BUG: loc-indexing with a slice on a CategoricalIndex

doc/source/whatsnew/v1.0.0.rst

@@ -743,7 +743,7 @@ Indexing
 - Fix assignment of column via `.loc` with numpy non-ns datetime type (:issue:`27395`)
 - Bug in :meth:`Float64Index.astype` where ``np.inf`` was not handled properly when casting to an integer dtype (:issue:`28475`)
 - :meth:`Index.union` could fail when the left contained duplicates (:issue:`28257`)
-- Bug when indexing with ``.loc`` where the index was a :class:`CategoricalIndex` with integer and float categories, a ValueError was raised (:issue:`17569`)
+- Bug when indexing with ``.loc`` where the index was a :class:`CategoricalIndex` with non-string categories didn't work (:issue:`17569`, :issue:`30225`)
 - :meth:`Index.get_indexer_non_unique` could fail with `TypeError` in some cases, such as when searching for ints in a string index (:issue:`28257`)
 - Bug in :meth:`Float64Index.get_loc` incorrectly raising ``TypeError`` instead of ``KeyError`` (:issue:`29189`)
 

pandas/core/indexes/base.py

@@ -2982,7 +2982,9 @@ def is_int(v):
 
         is_null_slicer = start is None and stop is None
         is_index_slice = is_int(start) and is_int(stop)
-        is_positional = is_index_slice and not self.is_integer()
+        is_positional = is_index_slice and not (
+            self.is_integer() or self.is_categorical()
+        )
 
         if kind == "getitem":
             """

pandas/core/indexes/category.py

@@ -753,6 +753,13 @@ def is_dtype_equal(self, other):
 
     take_nd = take
 
+    @Appender(_index_shared_docs["_maybe_cast_slice_bound"])
+    def _maybe_cast_slice_bound(self, label, side, kind):
+        if kind == "loc":
+            return label
+
+        return super()._maybe_cast_slice_bound(label, side, kind)
+
     def map(self, mapper):
         """
         Map values using input correspondence (a dict, Series, or function).

pandas/tests/indexing/test_categorical.py

@@ -654,22 +654,13 @@ def test_reindexing(self):
             df.reindex(["a"], limit=2)
 
     def test_loc_slice(self):
-        # slicing
-        # not implemented ATM
         # GH9748
-
-        msg = (
-            "cannot do slice indexing on {klass} with these "
-            r"indexers \[1\] of {kind}".format(
-                klass=str(CategoricalIndex), kind=str(int)
-            )
-        )
-        with pytest.raises(TypeError, match=msg):
+        with pytest.raises(KeyError, match="1"):
             self.df.loc[1:5]
 
-        # result = df.loc[1:5]
-        # expected = df.iloc[[1,2,3,4]]
-        # tm.assert_frame_equal(result, expected)
+        result = self.df.loc["b":"c"]
+        expected = self.df.iloc[[2, 3, 4]]
+        tm.assert_frame_equal(result, expected)
 
     def test_loc_and_at_with_categorical_index(self):
         # GH 20629
@@ -794,6 +785,7 @@ def test_loc_with_non_string_categories(self, idx_values, ordered_fixture):
         # GH-17569
         cat_idx = CategoricalIndex(idx_values, ordered=ordered_fixture)
         df = DataFrame({"A": ["foo", "bar", "baz"]}, index=cat_idx)
+        sl = slice(idx_values[0], idx_values[1])
 
         # scalar selection
         result = df.loc[idx_values[0]]
@@ -805,6 +797,11 @@ def test_loc_with_non_string_categories(self, idx_values, ordered_fixture):
         expected = DataFrame(["foo", "bar"], index=cat_idx[:2], columns=["A"])
         tm.assert_frame_equal(result, expected)
 
+        # slice selection
+        result = df.loc[sl]
+        expected = DataFrame(["foo", "bar"], index=cat_idx[:2], columns=["A"])
+        tm.assert_frame_equal(result, expected)
+
         # scalar assignment
         result = df.copy()
         result.loc[idx_values[0]] = "qux"
@@ -816,3 +813,9 @@ def test_loc_with_non_string_categories(self, idx_values, ordered_fixture):
         result.loc[idx_values[:2], "A"] = ["qux", "qux2"]
         expected = DataFrame({"A": ["qux", "qux2", "baz"]}, index=cat_idx)
         tm.assert_frame_equal(result, expected)
+
+        # slice assignment
+        result = df.copy()
+        result.loc[sl, "A"] = ["qux", "qux2"]
+        expected = DataFrame({"A": ["qux", "qux2", "baz"]}, index=cat_idx)
+        tm.assert_frame_equal(result, expected)