Merge pull request #6588 from jreback/date_fill

BUG: Bug in fillna with method = bfill/ffill and datetime64[ns] dtype (GH6587)

Author: jreback

doc/source/release.rst

@@ -225,6 +225,7 @@ Bug Fixes
 - Bug in indexing: empty list lookup caused ``IndexError`` exceptions (:issue:`6536`, :issue:`6551`)
 - Series.quantile raising on an ``object`` dtype (:issue:`6555`)
 - Bug in ``.xs`` with a ``nan`` in level when dropped (:issue:`6574`)
+- Bug in fillna with method = 'bfill/ffill' and ``datetime64[ns]`` dtype (:issue:`6587`)
 
 pandas 0.13.1
 -------------

pandas/core/common.py

@@ -1244,13 +1244,14 @@ def wrapper(arr, mask, limit=None):
                                         np.int64)
 
 
-def pad_1d(values, limit=None, mask=None):
+def pad_1d(values, limit=None, mask=None, dtype=None):
 
-    dtype = values.dtype.name
+    if dtype is None:
+        dtype = values.dtype
     _method = None
     if is_float_dtype(values):
-        _method = getattr(algos, 'pad_inplace_%s' % dtype, None)
-    elif is_datetime64_dtype(values):
+        _method = getattr(algos, 'pad_inplace_%s' % dtype.name, None)
+    elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values):
         _method = _pad_1d_datetime
     elif is_integer_dtype(values):
         values = _ensure_float64(values)
@@ -1259,7 +1260,7 @@ def pad_1d(values, limit=None, mask=None):
         _method = algos.pad_inplace_object
 
     if _method is None:
-        raise ValueError('Invalid dtype for pad_1d [%s]' % dtype)
+        raise ValueError('Invalid dtype for pad_1d [%s]' % dtype.name)
 
     if mask is None:
         mask = isnull(values)
@@ -1268,13 +1269,14 @@ def pad_1d(values, limit=None, mask=None):
     return values
 
 
-def backfill_1d(values, limit=None, mask=None):
+def backfill_1d(values, limit=None, mask=None, dtype=None):
 
-    dtype = values.dtype.name
+    if dtype is None:
+        dtype = values.dtype
     _method = None
     if is_float_dtype(values):
-        _method = getattr(algos, 'backfill_inplace_%s' % dtype, None)
-    elif is_datetime64_dtype(values):
+        _method = getattr(algos, 'backfill_inplace_%s' % dtype.name, None)
+    elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values):
         _method = _backfill_1d_datetime
     elif is_integer_dtype(values):
         values = _ensure_float64(values)
@@ -1283,7 +1285,7 @@ def backfill_1d(values, limit=None, mask=None):
         _method = algos.backfill_inplace_object
 
     if _method is None:
-        raise ValueError('Invalid dtype for backfill_1d [%s]' % dtype)
+        raise ValueError('Invalid dtype for backfill_1d [%s]' % dtype.name)
 
     if mask is None:
         mask = isnull(values)
@@ -1293,13 +1295,14 @@ def backfill_1d(values, limit=None, mask=None):
     return values
 
 
-def pad_2d(values, limit=None, mask=None):
+def pad_2d(values, limit=None, mask=None, dtype=None):
 
-    dtype = values.dtype.name
+    if dtype is None:
+        dtype = values.dtype
     _method = None
     if is_float_dtype(values):
-        _method = getattr(algos, 'pad_2d_inplace_%s' % dtype, None)
-    elif is_datetime64_dtype(values):
+        _method = getattr(algos, 'pad_2d_inplace_%s' % dtype.name, None)
+    elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values):
         _method = _pad_2d_datetime
     elif is_integer_dtype(values):
         values = _ensure_float64(values)
@@ -1308,7 +1311,7 @@ def pad_2d(values, limit=None, mask=None):
         _method = algos.pad_2d_inplace_object
 
     if _method is None:
-        raise ValueError('Invalid dtype for pad_2d [%s]' % dtype)
+        raise ValueError('Invalid dtype for pad_2d [%s]' % dtype.name)
 
     if mask is None:
         mask = isnull(values)
@@ -1322,13 +1325,14 @@ def pad_2d(values, limit=None, mask=None):
     return values
 
 
-def backfill_2d(values, limit=None, mask=None):
+def backfill_2d(values, limit=None, mask=None, dtype=None):
 
-    dtype = values.dtype.name
+    if dtype is None:
+        dtype = values.dtype
     _method = None
     if is_float_dtype(values):
-        _method = getattr(algos, 'backfill_2d_inplace_%s' % dtype, None)
-    elif is_datetime64_dtype(values):
+        _method = getattr(algos, 'backfill_2d_inplace_%s' % dtype.name, None)
+    elif dtype in _DATELIKE_DTYPES or is_datetime64_dtype(values):
         _method = _backfill_2d_datetime
     elif is_integer_dtype(values):
         values = _ensure_float64(values)
@@ -1337,7 +1341,7 @@ def backfill_2d(values, limit=None, mask=None):
         _method = algos.backfill_2d_inplace_object
 
     if _method is None:
-        raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype)
+        raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype.name)
 
     if mask is None:
         mask = isnull(values)
@@ -1503,7 +1507,7 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
     return new_y
 
 
-def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None):
+def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None):
     """ perform an actual interpolation of values, values will be make 2-d if
     needed fills inplace, returns the result
     """
@@ -1525,9 +1529,9 @@ def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None):
 
     method = _clean_fill_method(method)
     if method == 'pad':
-        values = transf(pad_2d(transf(values), limit=limit, mask=mask))
+        values = transf(pad_2d(transf(values), limit=limit, mask=mask, dtype=dtype))
     else:
-        values = transf(backfill_2d(transf(values), limit=limit, mask=mask))
+        values = transf(backfill_2d(transf(values), limit=limit, mask=mask, dtype=dtype))
 
     # reshape back
     if ndim == 1:

pandas/core/internals.py

@@ -878,7 +878,12 @@ def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
         fill_value = self._try_fill(fill_value)
         values = self.values if inplace else self.values.copy()
         values = self._try_operate(values)
-        values = com.interpolate_2d(values, method, axis, limit, fill_value)
+        values = com.interpolate_2d(values,
+                                    method=method,
+                                    axis=axis,
+                                    limit=limit,
+                                    fill_value=fill_value,
+                                    dtype=self.dtype)
         values = self._try_coerce_result(values)
 
         blocks = [make_block(values, self.items, self.ref_items,

pandas/tests/test_series.py

@@ -2854,6 +2854,14 @@ def test_datetime64_fillna(self):
                            Timestamp('20130103 9:01:01')])
         assert_series_equal(result, expected)
 
+        # GH 6587
+        # make sure that we are treating as integer when filling
+        s = Series([pd.NaT, pd.NaT, '2013-08-05 15:30:00.000001'])
+        expected = Series(['2013-08-05 15:30:00.000001', '2013-08-05 15:30:00.000001', '2013-08-05 15:30:00.000001'], dtype='M8[ns]')
+        result = s.fillna(method='backfill')
+        assert_series_equal(result, expected)
+
+
     def test_fillna_int(self):
         s = Series(np.random.randint(-100, 100, 50))
         s.fillna(method='ffill', inplace=True)