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Update app.py

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  1. app.py +1 -0
app.py CHANGED
@@ -725,6 +725,7 @@ CRITICAL RULES:
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  8. **CONSISTENT COLUMN NAMES:** After aggregation or renaming, always use the NEW column names in subsequent operations. Track renamed columns carefully - if you rename 'Zone' to 'zone', use 'zone' everywhere after.
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  9. **SAFE ITERATION:** When iterating over mixed data structures, always check types before accessing attributes. Not all list items are dicts (some may be strings), not all values have `.items()`.
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  10. **KEY-VALUE DATA PATTERN:** Many healthcare datasets use key-value format (e.g., columns: 'Indicator'/'Value' or 'Metric'/'Amount'). To extract a specific value, filter rows by the key column, then access the value column: `df.loc[df['Indicator'] == 'Cost per client', 'Value'].iloc[0]`
 
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  --- USER'S SCENARIO ---
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  {user_scenario}
 
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  8. **CONSISTENT COLUMN NAMES:** After aggregation or renaming, always use the NEW column names in subsequent operations. Track renamed columns carefully - if you rename 'Zone' to 'zone', use 'zone' everywhere after.
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  9. **SAFE ITERATION:** When iterating over mixed data structures, always check types before accessing attributes. Not all list items are dicts (some may be strings), not all values have `.items()`.
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  10. **KEY-VALUE DATA PATTERN:** Many healthcare datasets use key-value format (e.g., columns: 'Indicator'/'Value' or 'Metric'/'Amount'). To extract a specific value, filter rows by the key column, then access the value column: `df.loc[df['Indicator'] == 'Cost per client', 'Value'].iloc[0]`
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+ 11. **CONVERT STRINGS BEFORE MATH:** Always convert strings to float/int BEFORE performing arithmetic. When parsing ranges like "8–10", split first, convert each part to float, then calculate: `values = text.split('–'); avg = (float(values[0]) + float(values[1])) / 2`
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  --- USER'S SCENARIO ---
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  {user_scenario}