Fix TypeError when merging categorical dates

doc/source/whatsnew/v0.21.0.txt

@@ -225,6 +225,7 @@ Sparse
 Reshaping
 ^^^^^^^^^
 - Joining/Merging with a non unique ``PeriodIndex`` raised a TypeError (:issue:`16871`)
+- Merging with categorical date columns raised a TypeError (:issue:`16900`)
 - Bug when using :func:`isin` on a large object series and large comparison array (:issue:`16012`)
 - Fixes regression from 0.20, :func:`Series.aggregate` and :func:`DataFrame.aggregate` allow dictionaries as return values again (:issue:`16741`)
 

pandas/core/reshape/merge.py

@@ -877,7 +877,7 @@ def _get_merge_keys(self):
         return left_keys, right_keys, join_names
 
     def _maybe_coerce_merge_keys(self):
-        # we have valid mergee's but we may have to further
+        # we have valid mergees but we may have to further
         # coerce these if they are originally incompatible types
         #
         # for example if these are categorical, but are not dtype_equal
@@ -889,12 +889,16 @@ def _maybe_coerce_merge_keys(self):
             if (len(lk) and not len(rk)) or (not len(lk) and len(rk)):
                 continue
 
+            lk_is_cat = is_categorical_dtype(lk)
+            rk_is_cat = is_categorical_dtype(rk)
+
             # if either left or right is a categorical
             # then the must match exactly in categories & ordered
-            if is_categorical_dtype(lk) and is_categorical_dtype(rk):
+            if lk_is_cat and rk_is_cat:
                 if lk.is_dtype_equal(rk):
                     continue
-            elif is_categorical_dtype(lk) or is_categorical_dtype(rk):
+
+            elif lk_is_cat or rk_is_cat:
                 pass
 
             elif is_dtype_equal(lk.dtype, rk.dtype):
@@ -904,7 +908,7 @@ def _maybe_coerce_merge_keys(self):
             # kinds to proceed, eg. int64 and int8
             # further if we are object, but we infer to
             # the same, then proceed
-            if (is_numeric_dtype(lk) and is_numeric_dtype(rk)):
+            if is_numeric_dtype(lk) and is_numeric_dtype(rk):
                 if lk.dtype.kind == rk.dtype.kind:
                     continue
 
@@ -913,13 +917,20 @@ def _maybe_coerce_merge_keys(self):
                     continue
 
             # Houston, we have a problem!
-            # let's coerce to object
+            # let's coerce to object if the dtypes aren't
+            # categorical, otherwise coerce to the category
+            # dtype. If we coerced categories to object,
+            # then we would lose type information on some
+            # columns, and end up trying to merge
+            # incompatible dtypes. See GH 16900.
             if name in self.left.columns:
+                typ = lk.categories.dtype if lk_is_cat else object
                 self.left = self.left.assign(
-                    **{name: self.left[name].astype(object)})
+                    **{name: self.left[name].astype(typ)})
             if name in self.right.columns:
+                typ = rk.categories.dtype if rk_is_cat else object
                 self.right = self.right.assign(
-                    **{name: self.right[name].astype(object)})
+                    **{name: self.right[name].astype(typ)})
 
     def _validate_specification(self):
         # Hm, any way to make this logic less complicated??

pandas/tests/reshape/test_merge.py

@@ -1,7 +1,7 @@
 # pylint: disable=E1103
 
 import pytest
-from datetime import datetime
+from datetime import datetime, date
 from numpy.random import randn
 from numpy import nan
 import numpy as np
@@ -1515,6 +1515,40 @@ def test_self_join_multiple_categories(self):
 
         assert_frame_equal(result, df)
 
+    def test_dtype_on_categorical_dates(self):
+        # GH 16900
+        # dates should not be coerced to ints
+
+        df = pd.DataFrame(
+            [[date(2001, 1, 1), 1.1],
+             [date(2001, 1, 2), 1.3]],
+            columns=['date', 'num2']
+        )
+        df['date'] = df['date'].astype('category')
+
+        df2 = pd.DataFrame(
+            [[date(2001, 1, 1), 1.3],
+             [date(2001, 1, 3), 1.4]],
+            columns=['date', 'num4']
+        )
+        df2['date'] = df2['date'].astype('category')
+
+        expected_outer = pd.DataFrame([
+            [pd.Timestamp('2001-01-01'), 1.1, 1.3],
+            [pd.Timestamp('2001-01-02'), 1.3, np.nan],
+            [pd.Timestamp('2001-01-03'), np.nan, 1.4]],
+            columns=['date', 'num2', 'num4']
+        )
+        result_outer = pd.merge(df, df2, how='outer', on=['date'])
+        assert_frame_equal(result_outer, expected_outer)
+
+        expected_inner = pd.DataFrame(
+            [[pd.Timestamp('2001-01-01'), 1.1, 1.3]],
+            columns=['date', 'num2', 'num4']
+        )
+        result_inner = pd.merge(df, df2, how='inner', on=['date'])
+        assert_frame_equal(result_inner, expected_inner)
+
 
 @pytest.fixture
 def left_df():