BUG: Index.fillna silently ignoring 'downcast' kwarg (#44873)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -711,6 +711,7 @@ Missing
 - Bug in :meth:`DataFrame.fillna` not replacing missing values when using a dict-like ``value`` and duplicate column names (:issue:`43476`)
 - Bug in constructing a :class:`DataFrame` with a dictionary ``np.datetime64`` as a value and ``dtype='timedelta64[ns]'``, or vice-versa, incorrectly casting instead of raising (:issue:`??`)
 - Bug in :meth:`Series.interpolate` and :meth:`DataFrame.interpolate` with ``inplace=True`` not writing to the underlying array(s) in-place (:issue:`44749`)
+- Bug in :meth:`Index.fillna` incorrectly returning an un-filled :class:`Index` when NA values are present and ``downcast`` argument is specified. This now raises ``NotImplementedError`` instead; do not pass ``downcast`` argument (:issue:`44873`)
 -
 
 MultiIndex

pandas/core/indexes/base.py

@@ -2723,13 +2723,18 @@ def fillna(self, value=None, downcast=None):
         DataFrame.fillna : Fill NaN values of a DataFrame.
         Series.fillna : Fill NaN Values of a Series.
         """
+
         value = self._require_scalar(value)
         if self.hasnans:
             result = self.putmask(self._isnan, value)
             if downcast is None:
                 # no need to care metadata other than name
-                # because it can't have freq if
+                # because it can't have freq if it has NaTs
                 return Index._with_infer(result, name=self.name)
+            raise NotImplementedError(
+                f"{type(self).__name__}.fillna does not support 'downcast' "
+                "argument values other than 'None'."
+            )
         return self._view()
 
     def dropna(self: _IndexT, how: str_t = "any") -> _IndexT:

pandas/core/indexes/category.py

@@ -377,20 +377,6 @@ def __contains__(self, key: Any) -> bool:
 
         return contains(self, key, container=self._engine)
 
-    @doc(Index.fillna)
-    def fillna(self, value, downcast=None):
-        value = self._require_scalar(value)
-        try:
-            cat = self._data.fillna(value)
-        except (ValueError, TypeError):
-            # invalid fill_value
-            if not self.hasnans:
-                # nothing to fill, we can get away without casting
-                return self.copy()
-            return self.astype(object).fillna(value, downcast=downcast)
-
-        return type(self)._simple_new(cat, name=self.name)
-
     # TODO(2.0): remove reindex once non-unique deprecation is enforced
     def reindex(
         self, target, method=None, level=None, limit=None, tolerance=None

pandas/tests/indexes/categorical/test_fillna.py

@@ -25,17 +25,19 @@ def test_fillna_categorical(self):
         tm.assert_index_equal(result, expected)
 
     def test_fillna_copies_with_no_nas(self):
-        # Nothing to fill, should still get a copy
+        # Nothing to fill, should still get a copy for the Categorical method,
+        #  but OK to get a view on CategoricalIndex method
         ci = CategoricalIndex([0, 1, 1])
-        cat = ci._data
         result = ci.fillna(0)
-        assert result._values._ndarray is not cat._ndarray
-        assert result._values._ndarray.base is None
+        assert result is not ci
+        assert tm.shares_memory(result, ci)
 
-        # Same check directly on the Categorical object
+        # But at the EA level we always get a copy.
+        cat = ci._data
         result = cat.fillna(0)
         assert result._ndarray is not cat._ndarray
         assert result._ndarray.base is None
+        assert not tm.shares_memory(result, cat)
 
     def test_fillna_validates_with_no_nas(self):
         # We validate the fill value even if fillna is a no-op

pandas/tests/indexes/common.py

@@ -516,6 +516,11 @@ def test_fillna(self, index):
 
             idx = type(index)(values)
 
+            msg = "does not support 'downcast'"
+            with pytest.raises(NotImplementedError, match=msg):
+                # For now at least, we only raise if there are NAs present
+                idx.fillna(idx[0], downcast="infer")
+
             expected = np.array([False] * len(idx), dtype=bool)
             expected[1] = True
             tm.assert_numpy_array_equal(idx._isnan, expected)