BUG: fix IntervalIndex for pivot table raise type error (#26765)

Author: peterpanmj

doc/source/whatsnew/v0.25.0.rst

@@ -726,6 +726,7 @@ Reshaping
 - Bug in :func:`pandas.cut` where large bins could incorrectly raise an error due to an integer overflow (:issue:`26045`)
 - Bug in :func:`DataFrame.sort_index` where an error is thrown when a multi-indexed ``DataFrame`` is sorted on all levels with the initial level sorted last (:issue:`26053`)
 - Bug in :meth:`Series.nlargest` treats ``True`` as smaller than ``False`` (:issue:`26154`)
+- Bug in :func:`DataFrame.pivot_table` with a :class:`IntervalIndex` as pivot index would raise ``TypeError`` (:issue:`25814`)
 
 Sparse
 ^^^^^^

pandas/core/arrays/categorical.py

@@ -181,7 +181,7 @@ def contains(cat, key, container):
     #  can't be in container either.
     try:
         loc = cat.categories.get_loc(key)
-    except KeyError:
+    except (KeyError, TypeError):
         return False
 
     # loc is the location of key in categories, but also the *value*

pandas/tests/reshape/test_pivot.py

@@ -19,6 +19,12 @@ def dropna(request):
     return request.param
 
 
+@pytest.fixture(params=[([0] * 4, [1] * 4), (range(0, 3), range(1, 4))])
+def interval_values(request, closed):
+    left, right = request.param
+    return Categorical(pd.IntervalIndex.from_arrays(left, right, closed))
+
+
 class TestPivotTable:
 
     def setup_method(self, method):
@@ -198,6 +204,18 @@ def test_pivot_with_non_observable_dropna(self, dropna):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_with_interval_index(self, interval_values, dropna):
+        # GH 25814
+        df = DataFrame(
+            {'A': interval_values,
+             'B': 1})
+        result = df.pivot_table(index='A', values='B', dropna=dropna)
+        expected = DataFrame(
+            {'B': 1},
+            index=Index(interval_values.unique(),
+                        name='A'))
+        tm.assert_frame_equal(result, expected)
+
     def test_pass_array(self):
         result = self.data.pivot_table(
             'D', index=self.data.A, columns=self.data.C)