FIX: Fixed and Added tests for Type Inference Bug

mssql_python/cursor.py

@@ -1474,35 +1474,6 @@ def columns(self, table=None, catalog=None, schema=None, column=None):
         # Use the helper method to prepare the result set
         return self._prepare_metadata_result_set(fallback_description=fallback_description)
 
-    @staticmethod
-    def _select_best_sample_value(column):
-        """
-        Selects the most representative non-null value from a column for type inference.
-
-        This is used during executemany() to infer SQL/C types based on actual data,
-        preferring a non-null value that is not the first row to avoid bias from placeholder defaults.
-
-        Args:
-            column: List of values in the column.
-        """
-        non_nulls = [v for v in column if v is not None]
-        if not non_nulls:
-            return None
-        if all(isinstance(v, int) for v in non_nulls):
-            # Pick the value with the widest range (min/max)
-            return max(non_nulls, key=lambda v: abs(v))
-        if all(isinstance(v, float) for v in non_nulls):
-            return 0.0
-        if all(isinstance(v, decimal.Decimal) for v in non_nulls):
-            return max(non_nulls, key=lambda d: len(d.as_tuple().digits))
-        if all(isinstance(v, str) for v in non_nulls):
-            return max(non_nulls, key=lambda s: len(str(s)))
-        if all(isinstance(v, datetime.datetime) for v in non_nulls):
-            return datetime.datetime.now()
-        if all(isinstance(v, datetime.date) for v in non_nulls):
-            return datetime.date.today()
-        return non_nulls[0]  # fallback
-
     def _transpose_rowwise_to_columnwise(self, seq_of_parameters: list) -> tuple[list, int]:
         """
         Convert sequence of rows (row-wise) into list of columns (column-wise),
@@ -1675,11 +1646,7 @@ def executemany(self, operation: str, seq_of_parameters: list) -> None:
             else:
                 # Use auto-detection for columns without explicit types
                 column = [row[col_index] for row in seq_of_parameters] if hasattr(seq_of_parameters, '__getitem__') else []
-                if not column:
-                    # For generators, use the sample row for inference
-                    sample_value = sample_row[col_index]
-                else:
-                    sample_value = self._select_best_sample_value(column)
+                sample_value, min_val, max_val = self._compute_column_type(column)
 
                 dummy_row = list(sample_row)
                 paraminfo = self._create_parameter_types_list(