ENH: try to preserve the dtype on combine_first for the case where the two DataFrame objects have the same columns (#39051)

Author: danielhrisca

doc/source/whatsnew/v1.3.0.rst

@@ -63,6 +63,36 @@ Notable bug fixes
 These are bug fixes that might have notable behavior changes.
 
 
+Preserve dtypes in  :meth:`~pandas.DataFrame.combine_first`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:meth:`~pandas.DataFrame.combine_first` will now preserve dtypes (:issue:`7509`)
+
+.. ipython:: python
+
+   df1 = pd.DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]}, index=[0, 1, 2])
+   df1
+   df2 = pd.DataFrame({"B": [4, 5, 6], "C": [1, 2, 3]}, index=[2, 3, 4])
+   df2
+   combined = df1.combine_first(df2)
+
+*pandas 1.2.x*
+
+.. code-block:: ipython
+
+   In [1]: combined.dtypes
+   Out[2]:
+   A    float64
+   B    float64
+   C    float64
+   dtype: object
+
+*pandas 1.3.0*
+
+.. ipython:: python
+
+   combined.dtypes
+
 
 .. _whatsnew_130.api_breaking.deps:
 

pandas/core/frame.py

@@ -6513,7 +6513,18 @@ def combiner(x, y):
 
             return expressions.where(mask, y_values, x_values)
 
-        return self.combine(other, combiner, overwrite=False)
+        combined = self.combine(other, combiner, overwrite=False)
+
+        dtypes = {
+            col: find_common_type([self.dtypes[col], other.dtypes[col]])
+            for col in self.columns.intersection(other.columns)
+            if not is_dtype_equal(combined.dtypes[col], self.dtypes[col])
+        }
+
+        if dtypes:
+            combined = combined.astype(dtypes)
+
+        return combined
 
     def update(
         self,

pandas/tests/frame/methods/test_combine_first.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.cast import find_common_type, is_dtype_equal
+
 import pandas as pd
 from pandas import DataFrame, Index, MultiIndex, Series
 import pandas._testing as tm
@@ -18,9 +20,7 @@ def test_combine_first_mixed(self):
         b = Series(range(2), index=range(5, 7))
         g = DataFrame({"A": a, "B": b})
 
-        exp = DataFrame(
-            {"A": list("abab"), "B": [0.0, 1.0, 0.0, 1.0]}, index=[0, 1, 5, 6]
-        )
+        exp = DataFrame({"A": list("abab"), "B": [0, 1, 0, 1]}, index=[0, 1, 5, 6])
         combined = f.combine_first(g)
         tm.assert_frame_equal(combined, exp)
 
@@ -144,7 +144,7 @@ def test_combine_first_return_obj_type_with_bools(self):
         )
         df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2])
 
-        expected = Series([True, True, False], name=2, dtype=object)
+        expected = Series([True, True, False], name=2, dtype=bool)
 
         result_12 = df1.combine_first(df2)[2]
         tm.assert_series_equal(result_12, expected)
@@ -157,22 +157,22 @@ def test_combine_first_return_obj_type_with_bools(self):
         (
             (
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
-                [None, None, None],
+                [pd.NaT, pd.NaT, pd.NaT],
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
             ),
             (
-                [None, None, None],
+                [pd.NaT, pd.NaT, pd.NaT],
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
             ),
             (
-                [datetime(2000, 1, 2), None, None],
+                [datetime(2000, 1, 2), pd.NaT, pd.NaT],
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
                 [datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3)],
             ),
             (
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
-                [datetime(2000, 1, 2), None, None],
+                [datetime(2000, 1, 2), pd.NaT, pd.NaT],
                 [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
             ),
         ),
@@ -196,13 +196,13 @@ def test_combine_first_align_nan(self):
 
         res = dfa.combine_first(dfb)
         exp = DataFrame(
-            {"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2.0, 5.0]},
+            {"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2, 5]},
             columns=["a", "b"],
         )
         tm.assert_frame_equal(res, exp)
         assert res["a"].dtype == "datetime64[ns]"
         # ToDo: this must be int64
-        assert res["b"].dtype == "float64"
+        assert res["b"].dtype == "int64"
 
         res = dfa.iloc[:0].combine_first(dfb)
         exp = DataFrame({"a": [np.nan, np.nan], "b": [4, 5]}, columns=["a", "b"])
@@ -219,14 +219,12 @@ def test_combine_first_timezone(self):
             columns=["UTCdatetime", "abc"],
             data=data1,
             index=pd.date_range("20140627", periods=1),
-            dtype="object",
         )
         data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC")
         df2 = DataFrame(
             columns=["UTCdatetime", "xyz"],
             data=data2,
             index=pd.date_range("20140628", periods=1),
-            dtype="object",
         )
         res = df2[["UTCdatetime"]].combine_first(df1)
         exp = DataFrame(
@@ -239,13 +237,10 @@ def test_combine_first_timezone(self):
             },
             columns=["UTCdatetime", "abc"],
             index=pd.date_range("20140627", periods=2, freq="D"),
-            dtype="object",
         )
         assert res["UTCdatetime"].dtype == "datetime64[ns, UTC]"
         assert res["abc"].dtype == "datetime64[ns, UTC]"
-        # Need to cast all to "obejct" because combine_first does not retain dtypes:
-        # GH Issue 7509
-        res = res.astype("object")
+
         tm.assert_frame_equal(res, exp)
 
         # see gh-10567
@@ -360,12 +355,11 @@ def test_combine_first_int(self):
         df2 = DataFrame({"a": [1, 4]}, dtype="int64")
 
         result_12 = df1.combine_first(df2)
-        expected_12 = DataFrame({"a": [0, 1, 3, 5]}, dtype="float64")
+        expected_12 = DataFrame({"a": [0, 1, 3, 5]})
         tm.assert_frame_equal(result_12, expected_12)
 
         result_21 = df2.combine_first(df1)
-        expected_21 = DataFrame({"a": [1, 4, 3, 5]}, dtype="float64")
-
+        expected_21 = DataFrame({"a": [1, 4, 3, 5]})
         tm.assert_frame_equal(result_21, expected_21)
 
     @pytest.mark.parametrize("val", [1, 1.0])
@@ -404,11 +398,38 @@ def test_combine_first_string_dtype_only_na(self):
 def test_combine_first_timestamp_bug(scalar1, scalar2, nulls_fixture):
     # GH28481
     na_value = nulls_fixture
+
     frame = DataFrame([[na_value, na_value]], columns=["a", "b"])
     other = DataFrame([[scalar1, scalar2]], columns=["b", "c"])
 
+    common_dtype = find_common_type([frame.dtypes["b"], other.dtypes["b"]])
+
+    if is_dtype_equal(common_dtype, "object") or frame.dtypes["b"] == other.dtypes["b"]:
+        val = scalar1
+    else:
+        val = na_value
+
+    result = frame.combine_first(other)
+
+    expected = DataFrame([[na_value, val, scalar2]], columns=["a", "b", "c"])
+
+    expected["b"] = expected["b"].astype(common_dtype)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_combine_first_timestamp_bug_NaT():
+    # GH28481
+    frame = DataFrame([[pd.NaT, pd.NaT]], columns=["a", "b"])
+    other = DataFrame(
+        [[datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["b", "c"]
+    )
+
     result = frame.combine_first(other)
-    expected = DataFrame([[na_value, scalar1, scalar2]], columns=["a", "b", "c"])
+    expected = DataFrame(
+        [[pd.NaT, datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["a", "b", "c"]
+    )
+
     tm.assert_frame_equal(result, expected)
 
 
@@ -439,3 +460,25 @@ def test_combine_first_with_nan_multiindex():
         index=mi_expected,
     )
     tm.assert_frame_equal(res, expected)
+
+
+def test_combine_preserve_dtypes():
+    # GH7509
+    a_column = Series(["a", "b"], index=range(2))
+    b_column = Series(range(2), index=range(2))
+    df1 = DataFrame({"A": a_column, "B": b_column})
+
+    c_column = Series(["a", "b"], index=range(5, 7))
+    b_column = Series(range(-1, 1), index=range(5, 7))
+    df2 = DataFrame({"B": b_column, "C": c_column})
+
+    expected = DataFrame(
+        {
+            "A": ["a", "b", np.nan, np.nan],
+            "B": [0, 1, -1, 0],
+            "C": [np.nan, np.nan, "a", "b"],
+        },
+        index=[0, 1, 5, 6],
+    )
+    combined = df1.combine_first(df2)
+    tm.assert_frame_equal(combined, expected)