perf: skip NaN restore for float columns without NaN

Signed-off-by: Maha Rana Yadavalli <271375718+maharanay22@users.noreply.github.com>

Author: maharanay22

src/databricks/sql/result_set.py

@@ -135,9 +135,14 @@ def _convert_arrow_table(self, table):
         # The nullable float dtypes turn IEEE NaN into NA, which would make it
         # indistinguishable from SQL NULL, so restore NaN values from Arrow.
         for i, field in enumerate(table_renamed.schema):
-            if pyarrow.types.is_floating(field.type):
-                is_nan = pyarrow.compute.is_nan(table_renamed.column(i))
-                res[is_nan.fill_null(False).to_numpy(), i] = float("nan")
+            if not pyarrow.types.is_floating(field.type):
+                continue
+            nan_mask = pyarrow.compute.is_nan(table_renamed.column(i))
+            if not pyarrow.compute.any(nan_mask).as_py():
+                continue
+            if nan_mask.null_count:
+                nan_mask = nan_mask.fill_null(False)
+            res[nan_mask.to_numpy(), i] = float("nan")
 
         return [ResultRow(*v) for v in res]