BUG: Allow a categorical series to accept dict or Series in fillna (pandas-dev#17033)

Author: reidy-p

pandas/core/categorical.py

@@ -1689,6 +1689,23 @@ def fillna(self, value=None, method=None, limit=None):
 
         else:
 
+            # print(value) # is value a scalar for df but series for series and this is why isna is causing problems?
+            # print(values)
+
+            if isinstance(value, ABCSeries):
+                if not value[~value.isin(self.categories)].isnull().all():
+                    raise ValueError("fill value must be in categories")
+                # if (not value.isin(self.categories).all() and
+                #     not value[~value.isin(self.categories)].isnull().all()):
+                #         raise ValueError("fill value must be in categories!")
+                # self.categories.get_loc(value[value.notna()])
+                # value[value.isna()] = np.array(list(self.categories[[1, 0]]))
+                mask = values == -1
+                # values[mask] = [self.categories.get_loc(x) for x in value[value.notna()]]
+                values[mask] = _get_codes_for_values(value[value.notna()], self.categories)
+                return self._constructor(values, categories=self.categories,
+                                         ordered=self.ordered, fastpath=True)
+
             if not isna(value) and value not in self.categories:
                 raise ValueError("fill value must be in categories")
 

pandas/core/generic.py

@@ -32,7 +32,7 @@
 from pandas.core.common import (_count_not_none,
                                 _maybe_box_datetimelike, _values_from_object,
                                 AbstractMethodError, SettingWithCopyError,
-                                SettingWithCopyWarning)
+                                SettingWithCopyWarning, is_categorical_dtype)
 
 from pandas.core.base import PandasObject, SelectionMixin
 from pandas.core.index import (Index, MultiIndex, _ensure_index,

pandas/tests/test_categorical.py

@@ -4603,6 +4603,18 @@ def f():
         df = pd.DataFrame({'a': pd.Categorical(idx)})
         tm.assert_frame_equal(df.fillna(value=pd.NaT), df)
 
+        s = pd.Series(pd.Categorical(['a', np.nan, 'b', np.nan], categories=['a', 'b']))
+        s1_exp = pd.Series(pd.Categorical(['a', 'a', 'b', 'a']))
+        tm.assert_series_equal(s.fillna('a'), s1_exp)
+
+        s = pd.Series(pd.Categorical(['a', np.nan, 'b', np.nan], categories=['a', 'b']))
+        s2_exp = pd.Series(pd.Categorical(['a', 'a', 'b', 'b']))
+        tm.assert_series_equal(s.fillna({1: 'a', 3: 'b'}), s2_exp)
+
+        s = pd.Series(pd.Categorical(['a', np.nan, 'b', np.nan], categories=['a', 'b']))
+        s3_exp = pd.Series(pd.Categorical(['a', 'a', 'b', 'a']))
+        tm.assert_series_equal(s.fillna(pd.Series('a')), s3_exp)
+
     def test_astype_to_other(self):
 
         s = self.cat['value_group']