fix(dataframe): handle arrays in NA check for JSON data preservation

pd.isna() raises ValueError when called on arrays (lists/dicts from JSON).
Use a helper function that catches this exception and returns False for
array values.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

superset/dataframe.py

@@ -37,6 +37,22 @@ def _convert_big_integers(val: Any) -> Any:
     return str(val) if isinstance(val, int) and abs(val) > JS_MAX_INTEGER else val
 
 
+def _is_na(val: Any) -> bool:
+    """
+    Check if a value is NA/NaN for scalar values only.
+
+    pd.isna() raises ValueError for arrays/lists, so we catch that case.
+
+    :param val: the value to check
+    :returns: True if the value is NA/NaN, False otherwise
+    """
+    try:
+        return bool(pd.isna(val))
+    except ValueError:
+        # pd.isna raises ValueError for arrays (e.g., lists, dicts from JSON)
+        return False
+
+
 def df_to_records(dframe: pd.DataFrame) -> list[dict[str, Any]]:
     """
     Convert a DataFrame to a set of records.
@@ -56,7 +72,7 @@ def df_to_records(dframe: pd.DataFrame) -> list[dict[str, Any]]:
     for record in records:
         for key in record:
             record[key] = (
-                None if pd.isna(record[key]) else _convert_big_integers(record[key])
+                None if _is_na(record[key]) else _convert_big_integers(record[key])
             )
 
     return records