ENH: ExtensionArray.fillna

pandas/core/arrays/base.py

@@ -1,4 +1,6 @@
 """An interface for extending pandas with custom arrays."""
+import itertools
+
 import numpy as np
 
 from pandas.errors import AbstractMethodError
@@ -216,6 +218,88 @@ def isna(self):
         """
         raise AbstractMethodError(self)
 
+    def tolist(self):
+        # type: () -> list
+        """Convert the array to a list of scalars."""
+        return list(self)
+
+    def fillna(self, value=None, method=None, limit=None):
+        """ Fill NA/NaN values using the specified method.
+
+        Parameters
+        ----------
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            Method to use for filling holes in reindexed Series
+            pad / ffill: propagate last valid observation forward to next valid
+            backfill / bfill: use NEXT valid observation to fill gap
+        value : scalar, array-like
+            If a scalar value is passed it is used to fill all missing values.
+            Alternatively, an array-like 'value' can be given. It's expected
+            that the array-like have the same length as 'self'.
+        limit : int, default None
+            (Not implemented yet for ExtensionArray!)
+            If method is specified, this is the maximum number of consecutive
+            NaN values to forward/backward fill. In other words, if there is
+            a gap with more than this number of consecutive NaNs, it will only
+            be partially filled. If method is not specified, this is the
+            maximum number of entries along the entire axis where NaNs will be
+            filled.
+
+        Returns
+        -------
+        filled : ExtensionArray with NA/NaN filled
+        """
+        from pandas.api.types import is_scalar
+        from pandas.util._validators import validate_fillna_kwargs
+
+        value, method = validate_fillna_kwargs(value, method)
+
+        if not is_scalar(value):
+            if len(value) != len(self):
+                raise ValueError("Length of 'value' does not match. Got ({}) "
+                                 " expected {}".format(len(value), len(self)))
+        else:
+            value = itertools.cycle([value])
+
+        if limit is not None:
+            msg = ("Specifying 'limit' for 'fillna' has not been implemented "
+                   "yet for {} typed data".format(self.dtype))
+            raise NotImplementedError(msg)
+
+        mask = self.isna()
+
+        if mask.any():
+            # ffill / bfill
+            if method is not None:
+                if method == 'backfill':
+                    data = reversed(self)
+                    mask = reversed(mask)
+                    last_valid = self[len(self) - 1]
+                else:
+                    last_valid = self[0]
+                    data = self
+
+                new_values = []
+
+                for is_na, val in zip(mask, data):
+                    if is_na:
+                        new_values.append(last_valid)
+                    else:
+                        new_values.append(val)
+                        last_valid = val
+
+                if method in {'bfill', 'backfill'}:
+                    new_values = list(reversed(new_values))
+            else:
+                # fill with value
+                new_values = [
+                    val if is_na else original
+                    for is_na, original, val in zip(mask, self, value)
+                ]
+        else:
+            new_values = self
+        return type(self)(new_values)
+
     # ------------------------------------------------------------------------
     # Indexing methods
     # ------------------------------------------------------------------------

pandas/core/internals.py

@@ -1963,6 +1963,23 @@ def concat_same_type(self, to_concat, placement=None):
         return self.make_block_same_class(values, ndim=self.ndim,
                                           placement=placement)
 
+    def fillna(self, value, limit=None, inplace=False, downcast=None,
+               mgr=None):
+        values = self.values if inplace else self.values.copy()
+        values = values.fillna(value=value, limit=limit)
+        return [self.make_block_same_class(values=values,
+                                           placement=self.mgr_locs,
+                                           ndim=self.ndim)]
+
+    def interpolate(self, method='pad', axis=0, inplace=False, limit=None,
+                    fill_value=None, **kwargs):
+
+        values = self.values if inplace else self.values.copy()
+        return self.make_block_same_class(
+            values=values.fillna(value=fill_value, method=method,
+                                 limit=limit),
+            placement=self.mgr_locs)
+
 
 class NumericBlock(Block):
     __slots__ = ()
@@ -2522,27 +2539,6 @@ def _try_coerce_result(self, result):
 
         return result
 
-    def fillna(self, value, limit=None, inplace=False, downcast=None,
-               mgr=None):
-        # we may need to upcast our fill to match our dtype
-        if limit is not None:
-            raise NotImplementedError("specifying a limit for 'fillna' has "
-                                      "not been implemented yet")
-
-        values = self.values if inplace else self.values.copy()
-        values = self._try_coerce_result(values.fillna(value=value,
-                                                       limit=limit))
-        return [self.make_block(values=values)]
-
-    def interpolate(self, method='pad', axis=0, inplace=False, limit=None,
-                    fill_value=None, **kwargs):
-
-        values = self.values if inplace else self.values.copy()
-        return self.make_block_same_class(
-            values=values.fillna(fill_value=fill_value, method=method,
-                                 limit=limit),
-            placement=self.mgr_locs)
-
     def shift(self, periods, axis=0, mgr=None):
         return self.make_block_same_class(values=self.values.shift(periods),
                                           placement=self.mgr_locs)

pandas/tests/extension/base/missing.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 
 import pandas as pd
 import pandas.util.testing as tm
@@ -45,3 +46,82 @@ def test_dropna_frame(self, data_missing):
         result = df.dropna()
         expected = df.iloc[:0]
         self.assert_frame_equal(result, expected)
+
+    def test_fillna_limit_raises(self, data_missing):
+        ser = pd.Series(data_missing)
+        fill_value = data_missing[1]
+        xpr = "Specifying 'limit' for 'fillna'.*{}".format(data_missing.dtype)
+
+        with tm.assert_raises_regex(NotImplementedError, xpr):
+            ser.fillna(fill_value, limit=2)
+
+    def test_fillna_series(self, data_missing):
+        fill_value = data_missing[1]
+        ser = pd.Series(data_missing)
+
+        result = ser.fillna(fill_value)
+        expected = pd.Series(type(data_missing)([fill_value, fill_value]))
+        self.assert_series_equal(result, expected)
+
+        # Fill with a series
+        result = ser.fillna(expected)
+        self.assert_series_equal(result, expected)
+
+        # Fill with a series not affecting the missing values
+        result = ser.fillna(ser)
+        self.assert_series_equal(result, ser)
+
+    @pytest.mark.xfail(reason="Too magical?")
+    def test_fillna_series_with_dict(self, data_missing):
+        fill_value = data_missing[1]
+        ser = pd.Series(data_missing)
+        expected = pd.Series(type(data_missing)([fill_value, fill_value]))
+
+        # Fill with a dict
+        result = ser.fillna({0: fill_value})
+        self.assert_series_equal(result, expected)
+
+        # Fill with a dict not affecting the missing values
+        result = ser.fillna({1: fill_value})
+        ser = pd.Series(data_missing)
+        self.assert_series_equal(result, ser)
+
+    @pytest.mark.parametrize('method', ['ffill', 'bfill'])
+    def test_fillna_series_method(self, data_missing, method):
+        fill_value = data_missing[1]
+
+        if method == 'ffill':
+            data_missing = type(data_missing)(data_missing[::-1])
+
+        result = pd.Series(data_missing).fillna(method=method)
+        expected = pd.Series(type(data_missing)([fill_value, fill_value]))
+
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_frame(self, data_missing):
+        fill_value = data_missing[1]
+
+        result = pd.DataFrame({
+            "A": data_missing,
+            "B": [1, 2]
+        }).fillna(fill_value)
+
+        expected = pd.DataFrame({
+            "A": type(data_missing)([fill_value, fill_value]),
+            "B": [1, 2],
+        })
+
+        self.assert_frame_equal(result, expected)
+
+    def test_fillna_fill_other(self, data):
+        result = pd.DataFrame({
+            "A": data,
+            "B": [np.nan] * len(data)
+        }).fillna({"B": 0.0})
+
+        expected = pd.DataFrame({
+            "A": data,
+            "B": [0.0] * len(result),
+        })
+
+        self.assert_frame_equal(result, expected)

pandas/tests/extension/category/test_categorical.py

@@ -69,7 +69,10 @@ def test_getitem_scalar(self):
 
 
 class TestMissing(base.BaseMissingTests):
-    pass
+
+    @pytest.mark.skip(reason="Backwards compatability")
+    def test_fillna_limit_raises(self):
+        """Has a different error message."""
 
 
 class TestMethods(base.BaseMethodsTests):

pandas/tests/extension/decimal/test_decimal.py

@@ -35,68 +35,59 @@ def na_value():
     return decimal.Decimal("NaN")
 
 
-class TestDtype(base.BaseDtypeTests):
-    pass
+class BaseDecimal(object):
+    @staticmethod
+    def assert_series_equal(left, right, *args, **kwargs):
+        # tm.assert_series_equal doesn't handle Decimal('NaN').
+        # We will ensure that the NA values match, and then
+        # drop those values before moving on.
 
+        left_na = left.isna()
+        right_na = right.isna()
 
-class TestInterface(base.BaseInterfaceTests):
-    pass
+        tm.assert_series_equal(left_na, right_na)
+        tm.assert_series_equal(left[~left_na], right[~right_na],
+                               *args, **kwargs)
 
+    @staticmethod
+    def assert_frame_equal(left, right, *args, **kwargs):
+        # TODO(EA): select_dtypes
+        decimals = (left.dtypes == 'decimal').index
 
-class TestConstructors(base.BaseConstructorsTests):
-    pass
+        for col in decimals:
+            BaseDecimal.assert_series_equal(left[col], right[col],
+                                            *args, **kwargs)
 
+        left = left.drop(columns=decimals)
+        right = right.drop(columns=decimals)
+        tm.assert_frame_equal(left, right, *args, **kwargs)
 
-class TestReshaping(base.BaseReshapingTests):
 
-    def test_align(self, data, na_value):
-        # Have to override since assert_series_equal doesn't
-        # compare Decimal(NaN) properly.
-        a = data[:3]
-        b = data[2:5]
-        r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))
+class TestDtype(BaseDecimal, base.BaseDtypeTests):
+    pass
 
-        # NaN handling
-        e1 = pd.Series(type(data)(list(a) + [na_value]))
-        e2 = pd.Series(type(data)([na_value] + list(b)))
-        tm.assert_series_equal(r1.iloc[:3], e1.iloc[:3])
-        assert r1[3].is_nan()
-        assert e1[3].is_nan()
 
-        tm.assert_series_equal(r2.iloc[1:], e2.iloc[1:])
-        assert r2[0].is_nan()
-        assert e2[0].is_nan()
+class TestInterface(BaseDecimal, base.BaseInterfaceTests):
+    pass
 
-    def test_align_frame(self, data, na_value):
-        # Override for Decimal(NaN) comparison
-        a = data[:3]
-        b = data[2:5]
-        r1, r2 = pd.DataFrame({'A': a}).align(
-            pd.DataFrame({'A': b}, index=[1, 2, 3])
-        )
 
-        # Assumes that the ctor can take a list of scalars of the type
-        e1 = pd.DataFrame({'A': type(data)(list(a) + [na_value])})
-        e2 = pd.DataFrame({'A': type(data)([na_value] + list(b))})
+class TestConstructors(BaseDecimal, base.BaseConstructorsTests):
+    pass
 
-        tm.assert_frame_equal(r1.iloc[:3], e1.iloc[:3])
-        assert r1.loc[3, 'A'].is_nan()
-        assert e1.loc[3, 'A'].is_nan()
 
-        tm.assert_frame_equal(r2.iloc[1:], e2.iloc[1:])
-        assert r2.loc[0, 'A'].is_nan()
-        assert e2.loc[0, 'A'].is_nan()
+class TestReshaping(BaseDecimal, base.BaseReshapingTests):
+    pass
 
 
-class TestGetitem(base.BaseGetitemTests):
+class TestGetitem(BaseDecimal, base.BaseGetitemTests):
     pass
 
 
-class TestMissing(base.BaseMissingTests):
+class TestMissing(BaseDecimal, base.BaseMissingTests):
     pass
 
 
-class TestMethods(base.BaseMethodsTests):
+class TestMethods(BaseDecimal, base.BaseMethodsTests):
     @pytest.mark.parametrize('dropna', [True, False])
     @pytest.mark.xfail(reason="value_counts not implemented yet.")
     def test_value_counts(self, all_data, dropna):
@@ -112,7 +103,7 @@ def test_value_counts(self, all_data, dropna):
         tm.assert_series_equal(result, expected)
 
 
-class TestCasting(base.BaseCastingTests):
+class TestCasting(BaseDecimal, base.BaseCastingTests):
     pass
 
 

pandas/tests/extension/json/test_json.py

@@ -60,7 +60,13 @@ class TestGetitem(base.BaseGetitemTests):
 
 
 class TestMissing(base.BaseMissingTests):
-    pass
+    @pytest.mark.xfail(reason="Setting a dict as a scalar")
+    def test_fillna_series(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
+
+    @pytest.mark.xfail(reason="Setting a dict as a scalar")
+    def test_fillna_frame(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
 
 
 class TestMethods(base.BaseMethodsTests):