BUG: merging with a boolean/int categorical column

doc/source/whatsnew/v0.21.0.txt

@@ -1010,6 +1010,7 @@ Categorical
 - Bug in :func:`Series.isin` when called with a categorical (:issue:`16639`)
 - Bug in the categorical constructor with empty values and categories causing the ``.categories`` to be an empty ``Float64Index`` rather than an empty ``Index`` with object dtype (:issue:`17248`)
 - Bug in categorical operations with :ref:`Series.cat <categorical.cat>` not preserving the original Series' name (:issue:`17509`)
+- Bug in :func:`DataFrame.merge` failing for categorical columns with boolean/int data types (:issue:`17187`)
 
 PyPy
 ^^^^

pandas/core/internals.py

@@ -5494,7 +5494,7 @@ def get_reindexed_values(self, empty_dtype, upcasted_na):
                     # preserve these for validation in _concat_compat
                     return self.block.values
 
-            if self.block.is_bool:
+            if self.block.is_bool and not self.block.is_categorical:
                 # External code requested filling/upcasting, bool values must
                 # be upcasted to object to avoid being upcasted to numeric.
                 values = self.block.astype(np.object_).values

pandas/tests/reshape/test_merge.py

@@ -1546,6 +1546,49 @@ def test_dtype_on_categorical_dates(self):
         result_inner = pd.merge(df, df2, how='inner', on=['date'])
         assert_frame_equal(result_inner, expected_inner)
 
+    def test_merging_with_boolean_cateorical_column(self):
+        df1 = pd.DataFrame({'id': [1, 2, 3, 4],
+                            'cat': [False, True, True, False]})
+        df1['cat'] = df1['cat'].astype('category',
+                                       categories=[True, False], ordered=True)
+        df2 = pd.DataFrame({'id': [2, 4], 'num': [1, 9]})
+        result = df1.merge(df2)
+        expected = pd.DataFrame({'id': [2, 4], 'cat': [True, False],
+                                 'num': [1, 9]})
+        expected['cat'] = expected['cat'].astype('category',
+                                                 categories=[True, False],
+                                                 ordered=True)
+        assert_frame_equal(expected, result)
+
+    def test_merging_with_integer_cateorical_column(self):
+        df1 = pd.DataFrame({'id': [1, 2, 3, 4],
+                            'cat': [2, 1, 1, 2]})
+        df1['cat'] = df1['cat'].astype('category',
+                                       categories=[1, 2], ordered=True)
+        df2 = pd.DataFrame({'id': [2, 4], 'num': [1, 9]})
+        result = df1.merge(df2)
+        expected = pd.DataFrame({'id': [2, 4], 'cat': [1, 2],
+                                 'num': [1, 9]})
+        expected['cat'] = expected['cat'].astype('category',
+                                                 categories=[1, 2],
+                                                 ordered=True)
+        assert_frame_equal(expected, result)
+
+    def test_merging_with_string_cateorical_column(self):
+        df1 = pd.DataFrame({'id': [1, 2, 3, 4],
+                            'cat': ['False', 'True', 'True', 'False']})
+        df1['cat'] = df1['cat'].astype('category',
+                                       categories=['True', 'False'],
+                                       ordered=True)
+        df2 = pd.DataFrame({'id': [2, 4], 'num': [1, 9]})
+        result = df1.merge(df2)
+        expected = pd.DataFrame({'id': [2, 4], 'cat': ['True', 'False'],
+                                 'num': [1, 9]})
+        expected['cat'] = expected['cat'].astype('category',
+                                                 categories=['True', 'False'],
+                                                 ordered=True)
+        assert_frame_equal(expected, result)
+
 
 @pytest.fixture
 def left_df():