zor

Here’s how Zor can help refactor a Python function for calculating user engagement metrics. The original function (80 lines) processes a dataset of 50,000 user sessions with nested loops, which is inefficient and hard to debug. Zor’s output includes:

1. **Performance Optimization**: Suggests replacing nested loops with vectorized operations using NumPy, reducing runtime from 45 seconds to 2.3 seconds for the dataset.
2. **Readability Improvements**: Breaks the function into smaller, modular components (e.g., `calculate_daily_engagement`, `aggregate_metrics`) and adds type hints for better maintainability.
3. **Error Handling**: Introduces validation checks for input data (e.g., ensuring session timestamps are in chronological order) and logs warnings for edge cases.
4. **Testing Guidance**: Provides a pytest script to verify the refactored function against the original output, ensuring no regression in results.

Zor also highlights potential pitfalls, such as memory usage when processing large datasets, and recommends using generators or chunking for scalability. The final output is a 25-line function with 90% fewer lines of code, improved clarity, and a 20x speedup. Example refactored code:
```python
def calculate_daily_engagement(sessions: list[Session]) -> dict[str, float]:
    """Calculate daily engagement metrics using vectorized operations."""
    df = pd.DataFrame([s.__dict__ for s in sessions])
    daily_metrics = df.groupby('date').agg({
        'clicks': 'sum',
        'time_spent': 'mean'
    }).to_dict('index')
    return daily_metrics
```