BUG: fix ngroups and len(groups) inconsistency when using [Grouper(freq=)] (GH33132)

pandas/core/groupby/ops.py

@@ -39,6 +39,7 @@
 from pandas.core.dtypes.missing import _maybe_fill, isna
 
 import pandas.core.algorithms as algorithms
+from pandas.core.arrays import Categorical
 from pandas.core.base import SelectionMixin
 import pandas.core.common as com
 from pandas.core.frame import DataFrame
@@ -837,11 +838,22 @@ def names(self):
 
     @property
     def groupings(self) -> "List[grouper.Grouping]":
+        codes, _, _ = self.group_info
+
+        if self.indexer is not None and len(self.indexer) != len(codes):
+            groupers = self.levels
+        else:
+            groupers = [self.result_index._constructor(
+                Categorical.from_codes(self.codes_info, self.result_index))]
+
         return [
             grouper.Grouping(lvl, lvl, in_axis=False, level=None, name=name)
-            for lvl, name in zip(self.levels, self.names)
+            for lvl, name in zip(groupers, self.names)
         ]
 
+    def __iter__(self):
+        return iter(self.groupings[0].grouper)
+
     def agg_series(self, obj: Series, func):
         # Caller is responsible for checking ngroups != 0
         assert self.ngroups != 0

pandas/tests/groupby/test_groupby.py

@@ -1863,6 +1863,17 @@ def test_groupby_groups_in_BaseGrouper():
     expected = df.groupby(["beta", "alpha"])
     assert result.groups == expected.groups
 
+    # GH 33132
+    # Test if DataFrame grouped with a pandas.Grouper and freq param has correct groups
+    mi = pd.MultiIndex.from_product([date_range(datetime.today(), periods=2),
+                                    ["C", "D"]], names=["alpha", "beta"])
+    df = pd.DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
+    result = df.groupby(["beta", pd.Grouper(level="alpha", freq='D')])
+    assert result.ngroups == len(result)
+
+    result = df.groupby([pd.Grouper(level="alpha", freq='D'), "beta"])
+    assert result.ngroups == len(result)
+
 
 @pytest.mark.parametrize("group_name", ["x", ["x"]])
 def test_groupby_axis_1(group_name):