BUG GH#40498 Fillna other missing values not modified

asv_bench/benchmarks/frame_methods.py

@@ -374,13 +374,13 @@ class Fillna:
 
     params = (
         [True, False],
-        ["pad", "bfill"],
+        [None, "pad", "bfill"],
         [
-            "float64",
-            "float32",
             "object",
-            "Int64",
-            "Float64",
+            "int32",
+            "int64",
+            "float32",
+            "float64",
             "datetime64[ns]",
             "datetime64[ns, tz]",
             "timedelta64[ns]",
@@ -400,15 +400,18 @@ def setup(self, inplace, method, dtype):
             }
             self.df = DataFrame({f"col_{i}": data[dtype] for i in range(M)})
             self.df[::2] = None
+            self.value = (
+                dict(zip(self.df.columns, data[dtype][:M])) if not method else None
+            )
         else:
-            values = np.random.randn(N, M)
-            values[::2] = np.nan
-            if dtype == "Int64":
-                values = values.round()
-            self.df = DataFrame(values, dtype=dtype)
+            data = np.random.randn(N, M)
+            data = data.astype(dtype)
+            self.df = DataFrame({f"col_{i}": data[i] for i in range(M)})
+            self.df[::2] = None
+            self.value = dict(zip(self.df.columns, data[M])) if not method else None
 
     def time_frame_fillna(self, inplace, method, dtype):
-        self.df.fillna(inplace=inplace, method=method)
+        self.df.fillna(value=self.value, inplace=inplace, method=method)
 
 
 class Dropna:

asv_bench/benchmarks/series_methods.py

@@ -6,6 +6,7 @@
     NaT,
     Series,
     date_range,
+    timedelta_range,
 )
 
 from .pandas_vb_common import tm
@@ -42,6 +43,48 @@ def time_nsmallest(self, keep):
         self.s.nsmallest(3, keep=keep)
 
 
+class Fillna:
+
+    params = (
+        [True, False],
+        [None, "pad", "bfill"],
+        [
+            "object",
+            "int32",
+            "int64",
+            "float32",
+            "float64",
+            "datetime64[ns]",
+            "datetime64[ns, tz]",
+            "timedelta64[ns]",
+        ],
+    )
+    param_names = ["inplace", "method", "dtype"]
+
+    def setup(self, inplace, method, dtype):
+        N = 10000
+        if dtype in ("datetime64[ns]", "datetime64[ns, tz]", "timedelta64[ns]"):
+            data = {
+                "datetime64[ns]": date_range("2011-01-01", freq="H", periods=N),
+                "datetime64[ns, tz]": date_range(
+                    "2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
+                ),
+                "timedelta64[ns]": timedelta_range(start="1 day", periods=N, freq="1D"),
+            }
+            self.ser = Series(data[dtype])
+            self.ser[::2] = None
+            self.value = dict(zip(self.ser.index, data[dtype])) if not method else None
+        else:
+            data = np.random.randn(N)
+            data = data.astype(dtype)
+            self.ser = Series(data, dtype=dtype)
+            self.ser[::2] = None
+            self.value = dict(zip(self.ser.index, data)) if not method else None
+
+    def time_series_fillna(self, inplace, method, dtype):
+        self.ser.fillna(value=self.value, inplace=inplace, method=method)
+
+
 class Dropna:
 
     params = ["int", "datetime"]

pandas/core/generic.py

@@ -6308,11 +6308,23 @@ def fillna(
         else:
             if self.ndim == 1:
                 if isinstance(value, (dict, ABCSeries)):
-                    value = create_series_with_explicit_dtype(
+                    value_map = create_series_with_explicit_dtype(
                         value, dtype_if_empty=object
                     )
-                    value = value.reindex(self.index, copy=False)
-                    value = value._values
+                    # GH#40498 objects can have multiple types of missing values which
+                    # should not be modified unless specified.  Add special casing to
+                    # minimize performance decrease on other data types where this is
+                    # not required.
+                    if is_object_dtype(self.dtype):
+                        value = self.copy()
+                        modification_index = value.index.intersection(value_map.index)
+                        if not modification_index.empty:
+                            value.loc[modification_index] = value_map[
+                                modification_index
+                            ]
+                    else:
+                        value = value_map.reindex(self.index)
+                        value = value._values
                 elif not is_list_like(value):
                     pass
                 else:

pandas/tests/series/methods/test_fillna.py

@@ -623,6 +623,41 @@ def test_fillna_numeric_inplace(self):
         expected = x.fillna(value=0)
         tm.assert_series_equal(y, expected)
 
+    def test_fillna_object_null_value_replacement(
+        self, frame_or_series, unique_nulls_fixture, unique_nulls_fixture2
+    ):
+        # GH#40498
+        ser = Series([1, unique_nulls_fixture, "three"])
+        obj = frame_or_series(ser)
+
+        if unique_nulls_fixture2 is not None:
+            value = unique_nulls_fixture2
+        else:
+            pytest.skip(f"{unique_nulls_fixture2} cannot be passed to fillna.")
+
+        result = obj.fillna(value)
+
+        expected = Series([1, unique_nulls_fixture2, "three"])
+        expected = frame_or_series(expected)
+
+        tm.assert_equal(result, expected)
+
+    def test_fillna_object_other_missing_values_not_modified(
+        self, unique_nulls_fixture, unique_nulls_fixture2, frame_or_series
+    ):
+        # GH#40498
+        ser = Series([1, unique_nulls_fixture, unique_nulls_fixture2, "four"])
+        obj = frame_or_series(ser)
+
+        value = {2: 0} if frame_or_series is Series else {0: {2: 0}}
+
+        result = obj.fillna(value)
+
+        expected = Series([1, unique_nulls_fixture, 0, "four"])
+        expected = frame_or_series(expected)
+
+        tm.assert_equal(result, expected)
+
     # ---------------------------------------------------------------
     # CategoricalDtype