REF: require scalar in IntervalIndex.get_loc, get_value (#31169)

Author: jbrockmendel

pandas/core/indexes/interval.py

@@ -1,7 +1,7 @@
 """ define the IntervalIndex """
 from operator import le, lt
 import textwrap
-from typing import Any, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Optional, Tuple, Union
 
 import numpy as np
 
@@ -34,7 +34,6 @@
     is_object_dtype,
     is_scalar,
 )
-from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import accessor
@@ -59,6 +58,10 @@
 from pandas.tseries.frequencies import to_offset
 from pandas.tseries.offsets import DateOffset
 
+if TYPE_CHECKING:
+    from pandas import Series
+
+
 _VALID_CLOSED = {"left", "right", "both", "neither"}
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 
@@ -681,7 +684,7 @@ def _searchsorted_monotonic(self, label, side, exclude_label=False):
         return sub_idx._searchsorted_monotonic(label, side)
 
     def get_loc(
-        self, key: Any, method: Optional[str] = None, tolerance=None
+        self, key, method: Optional[str] = None, tolerance=None
     ) -> Union[int, slice, np.ndarray]:
         """
         Get integer location, slice or boolean mask for requested label.
@@ -723,10 +726,8 @@ def get_loc(
         """
         self._check_method(method)
 
-        # list-like are invalid labels for II but in some cases may work, e.g
-        # single element array of comparable type, so guard against them early
-        if is_list_like(key):
-            raise KeyError(key)
+        if not is_scalar(key):
+            raise InvalidIndexError(key)
 
         if isinstance(key, Interval):
             if self.closed != key.closed:
@@ -819,6 +820,9 @@ def get_indexer(
                     loc = self.get_loc(key)
                 except KeyError:
                     loc = -1
+                except InvalidIndexError:
+                    # i.e. non-scalar key
+                    raise TypeError(key)
                 indexer.append(loc)
 
         return ensure_platform_int(indexer)
@@ -882,25 +886,15 @@ def get_indexer_for(self, target: AnyArrayLike, **kwargs) -> np.ndarray:
         return self.get_indexer(target, **kwargs)
 
     @Appender(_index_shared_docs["get_value"] % _index_doc_kwargs)
-    def get_value(self, series: ABCSeries, key: Any) -> Any:
-
-        if com.is_bool_indexer(key):
-            loc = key
-        elif is_list_like(key):
-            if self.is_overlapping:
-                loc, missing = self.get_indexer_non_unique(key)
-                if len(missing):
-                    raise KeyError
-            else:
-                loc = self.get_indexer(key)
-        elif isinstance(key, slice):
-            if not (key.step is None or key.step == 1):
-                raise ValueError("cannot support not-default step in a slice")
-            loc = self._convert_slice_indexer(key, kind="getitem")
-        else:
-            loc = self.get_loc(key)
+    def get_value(self, series: "Series", key):
+        loc = self.get_loc(key)
         return series.iloc[loc]
 
+    def _convert_slice_indexer(self, key: slice, kind=None):
+        if not (key.step is None or key.step == 1):
+            raise ValueError("cannot support not-default step in a slice")
+        return super()._convert_slice_indexer(key, kind)
+
     @Appender(_index_shared_docs["where"])
     def where(self, cond, other=None):
         if other is None:

pandas/core/series.py

@@ -75,6 +75,7 @@
 from pandas.core.indexes.api import (
     Float64Index,
     Index,
+    IntervalIndex,
     InvalidIndexError,
     MultiIndex,
     ensure_index,
@@ -905,6 +906,9 @@ def _get_with(self, key):
         if key_type == "integer":
             if self.index.is_integer() or self.index.is_floating():
                 return self.loc[key]
+            elif isinstance(self.index, IntervalIndex):
+                indexer = self.index.get_indexer_for(key)
+                return self.iloc[indexer]
             else:
                 return self._get_values(key)
         elif key_type == "boolean":