fix: prevent DataFrame mutation in prepare_data() causing indication charts to fail

prepare_data() mapped Provider Code → Name in-place. When called for directory
charts first, then indication charts, the second call re-mapped already-mapped
values to NaN, silently dropping all data. Added df.copy() to prevent mutation.

Also fixes directory charts only generating data for the first date filter.

Results: 3,633 pathway nodes now generated (1,101 directory + 2,532 indication)
across all 12 datasets (6 date filters × 2 chart types).

guardrails.md

@@ -221,6 +221,11 @@ def filtered_count(self) -> int:
 - **Rule**: Check `pd.notna(directory)` before concatenating to string. Use `"UNKNOWN (no GP dx)"` for NaN cases.
 - **Why**: `str(nan) + " (no GP dx)"` doesn't cause error, but `nan + " (no GP dx)"` causes TypeError. Always be explicit about NaN handling.
 
+### Copy DataFrames in functions that modify columns
+- **When**: Writing functions like `prepare_data()` that modify DataFrame columns (e.g., mapping Provider Code to trust names)
+- **Rule**: Always `df = df.copy()` at the start of any function that modifies column values on the input DataFrame
+- **Why**: `prepare_data()` mapped Provider Code → Name in-place. When called for directory charts first, then indication charts second, the second call tried to map already-mapped names → NaN, silently dropping all data. The fix: `df = df.copy()` prevents destructive mutation of the caller's DataFrame.
+
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 ADD NEW GUARDRAILS BELOW as failures are observed during the loop.