ENH: IntervalArray.min/max

doc/source/whatsnew/v1.4.0.rst

@@ -218,6 +218,8 @@ Other enhancements
   ``USFederalHolidayCalendar``.  See also `Other API changes`_.
 - :meth:`.Rolling.var`, :meth:`.Expanding.var`, :meth:`.Rolling.std`, :meth:`.Expanding.std` now support `Numba <http://numba.pydata.org/>`_ execution with the ``engine`` keyword (:issue:`44461`)
 - :meth:`Series.info` has been added, for compatibility with :meth:`DataFrame.info` (:issue:`5167`)
+- Implemented :meth:`IntervalArray.min`, :meth:`IntervalArray.max`, as a result of which ``min`` and ``max`` now work for :class:`IntervalIndex`, :class:`Series` and :class:`DataFrame` with ``IntervalDtype`` (:issue:`44746`)
+-
 
 
 .. ---------------------------------------------------------------------------

pandas/core/arrays/base.py

@@ -1352,7 +1352,10 @@ def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
         ------
         TypeError : subclass does not define reductions
         """
-        raise TypeError(f"cannot perform {name} with type {self.dtype}")
+        meth = getattr(self, name, None)
+        if meth is None:
+            raise TypeError(f"cannot perform {name} with type {self.dtype}")
+        return meth(skipna=skipna, **kwargs)
 
     # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
     # Incompatible types in assignment (expression has type "None", base class

pandas/core/arrays/interval.py

@@ -790,6 +790,36 @@ def argsort(
             ascending=ascending, kind=kind, na_position=na_position, **kwargs
         )
 
+    def min(self, *, axis: int | None = None, skipna: bool = True):
+        if axis is not None and axis >= self.ndim:
+            raise ValueError(axis)
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            return self[~mask].min()
+
+        indexer = self.argsort()[0]
+        return self[indexer]
+
+    def max(self, *, axis: int | None = None, skipna: bool = True):
+        if axis is not None and axis >= self.ndim:
+            raise ValueError(axis)
+        if not len(self):
+            return self._na_value
+
+        mask = self.isna()
+        if mask.any():
+            if not skipna:
+                return self._na_value
+            return self[~mask].max()
+
+        indexer = self.argsort()[-1]
+        return self[indexer]
+
     def fillna(
         self: IntervalArrayT, value=None, method=None, limit=None
     ) -> IntervalArrayT:

pandas/tests/arrays/interval/test_interval.py

@@ -103,6 +103,8 @@ def test_shift_datetime(self):
 class TestSetitem:
     def test_set_na(self, left_right_dtypes):
         left, right = left_right_dtypes
+        left = left.copy(deep=True)
+        right = right.copy(deep=True)
         result = IntervalArray.from_arrays(left, right)
 
         if result.dtype.subtype.kind not in ["m", "M"]:
@@ -161,6 +163,52 @@ def test_repr():
     assert result == expected
 
 
+class TestReductions:
+    def test_min_max(self, left_right_dtypes, index_or_series_or_array):
+        # GH#44746
+        left, right = left_right_dtypes
+        left = left.copy(deep=True)
+        right = right.copy(deep=True)
+        arr = IntervalArray.from_arrays(left, right)
+
+        # The expected results below are only valid if monotonic
+        assert left.is_monotonic_increasing
+        assert Index(arr).is_monotonic_increasing
+
+        MIN = arr[0]
+        MAX = arr[-1]
+
+        indexer = np.arange(len(arr))
+        np.random.shuffle(indexer)
+        arr = arr.take(indexer)
+
+        arr_na = arr.insert(2, np.nan)
+
+        arr = index_or_series_or_array(arr)
+        arr_na = index_or_series_or_array(arr_na)
+
+        for skipna in [True, False]:
+            res = arr.min(skipna=skipna)
+            assert res == MIN
+            assert type(res) == type(MIN)
+
+            res = arr.max(skipna=skipna)
+            assert res == MAX
+            assert type(res) == type(MAX)
+
+        res = arr_na.min(skipna=False)
+        assert np.isnan(res)
+        res = arr_na.max(skipna=False)
+        assert np.isnan(res)
+
+        res = arr_na.min(skipna=True)
+        assert res == MIN
+        assert type(res) == type(MIN)
+        res = arr_na.max(skipna=True)
+        assert res == MAX
+        assert type(res) == type(MAX)
+
+
 # ----------------------------------------------------------------------------
 # Arrow interaction
 

pandas/tests/extension/test_interval.py

@@ -18,7 +18,10 @@
 
 from pandas.core.dtypes.dtypes import IntervalDtype
 
-from pandas import Interval
+from pandas import (
+    Interval,
+    Series,
+)
 from pandas.core.arrays import IntervalArray
 from pandas.tests.extension import base
 
@@ -102,17 +105,31 @@ def test_view(self, data):
 
 
 class TestReduce(base.BaseNoReduceTests):
-    pass
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
+        op_name = all_numeric_reductions
+        ser = Series(data)
+
+        if op_name in ["min", "max"]:
+            # IntervalArray *does* implement these
+            assert getattr(ser, op_name)(skipna=skipna) in data
+            assert getattr(data, op_name)(skipna=skipna) in data
+            return
+
+        super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
 
 
 class TestMethods(BaseInterval, base.BaseMethodsTests):
     @pytest.mark.skip(reason="addition is not defined for intervals")
     def test_combine_add(self, data_repeated):
         pass
 
-    @pytest.mark.skip(reason="Not Applicable")
+    @pytest.mark.xfail(
+        reason="Raises with incorrect message bc it disallows *all* listlikes "
+        "instead of just wrong-length listlikes"
+    )
     def test_fillna_length_mismatch(self, data_missing):
-        pass
+        super().test_fillna_length_mismatch(data_missing)
 
 
 class TestMissing(BaseInterval, base.BaseMissingTests):

pandas/tests/series/test_ufunc.py

@@ -277,14 +277,6 @@ def test_reduce(values, box, request):
         if values.dtype.kind in ["i", "f"]:
             # ATM Index casts to object, so we get python ints/floats
             same_type = False
-        elif isinstance(values, pd.IntervalIndex):
-            mark = pytest.mark.xfail(reason="IntervalArray.min/max not implemented")
-            request.node.add_marker(mark)
-
-    elif box is pd.Series or box is pd.DataFrame:
-        if isinstance(values, pd.IntervalIndex):
-            mark = pytest.mark.xfail(reason="IntervalArray.min/max not implemented")
-            request.node.add_marker(mark)
 
     if values.dtype == "i8" and box is pd.array:
         # FIXME: pd.array casts to Int64