (Line Chart)

This line chart illustrates daily temperature readings over an entire year (365 days), showing natural cyclical patterns with a 30-day moving average overlay. The data simulates realistic temperature variations with a yearly sine wave pattern plus random daily fluctuations.

**Key (redacted)

• 365 daily temperature readings
• Base temperature of 15°C with ±10°C seasonal variation
• Random noise added to simulate real-world variability
• 30-day moving average smooths short-term fluctuations

Chart 3: Height vs Weight Correlation (Scatter Plot)

This scatter plot demonstrates the correlation between height and weight across 200 sample data points. A linear regression line is fitted to show the trend, and the correlation coefficient is displayed in the title. The color gradient represents weight values, providing an additional visual dimension.

**Key (redacted)

• 200 sample data points
• Strong positive correlation between height and weight
• Linear regression line shows the trend relationship
• Color-coded by weight for enhanced visualization

Chart 4: Test Score Distribution (Histogram with KDE)

This distribution plot shows a bimodal distribution of test scores from 500 students, with a histogram and kernel density estimate (KDE) overlay. The chart includes mean and median reference lines, illustrating the two distinct performance groups in the simulated data.

**Key (redacted)

• 500 student test scores
• Bimodal distribution (two groups: mean ~65 and ~85)
• KDE curve smoothly represents the probability density
• Mean and median lines show central tendency measures

Data Information

Dataset Details

Dataset	Records	Variables	Pattern
Sales Data	60	month, category, sales	Seasonal trends with sinusoidal variation
Temperature Data	365	date, temperature	Annual cycle with random daily noise
Correlation Data	200	height, weight	Linear positive correlation (r ≈ 0.95)
Distribution Data	500	score	Bimodal distribution (two student groups)

Data Generation Methodology

All data was generated using NumPy's random number generators with a fixed seed (42) for reproducibility:

• Sales Data: Combines random baseline values with seasonal factors using sine waves
• Temperature Data: Uses sinusoidal patterns for yearly cycles plus Gaussian noise
• Correlation Data: Height values from normal distribution, weight calculated with linear relationship plus noise
• Distribution Data: Two normal distributions combined to create bimodal pattern

Libraries Used

• NumPy: Array processing, random number generation, and numerical operations
• Pandas: Data manipulation, CSV operations, and data analysis
• Matplotlib: Chart generation, customization, and high-quality output
• Seaborn: Statistical data visualization and aesthetic styling
• SciPy: Kernel density estimation and scientific computing utilities

Technical Specifications

• Chart Resolution: 300 DPI (publication quality)
• Figure Sizes: 10x6 inches (standard charts), 12x6 inches (time series)
• Color Palette: Seaborn "husl" palette for colorblind-friendly visualization
• Style: Whitegrid background for improved readability
• Output Format: PNG with transparent backgrounds and tight bounding boxes

Workflow Run

• Repository: githubnext/gh-aw
• Run ID: 18988081848
• Run URL: https://github.com/githubnext/gh-aw/actions/runs/18988081848

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This report was automatically generated by the Python Data Visualization Generator workflow. All data is randomly generated for demonstration purposes and does not represent real-world measurements.

AI generated by Python Data Visualization Generator