pandas-dev#35028 DFGroupBy.count() now returns zero for missing categories when groupby by multiple categories

Author: smithto1

pandas/core/groupby/generic.py

@@ -31,7 +31,7 @@
 import numpy as np
 
 from pandas._libs import lib
-from pandas._typing import FrameOrSeries, FrameOrSeriesUnion
+from pandas._typing import FrameOrSeries, FrameOrSeriesUnion, Scalar
 from pandas.util._decorators import Appender, Substitution, doc
 
 from pandas.core.dtypes.cast import (
@@ -1788,7 +1788,9 @@ def _wrap_transformed_output(
 
         return result
 
-    def _wrap_agged_blocks(self, blocks: "Sequence[Block]", items: Index) -> DataFrame:
+    def _wrap_agged_blocks(
+        self, blocks: "Sequence[Block]", items: Index, fill_value: Scalar = np.NaN
+    ) -> DataFrame:
         if not self.as_index:
             index = np.arange(blocks[0].values.shape[-1])
             mgr = BlockManager(blocks, axes=[items, index])
@@ -1804,7 +1806,7 @@ def _wrap_agged_blocks(self, blocks: "Sequence[Block]", items: Index) -> DataFra
         if self.axis == 1:
             result = result.T
 
-        return self._reindex_output(result)._convert(datetime=True)
+        return self._reindex_output(result, fill_value)._convert(datetime=True)
 
     def _iterate_column_groupbys(self):
         for i, colname in enumerate(self._selected_obj.columns):
@@ -1846,7 +1848,7 @@ def count(self):
         )
         blocks = [make_block(val, placement=loc) for val, loc in zip(counted, locs)]
 
-        return self._wrap_agged_blocks(blocks, items=data.items)
+        return self._wrap_agged_blocks(blocks, items=data.items, fill_value=0)
 
     def nunique(self, dropna: bool = True):
         """

pandas/tests/groupby/test_categorical.py

@@ -1412,15 +1412,6 @@ def test_dataframe_groupby_on_2_categoricals_when_observed_is_false(
     if reduction_func == "ngroup":
         pytest.skip("ngroup does not return the Categories on the index")
 
-    if reduction_func == "count":  # GH 35028
-        mark = pytest.mark.xfail(
-            reason=(
-                "DataFrameGroupBy.count returns np.NaN for missing "
-                "categories, when it should return 0. See GH 35028"
-            )
-        )
-        request.node.add_marker(mark)
-
     if reduction_func == "sum":  # GH 31422
         mark = pytest.mark.xfail(
             reason=(