BUG: Support categorical targets in IntervalIndex.get_indexer

doc/source/whatsnew/v1.0.0.rst

@@ -711,6 +711,7 @@ Numeric
 - Bug in :class:`NumericIndex` construction that caused indexing to fail when integers in the ``np.uint64`` range were used (:issue:`28023`)
 - Bug in :class:`NumericIndex` construction that caused :class:`UInt64Index` to be casted to :class:`Float64Index` when integers in the ``np.uint64`` range were used to index a :class:`DataFrame` (:issue:`28279`)
 - Bug in :meth:`Series.interpolate` when using method=`index` with an unsorted index, would previously return incorrect results. (:issue:`21037`)
+- Bug in :meth:`DataFrame.round` where a :class:`DataFrame` with a :class:`CategoricalIndex` of :class:`IntervalIndex` columns would incorrectly raise a ``TypeError`` (:issue:`30063`)
 
 Conversion
 ^^^^^^^^^^
@@ -728,7 +729,7 @@ Strings
 Interval
 ^^^^^^^^
 
--
+- Bug in :meth:`IntervalIndex.get_indexer` where a :class:`Categorical` or :class:`CategoricalIndex` ``target`` would incorrectly raise a ``TypeError`` (:issue:`30063`)
 -
 
 Indexing

pandas/core/indexes/interval.py

@@ -19,6 +19,7 @@
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
+    is_categorical,
     is_datetime64tz_dtype,
     is_datetime_or_timedelta_dtype,
     is_dtype_equal,
@@ -36,6 +37,7 @@
 from pandas.core.dtypes.missing import isna
 
 from pandas._typing import AnyArrayLike
+from pandas.core.algorithms import take_1d
 from pandas.core.arrays.interval import IntervalArray, _interval_shared_docs
 import pandas.core.common as com
 import pandas.core.indexes.base as ibase
@@ -958,6 +960,10 @@ def get_indexer(
             left_indexer = self.left.get_indexer(target_as_index.left)
             right_indexer = self.right.get_indexer(target_as_index.right)
             indexer = np.where(left_indexer == right_indexer, left_indexer, -1)
+        elif is_categorical(target_as_index):
+            # get an indexer for unique categories then propogate to codes via take_1d
+            categories_indexer = self.get_indexer(target_as_index.categories)
+            indexer = take_1d(categories_indexer, target_as_index.codes, fill_value=-1)
         elif not is_object_dtype(target_as_index):
             # homogeneous scalar index: use IntervalTree
             target_as_index = self._maybe_convert_i8(target_as_index)

pandas/tests/frame/test_analytics.py

@@ -2272,6 +2272,15 @@ def test_round_nonunique_categorical(self):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_round_interval_category_columns(self):
+        # GH 30063
+        columns = pd.CategoricalIndex(pd.interval_range(0, 2))
+        df = DataFrame([[0.66, 1.1], [0.3, 0.25]], columns=columns)
+
+        result = df.round()
+        expected = DataFrame([[1.0, 1.0], [0.0, 0.0]], columns=columns)
+        tm.assert_frame_equal(result, expected)
+
     # ---------------------------------------------------------------------
     # Clip
 

pandas/tests/indexes/interval/test_indexing.py

@@ -3,7 +3,14 @@
 import numpy as np
 import pytest
 
-from pandas import Interval, IntervalIndex, Timedelta, date_range, timedelta_range
+from pandas import (
+    CategoricalIndex,
+    Interval,
+    IntervalIndex,
+    Timedelta,
+    date_range,
+    timedelta_range,
+)
 from pandas.core.indexes.base import InvalidIndexError
 import pandas.util.testing as tm
 
@@ -231,6 +238,25 @@ def test_get_indexer_length_one_interval(self, size, closed):
         expected = np.array([0] * size, dtype="intp")
         tm.assert_numpy_array_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "target",
+        [
+            IntervalIndex.from_tuples([(7, 8), (1, 2), (3, 4), (0, 1)]),
+            IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4), np.nan]),
+            IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)], closed="both"),
+            [-1, 0, 0.5, 1, 2, 2.5, np.nan],
+            ["foo", "foo", "bar", "baz"],
+        ],
+    )
+    def test_get_indexer_categorical(self, target, ordered_fixture):
+        # GH 30063: categorical and non-categorical results should be consistent
+        index = IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)])
+        categorical_target = CategoricalIndex(target, ordered=ordered_fixture)
+
+        result = index.get_indexer(categorical_target)
+        expected = index.get_indexer(target)
+        tm.assert_numpy_array_equal(result, expected)
+
     @pytest.mark.parametrize(
         "tuples, closed",
         [