ENH: Categorical.fillna allow Categorical/ndarray

pandas/core/algorithms.py

@@ -11,6 +11,7 @@
 
 from pandas._libs import Timestamp, algos, hashtable as htable, lib
 from pandas._libs.tslib import iNaT
+from pandas._typing import AnyArrayLike
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.cast import (
@@ -45,10 +46,14 @@
     is_unsigned_integer_dtype,
     needs_i8_conversion,
 )
-from pandas.core.dtypes.generic import ABCIndex, ABCIndexClass, ABCSeries
+from pandas.core.dtypes.generic import (
+    ABCExtensionArray,
+    ABCIndex,
+    ABCIndexClass,
+    ABCSeries,
+)
 from pandas.core.dtypes.missing import isna, na_value_for_dtype
 
-import pandas.core.common as com
 from pandas.core.construction import array, extract_array
 from pandas.core.indexers import validate_indices
 
@@ -384,7 +389,7 @@ def unique(values):
 unique1d = unique
 
 
-def isin(comps, values) -> np.ndarray:
+def isin(comps: AnyArrayLike, values: AnyArrayLike) -> np.ndarray:
     """
     Compute the isin boolean array.
 
@@ -409,15 +414,14 @@ def isin(comps, values) -> np.ndarray:
             f"to isin(), you passed a [{type(values).__name__}]"
         )
 
-    if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
+    if not isinstance(values, (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray)):
         values = construct_1d_object_array_from_listlike(list(values))
 
+    comps = extract_array(comps, extract_numpy=True)
     if is_categorical_dtype(comps):
         # TODO(extension)
         # handle categoricals
-        return comps._values.isin(values)
-
-    comps = com.values_from_object(comps)
+        return comps.isin(values)  # type: ignore
 
     comps, dtype = _ensure_data(comps)
     values, _ = _ensure_data(values, dtype=dtype)

pandas/core/arrays/categorical.py

@@ -1738,12 +1738,17 @@ def fillna(self, value=None, method=None, limit=None):
 
             # If value is a dict or a Series (a dict value has already
             # been converted to a Series)
-            if isinstance(value, ABCSeries):
-                if not value[~value.isin(self.categories)].isna().all():
+            if isinstance(value, (np.ndarray, Categorical, ABCSeries)):
+                # We get ndarray or Categorical if called via Series.fillna,
+                #  where it will unwrap another aligned Series before getting here
+
+                mask = ~algorithms.isin(value, self.categories)
+                if not isna(value[mask]).all():
                     raise ValueError("fill value must be in categories")
 
                 values_codes = _get_codes_for_values(value, self.categories)
                 indexer = np.where(codes == -1)
+                codes = codes.copy()
                 codes[indexer] = values_codes[indexer]
 
             # If value is not a dict or Series it should be a scalar

pandas/core/generic.py

@@ -5988,6 +5988,8 @@ def fillna(
                     value = create_series_with_explicit_dtype(
                         value, dtype_if_empty=object
                     )
+                    value = value.reindex(self.index, copy=False)
+                    value = value._values
                 elif not is_list_like(value):
                     pass
                 else:

pandas/core/internals/managers.py

@@ -396,6 +396,7 @@ def apply(self, f, filter=None, **kwargs) -> "BlockManager":
         BlockManager
         """
         result_blocks = []
+        # fillna: Series/DataFrame is responsible for making sure value is aligned
 
         # filter kwarg is used in replace-* family of methods
         if filter is not None:
@@ -420,11 +421,6 @@ def apply(self, f, filter=None, **kwargs) -> "BlockManager":
                 align_keys = ["new", "mask"]
             else:
                 align_keys = ["mask"]
-        elif f == "fillna":
-            # fillna internally does putmask, maybe it's better to do this
-            # at mgr, not block level?
-            align_copy = False
-            align_keys = ["value"]
         else:
             align_keys = []
 

pandas/tests/arrays/categorical/test_missing.py

@@ -82,3 +82,18 @@ def test_fillna_iterable_category(self, named):
         expected = Categorical([Point(0, 0), Point(0, 1), Point(0, 0)])
 
         tm.assert_categorical_equal(result, expected)
+
+    def test_fillna_array(self):
+        # accept Categorical or ndarray value if it holds appropriate values
+        cat = Categorical(["A", "B", "C", None, None])
+
+        other = cat.fillna("C")
+        result = cat.fillna(other)
+        tm.assert_categorical_equal(result, other)
+        assert isna(cat[-1])  # didnt modify original inplace
+
+        other = np.array(["A", "B", "C", "B", "A"])
+        result = cat.fillna(other)
+        expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype)
+        tm.assert_categorical_equal(result, expected)
+        assert isna(cat[-1])  # didnt modify original inplace

pandas/tests/test_algos.py

@@ -760,6 +760,16 @@ def test_categorical_from_codes(self):
         result = algos.isin(Sd, St)
         tm.assert_numpy_array_equal(expected, result)
 
+    def test_categorical_isin(self):
+        vals = np.array([0, 1, 2, 0])
+        cats = ["a", "b", "c"]
+        cat = Categorical(1).from_codes(vals, cats)
+        other = Categorical(1).from_codes(np.array([0, 1]), cats)
+
+        expected = np.array([True, True, False, True])
+        result = algos.isin(cat, other)
+        tm.assert_numpy_array_equal(expected, result)
+
     def test_same_nan_is_in(self):
         # GH 22160
         # nan is special, because from " a is b" doesn't follow "a == b"