BUG: TypeError when loc-indexing on a CategoricalIndex with integer categories

Author: topper-123

doc/source/whatsnew/v1.0.0.rst

@@ -570,6 +570,7 @@ 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 or float categories, a ValueError was raised (:issue:`17569`)
 - :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/category.py

@@ -696,8 +696,11 @@ def get_indexer_non_unique(self, target):
 
     @Appender(_index_shared_docs["_convert_scalar_indexer"])
     def _convert_scalar_indexer(self, key, kind=None):
-        if self.categories._defer_to_indexing:
-            return self.categories._convert_scalar_indexer(key, kind=kind)
+        if kind == "loc" or self.categories._defer_to_indexing:
+            try:
+                return self.categories._convert_scalar_indexer(key, kind=kind)
+            except TypeError:
+                self._invalid_indexer("label", key=key)
 
         return super()._convert_scalar_indexer(key, kind=kind)
 

pandas/tests/indexing/test_categorical.py

@@ -754,3 +754,29 @@ def test_map_with_dict_or_series(self):
         output = cur_index.map(mapper)
         # Order of categories in output can be different
         tm.assert_index_equal(expected, output)
+
+    @pytest.mark.parametrize(
+        "idx_values", [[1, 2, 3], [-1, -2, -3], [1.5, 2.5, 3.5], [-1.5, -2.5, -3.5],]
+    )
+    def test_loc_with_non_string_categories(self, idx_values, ordered_fixture):
+        # GH-17569
+        cat_idx = CategoricalIndex(idx_values, ordered=ordered_fixture)
+        cat = DataFrame({"A": ["foo", "bar", "baz"]}, index=cat_idx)
+        # scalar
+        result = cat.loc[idx_values[0]]
+        expected = Series(["foo"], index=["A"], name=idx_values[0])
+        tm.assert_series_equal(result, expected)
+        # list
+        result = cat.loc[idx_values[:2]]
+        expected = DataFrame(["foo", "bar"], index=cat_idx[:2], columns=["A"])
+        tm.assert_frame_equal(result, expected)
+        # scalar assignment
+        result = cat.copy()
+        result.loc[idx_values[0]] = "qux"
+        expected = DataFrame({"A": ["qux", "bar", "baz"]}, index=cat_idx)
+        tm.assert_frame_equal(result, expected)
+        # list assignment
+        result = cat.copy()
+        result.loc[idx_values[:2], "A"] = ["qux", "qux2"]
+        expected = DataFrame({"A": ["qux", "qux2", "baz"]}, index=cat_idx)
+        tm.assert_frame_equal(result, expected)

pandas/tests/indexing/test_floats.py

@@ -100,7 +100,12 @@ def test_scalar_non_numeric(self):
                         idxr(s)[3.0]
 
                 # label based can be a TypeError or KeyError
-                if s.index.inferred_type in ["string", "unicode", "mixed"]:
+                if s.index.inferred_type in {
+                    "categorical",
+                    "string",
+                    "unicode",
+                    "mixed",
+                }:
                     error = KeyError
                     msg = r"^3$"
                 else: