BUG: fix combine_first converting timestamp to int

doc/source/whatsnew/v1.2.0.rst

@@ -301,6 +301,7 @@ Categorical
 
 Datetimelike
 ^^^^^^^^^^^^
+- Bug in :meth:`DataFrame.combine_first` that would convert datetime-like column on other :class:`DataFrame` to integer when the column is not present in original :class:`DataFrame` (:issue:`28481`)
 - Bug in :attr:`DatetimeArray.date` where a ``ValueError`` would be raised with a read-only backing array (:issue:`33530`)
 - Bug in ``NaT`` comparisons failing to raise ``TypeError`` on invalid inequality comparisons (:issue:`35046`)
 - Bug in :class:`DateOffset` where attributes reconstructed from pickle files differ from original objects when input values exceed normal ranges (e.g months=12) (:issue:`34511`)

pandas/core/frame.py

@@ -6169,7 +6169,7 @@ def combine(
                     otherSeries = otherSeries.astype(new_dtype)
 
             arr = func(series, otherSeries)
-            arr = maybe_downcast_to_dtype(arr, this_dtype)
+            arr = maybe_downcast_to_dtype(arr, new_dtype)
 
             result[col] = arr
 

pandas/tests/frame/methods/test_combine_first.py

@@ -103,6 +103,7 @@ def test_combine_first_mixed_bug(self):
         combined = frame1.combine_first(frame2)
         assert len(combined.columns) == 5
 
+    def test_combine_first_same_as_in_update(self):
         # gh 3016 (same as in update)
         df = DataFrame(
             [[1.0, 2.0, False, True], [4.0, 5.0, True, False]],
@@ -118,6 +119,7 @@ def test_combine_first_mixed_bug(self):
         df.loc[0, "A"] = 45
         tm.assert_frame_equal(result, df)
 
+    def test_combine_first_doc_example(self):
         # doc example
         df1 = DataFrame(
             {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
@@ -134,16 +136,23 @@ def test_combine_first_mixed_bug(self):
         expected = DataFrame({"A": [1, 2, 3, 5, 3, 7.0], "B": [np.nan, 2, 3, 4, 6, 8]})
         tm.assert_frame_equal(result, expected)
 
+    def test_combine_first_return_obj_type_with_bools(self):
         # GH3552, return object dtype with bools
         df1 = DataFrame(
             [[np.nan, 3.0, True], [-4.6, np.nan, True], [np.nan, 7.0, False]]
         )
         df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2])
 
-        result = df1.combine_first(df2)[2]
-        expected = Series([True, True, False], name=2)
-        tm.assert_series_equal(result, expected)
+        expected1 = pd.Series([True, True, False], name=2, dtype=object)
+        expected2 = pd.Series([True, True, False], name=2, dtype=object)
 
+        result1 = df1.combine_first(df2)[2]
+        result2 = df2.combine_first(df1)[2]
+
+        tm.assert_series_equal(result1, expected1)
+        tm.assert_series_equal(result2, expected2)
+
+    def test_combine_first_convert_datatime_correctly(self):
         # GH 3593, converting datetime64[ns] incorrectly
         df0 = DataFrame(
             {"a": [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]}
@@ -339,9 +348,14 @@ def test_combine_first_int(self):
         df1 = pd.DataFrame({"a": [0, 1, 3, 5]}, dtype="int64")
         df2 = pd.DataFrame({"a": [1, 4]}, dtype="int64")
 
-        res = df1.combine_first(df2)
-        tm.assert_frame_equal(res, df1)
-        assert res["a"].dtype == "int64"
+        exp1 = pd.DataFrame({"a": [0, 1, 3, 5]}, dtype="float64")
+        exp2 = pd.DataFrame({"a": [1, 4, 3, 5]}, dtype="float64")
+
+        res1 = df1.combine_first(df2)
+        res2 = df2.combine_first(df1)
+
+        tm.assert_frame_equal(res1, exp1)
+        tm.assert_frame_equal(res2, exp2)
 
     @pytest.mark.parametrize("val", [1, 1.0])
     def test_combine_first_with_asymmetric_other(self, val):
@@ -353,3 +367,22 @@ def test_combine_first_with_asymmetric_other(self, val):
         exp = pd.DataFrame({"isBool": [True], "isNum": [val]})
 
         tm.assert_frame_equal(res, exp)
+
+
+@pytest.mark.parametrize(
+    "val1, val2",
+    [
+        (datetime(2020, 1, 1), datetime(2020, 1, 2)),
+        (pd.Period("2020-01-01", "D"), pd.Period("2020-01-02", "D")),
+        (pd.Timedelta("89 days"), pd.Timedelta("60 min")),
+    ],
+)
+def test_combine_first_timestamp_bug(val1, val2, nulls_fixture):
+
+    df1 = pd.DataFrame([[nulls_fixture, nulls_fixture]], columns=["a", "b"])
+    df2 = pd.DataFrame([[val1, val2]], columns=["b", "c"])
+
+    res = df1.combine_first(df2)
+    exp = pd.DataFrame([[nulls_fixture, val1, val2]], columns=["a", "b", "c"])
+
+    tm.assert_frame_equal(res, exp)