REF: de-duplicate IntervalArray._validate_foo (#36483)

Author: jbrockmendel

pandas/core/arrays/interval.py

@@ -20,7 +20,6 @@
     is_datetime64_any_dtype,
     is_float_dtype,
     is_integer_dtype,
-    is_interval,
     is_interval_dtype,
     is_list_like,
     is_object_dtype,
@@ -813,7 +812,9 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
 
         fill_left = fill_right = fill_value
         if allow_fill:
-            fill_left, fill_right = self._validate_fill_value(fill_value)
+            if (np.asarray(indices) == -1).any():
+                # We have excel tests that pass fill_value=True, xref GH#36466
+                fill_left, fill_right = self._validate_fill_value(fill_value)
 
         left_take = take(
             self.left, indices, allow_fill=allow_fill, fill_value=fill_left
@@ -824,20 +825,33 @@ def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
 
         return self._shallow_copy(left_take, right_take)
 
-    def _validate_fill_value(self, value):
-        if is_interval(value):
-            self._check_closed_matches(value, name="fill_value")
-            fill_left, fill_right = value.left, value.right
-        elif not is_scalar(value) and notna(value):
-            msg = (
-                "'IntervalArray.fillna' only supports filling with a "
-                "'scalar pandas.Interval or NA'. "
-                f"Got a '{type(value).__name__}' instead."
-            )
-            raise ValueError(msg)
+    def _validate_listlike(self, value):
+        # list-like of intervals
+        try:
+            array = IntervalArray(value)
+            # TODO: self._check_closed_matches(array, name="value")
+            value_left, value_right = array.left, array.right
+        except TypeError as err:
+            # wrong type: not interval or NA
+            msg = f"'value' should be an interval type, got {type(value)} instead."
+            raise TypeError(msg) from err
+        return value_left, value_right
+
+    def _validate_scalar(self, value):
+        if isinstance(value, Interval):
+            self._check_closed_matches(value, name="value")
+            left, right = value.left, value.right
+        elif is_valid_nat_for_dtype(value, self.left.dtype):
+            # GH#18295
+            left = right = value
         else:
-            fill_left = fill_right = self.left._na_value
-        return fill_left, fill_right
+            raise ValueError(
+                "can only insert Interval objects and NA into an IntervalArray"
+            )
+        return left, right
+
+    def _validate_fill_value(self, value):
+        return self._validate_scalar(value)
 
     def _validate_fillna_value(self, value):
         if not isinstance(value, Interval):
@@ -851,26 +865,12 @@ def _validate_fillna_value(self, value):
         return value.left, value.right
 
     def _validate_insert_value(self, value):
-        if isinstance(value, Interval):
-            if value.closed != self.closed:
-                raise ValueError(
-                    "inserted item must be closed on the same side as the index"
-                )
-            left_insert = value.left
-            right_insert = value.right
-        elif is_valid_nat_for_dtype(value, self.left.dtype):
-            # GH#18295
-            left_insert = right_insert = value
-        else:
-            raise ValueError(
-                "can only insert Interval objects and NA into an IntervalIndex"
-            )
-        return left_insert, right_insert
+        return self._validate_scalar(value)
 
     def _validate_setitem_value(self, value):
         needs_float_conversion = False
 
-        if is_scalar(value) and isna(value):
+        if is_valid_nat_for_dtype(value, self.left.dtype):
             # na value: need special casing to set directly on numpy arrays
             if is_integer_dtype(self.dtype.subtype):
                 # can't set NaN on a numpy integer array

pandas/tests/arrays/interval/test_interval.py

@@ -105,6 +105,10 @@ def test_set_na(self, left_right_dtypes):
         left, right = left_right_dtypes
         result = IntervalArray.from_arrays(left, right)
 
+        if result.dtype.subtype.kind not in ["m", "M"]:
+            msg = "'value' should be an interval type, got <.*NaTType'> instead."
+            with pytest.raises(TypeError, match=msg):
+                result[0] = pd.NaT
         if result.dtype.subtype.kind in ["i", "u"]:
             msg = "Cannot set float NaN to integer-backed IntervalArray"
             with pytest.raises(ValueError, match=msg):

pandas/tests/indexes/interval/test_interval.py

@@ -191,13 +191,14 @@ def test_insert(self, data):
         tm.assert_index_equal(result, expected)
 
         # invalid type
-        msg = "can only insert Interval objects and NA into an IntervalIndex"
+        msg = "can only insert Interval objects and NA into an IntervalArray"
         with pytest.raises(ValueError, match=msg):
             data.insert(1, "foo")
 
         # invalid closed
-        msg = "inserted item must be closed on the same side as the index"
+        msg = "'value.closed' is 'left', expected 'right'."
         for closed in {"left", "right", "both", "neither"} - {item.closed}:
+            msg = f"'value.closed' is '{closed}', expected '{item.closed}'."
             with pytest.raises(ValueError, match=msg):
                 bad_item = Interval(item.left, item.right, closed=closed)
                 data.insert(1, bad_item)
@@ -211,7 +212,7 @@ def test_insert(self, data):
 
         if data.left.dtype.kind not in ["m", "M"]:
             # trying to insert pd.NaT into a numeric-dtyped Index should cast/raise
-            msg = "can only insert Interval objects and NA into an IntervalIndex"
+            msg = "can only insert Interval objects and NA into an IntervalArray"
             with pytest.raises(ValueError, match=msg):
                 result = data.insert(1, pd.NaT)
         else: