Analysis Dashboard

Upload your analysis results to visualize code complexity and explore detailed breakdowns.

Level Distribution

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CEFR Level Analysis

How to Use This Dashboard

1. Upload Results

Click "Load JSON File" to upload your data.json or summary_data.json file from a pycefrl analysis.

2. Explore Visualizations

View level distribution charts, element breakdowns, and file-by-file analysis.

3. Filter & Search

Use the search box and level filter to focus on specific code elements or complexity levels.

4. Export Data

Download filtered results as CSV for further analysis in Excel or other tools.

Understanding the Visualizations

Level Distribution

Shows the count of code elements at each CEFR level (A1-C2). Higher levels indicate more complex code patterns.

CEFR Level Chart

Bar chart visualization of how many elements exist at each complexity level. Useful for getting a quick overview of overall code complexity.

Top Code Elements

Displays the most frequently used code constructs in your project, helping identify common patterns and potential areas for refactoring.

File-Level Analysis

Detailed breakdown showing which files contain elements at each level. Helps identify which files might benefit from simplification or refactoring.

Interpretation Guide

What High C1/C2 Percentages Mean

A high percentage of advanced (C1/C2) elements might indicate:

  • Complex, sophisticated code that uses advanced Python features
  • Code that may be harder for junior developers to understand
  • Good use of Pythonic idioms (comprehensions, generators, etc.)
  • Potential areas where simpler alternatives might improve readability

What High A1/A2 Percentages Mean

A high percentage of basic (A1/A2) elements might indicate:

  • Code that is easy to read and understand
  • Good for beginners and learning resources
  • Potential opportunities to use more advanced Python features
  • Scripts or simple utilities that don't require complexity

Balanced Distribution

A balanced distribution across levels often indicates:

  • Well-structured code with appropriate complexity for different tasks
  • Good separation of concerns (simple utilities + complex business logic)
  • Code that's accessible to developers of various skill levels

Need Help?

For more information on running analyses and generating data files: