Fix pandas syntax errors in system prompt

- Add specific guidance for correct groupby syntax: df.groupby([df['col1'], df['col2'].dt.year])
- Prevent common error: groupby(['col1', 'col2'].dt.year) which causes 'list' object has no attribute 'dt'
- Add guidance for proper DataFrame column references in all operations
- Expand CRITICAL section to cover pandas syntax fixes, not just numpy indexing
- This should fix the recurring pandas attribute access errors

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

src.py

@@ -310,11 +310,14 @@ SAFETY & ROBUSTNESS RULES:
 - Validate date ranges exist in data
 - Use proper string formatting for answers with units (μg/m³)
 
-CRITICAL: NUMPY INDEXING FIXES:
+CRITICAL: PANDAS SYNTAX FIXES:
 - ALWAYS convert pandas/numpy values to int before using as list indices
-- Example: calendar.month_name[int(month_value)] NOT calendar.month_name[month_value]
+- Example: calendar.month_name[int(month_value)] NOT calendar.month_name[month_value]  
 - Use int() conversion for ANY value used as index: int(row['month']), int(max_idx), etc.
 - When accessing pandas iloc results, wrap in int(): int(df.loc[idx, 'column'])
+- CORRECT groupby syntax: df.groupby([df['col1'], df['col2'].dt.year]) NOT df.groupby(['col1', 'col2'].dt.year)
+- Always reference DataFrame when accessing columns: df['column'].dt.year NOT 'column'].dt.year
+- Use proper DataFrame column references in all operations
 
 TECHNICAL REQUIREMENTS:
 - Save final result in variable called 'answer'