fix(pivot): default missing series to NULL_STRING (apache#14748)

superset/utils/pandas_postprocessing.py

@@ -25,6 +25,7 @@
 from geopy.point import Point
 from pandas import DataFrame, NamedAgg, Series, Timestamp
 
+from superset.constants import NULL_STRING
 from superset.exceptions import QueryObjectValidationError
 from superset.utils.core import (
     DTTM_ALIAS,
@@ -214,7 +215,7 @@ def pivot(  # pylint: disable=too-many-arguments
     aggregates: Dict[str, Dict[str, Any]],
     columns: Optional[List[str]] = None,
     metric_fill_value: Optional[Any] = None,
-    column_fill_value: Optional[str] = None,
+    column_fill_value: Optional[str] = NULL_STRING,
     drop_missing_columns: Optional[bool] = True,
     combine_value_with_metric: bool = False,
     marginal_distributions: Optional[bool] = None,
@@ -228,7 +229,9 @@ def pivot(  # pylint: disable=too-many-arguments
     :param index: Columns to group by on the table index (=rows)
     :param columns: Columns to group by on the table columns
     :param metric_fill_value: Value to replace missing values with
-    :param column_fill_value: Value to replace missing pivot columns with
+    :param column_fill_value: Value to replace missing pivot columns with. By default
+           replaces missing values with "<NULL>". Set to `None` to remove columns
+           with missing values.
     :param drop_missing_columns: Do not include columns whose entries are all missing
     :param combine_value_with_metric: Display metrics side by side within each column,
            as opposed to each column being displayed side by side for each metric.
@@ -250,7 +253,7 @@ def pivot(  # pylint: disable=too-many-arguments
             _("Pivot operation must include at least one aggregate")
         )
 
-    if column_fill_value:
+    if columns and column_fill_value:
         df[columns] = df[columns].fillna(value=column_fill_value)
 
     aggregate_funcs = _get_aggregate_funcs(df, aggregates)

tests/pandas_postprocessing_tests.py

@@ -197,6 +197,21 @@ def test_pivot_fill_values(self):
         )
         self.assertEqual(df.sum()[1], 382)
 
+    def test_pivot_fill_column_values(self):
+        """
+        Make sure pivot witn null column names returns correct DataFrame
+        """
+        df_copy = categories_df.copy()
+        df_copy["category"] = None
+        df = proc.pivot(
+            df=df_copy,
+            index=["name"],
+            columns=["category"],
+            aggregates={"idx_nulls": {"operator": "sum"}},
+        )
+        assert len(df) == 101
+        assert df.columns.tolist() == ["name", "<NULL>"]
+
     def test_pivot_exceptions(self):
         """
         Make sure pivot raises correct Exceptions