Removed default_fill_value

pandas/compat/__init__.py

@@ -451,6 +451,3 @@ def is_platform_mac():
 
 def is_platform_32bit():
     return struct.calcsize("P") * 8 < 64
-
-
-_default_fill_value = object()

pandas/core/algorithms.py

@@ -30,7 +30,6 @@
     _ensure_platform_int, _ensure_object,
     _ensure_float64, _ensure_uint64,
     _ensure_int64)
-from pandas.compat import _default_fill_value
 from pandas.compat.numpy import _np_version_under1p10
 from pandas.core.dtypes.missing import isna, na_value_for_dtype
 
@@ -1449,10 +1448,10 @@ def func(arr, indexer, out, fill_value=np.nan):
     return func
 
 
-def take(arr, indexer, fill_value=_default_fill_value):
+def take(arr, indexer, fill_value=None, allow_fill=None):
     indexer = np.asarray(indexer)
 
-    if fill_value is _default_fill_value:
+    if allow_fill is None:
         # NumPy style
         result = arr.take(indexer)
     else:

pandas/core/arrays/base.py

@@ -10,7 +10,6 @@
 import numpy as np
 
 from pandas.errors import AbstractMethodError
-from pandas.compat import _default_fill_value
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution
 
@@ -535,7 +534,7 @@ def _values_for_take(self):
 
     @Substitution(arr='')
     @Appender(_take_docstring)
-    def take(self, indexer, fill_value=_default_fill_value):
+    def take(self, indexer, fill_value=None, allow_fill=None):
         # type: (Sequence[int], Optional[Any]) -> ExtensionArray
         # assert fill_value is not np.nan
         from pandas.core.algorithms import take

pandas/core/dtypes/cast.py

@@ -7,7 +7,7 @@
 
 from pandas._libs import tslib, lib
 from pandas._libs.tslib import iNaT
-from pandas.compat import string_types, text_type, PY3, _default_fill_value
+from pandas.compat import string_types, text_type, PY3
 from .common import (_ensure_object, is_bool, is_integer, is_float,
                      is_complex, is_datetimetz, is_categorical_dtype,
                      is_datetimelike,
@@ -255,9 +255,6 @@ def changeit():
 
 
 def maybe_promote(dtype, fill_value=np.nan):
-    if fill_value is _default_fill_value:
-        fill_value = np.nan
-
     # if we passed an array here, determine the fill value by dtype
     if isinstance(fill_value, np.ndarray):
         if issubclass(fill_value.dtype.type, (np.datetime64, np.timedelta64)):

pandas/core/frame.py

@@ -77,7 +77,7 @@
 from pandas.core.arrays import Categorical, ExtensionArray
 import pandas.core.algorithms as algorithms
 from pandas.compat import (range, map, zip, lrange, lmap, lzip, StringIO, u,
-                           OrderedDict, raise_with_traceback, _default_fill_value)
+                           OrderedDict, raise_with_traceback)
 from pandas import compat
 from pandas.compat import PY36
 from pandas.compat.numpy import function as nv
@@ -3504,7 +3504,7 @@ def _reindex_multi(self, axes, copy, fill_value):
 
     @Appender(_shared_docs['align'] % _shared_doc_kwargs)
     def align(self, other, join='outer', axis=None, level=None, copy=True,
-              fill_value=_default_fill_value, method=None, limit=None, fill_axis=0,
+              fill_value=None, method=None, limit=None, fill_axis=0,
               broadcast_axis=None):
         return super(DataFrame, self).align(other, join=join, axis=axis,
                                             level=level, copy=copy,

pandas/core/generic.py

@@ -50,8 +50,7 @@
 from pandas import compat
 from pandas.compat.numpy import function as nv
 from pandas.compat import (map, zip, lzip, lrange, string_types, to_str,
-                           isidentifier, set_function_name, cPickle as pkl,
-                           _default_fill_value)
+                           isidentifier, set_function_name, cPickle as pkl)
 from pandas.core.ops import _align_method_FRAME
 import pandas.core.nanops as nanops
 from pandas.util._decorators import (Appender, Substitution,
@@ -3661,7 +3660,7 @@ def reindex(self, *args, **kwargs):
         copy = kwargs.pop('copy', True)
         limit = kwargs.pop('limit', None)
         tolerance = kwargs.pop('tolerance', None)
-        fill_value = kwargs.pop('fill_value', _default_fill_value)
+        fill_value = kwargs.pop('fill_value', np.nan)
 
         # Series.reindex doesn't use / need the axis kwarg
         # We pop and ignore it here, to make writing Series/Frame generic code
@@ -3791,9 +3790,7 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True,
         return self._reindex_with_indexers({axis: [new_index, indexer]},
                                            fill_value=fill_value, copy=copy)
 
-    def _reindex_with_indexers(self, reindexers,
-                               fill_value=_default_fill_value,
-                               copy=False,
+    def _reindex_with_indexers(self, reindexers, fill_value=np.nan, copy=False,
                                allow_dups=False):
         """allow_dups indicates an internal call here """
 
@@ -7212,7 +7209,7 @@ def ranker(data):
 
     @Appender(_shared_docs['align'] % _shared_doc_kwargs)
     def align(self, other, join='outer', axis=None, level=None, copy=True,
-              fill_value=_default_fill_value, method=None, limit=None, fill_axis=0,
+              fill_value=None, method=None, limit=None, fill_axis=0,
               broadcast_axis=None):
         from pandas import DataFrame, Series
         method = missing.clean_fill_method(method)
@@ -7263,9 +7260,6 @@ def _align_frame(self, other, join='outer', axis=None, level=None,
         clidx, cridx = None, None
 
         is_series = isinstance(self, ABCSeries)
-        if fill_value is _default_fill_value:
-            # XXX: per-column?
-            fill_value = np.nan
 
         if axis is None or axis == 0:
             if not self.index.equals(other.index):
@@ -7365,9 +7359,6 @@ def _align_series(self, other, join='outer', axis=None, level=None,
                 right = other.reindex(join_index, level=level)
 
         # fill
-        if fill_value is _default_fill_value:
-            fill_value = None
-
         fill_na = notna(fill_value) or (method is not None)
         if fill_na:
             left = left.fillna(fill_value, method=method, limit=limit,

pandas/core/internals.py

@@ -1,5 +1,4 @@
 import warnings
-
 import copy
 from warnings import catch_warnings
 import inspect
@@ -83,7 +82,7 @@
 from pandas.util._decorators import cache_readonly
 from pandas.util._validators import validate_bool_kwarg
 from pandas import compat
-from pandas.compat import range, map, zip, u, _default_fill_value
+from pandas.compat import range, map, zip, u
 
 
 class Block(PandasObject):
@@ -1889,10 +1888,6 @@ def _holder(self):
         # For extension blocks, the holder is values-dependent.
         return type(self.values)
 
-    @property
-    def fill_value(self):
-        return self.values.dtype.na_value  # TODO: change to _na_value
-
     @property
     def _can_hold_na(self):
         # The default ExtensionArray._can_hold_na is True
@@ -4391,8 +4386,6 @@ def reindex_indexer(self, new_axis, indexer, axis, fill_value=None,
 
         pandas-indexer with -1's only.
         """
-        # TODO: see if we can make fill_value be {col -> fill_value}
-        # maybe earlier...
         if indexer is None:
             if new_axis is self.axes[axis] and not copy:
                 return self
@@ -4415,17 +4408,9 @@ def reindex_indexer(self, new_axis, indexer, axis, fill_value=None,
             new_blocks = self._slice_take_blocks_ax0(indexer,
                                                      fill_tuple=(fill_value,))
         else:
-            if fill_value is None:
-                fill_value = _default_fill_value
-
-            new_blocks = []
-            for blk in self.blocks:
-                if fill_value is not _default_fill_value:
-                    fill_tuple = (fill_value,)
-                else:
-                    fill_tuple = (blk.fill_value,)
-            new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=fill_tuple)
-                          for blk in self.blocks]
+            new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(
+                fill_value if fill_value is not None else blk.fill_value,))
+                for blk in self.blocks]
 
         new_axes = list(self.axes)
         new_axes[axis] = new_axis
@@ -4451,9 +4436,6 @@ def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None):
         if self._is_single_block:
             blk = self.blocks[0]
 
-            if allow_fill and fill_tuple[0] is _default_fill_value:
-                fill_tuple = (blk.fill_value,)
-
             if sl_type in ('slice', 'mask'):
                 return [blk.getitem_block(slobj, new_mgr_locs=slice(0, sllen))]
             elif not allow_fill or self.ndim == 1:
@@ -5422,25 +5404,6 @@ def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
         elif is_uniform_join_units(join_units):
             b = join_units[0].block.concat_same_type(
                 [ju.block for ju in join_units], placement=placement)
-        elif is_uniform_reindexer(join_units):
-            old_block = join_units[0].block
-
-            new_values = concatenate_join_units(join_units, concat_axis,
-                                                copy=copy)
-            if new_values.ndim == 2:
-                # XXX: categorical returns a categorical here
-                # EA returns a 2d ndarray
-                # need to harmoinze these to always be EAs?
-                assert new_values.shape[0] == 1
-                new_values = new_values[0]
-
-            assert isinstance(old_block._holder, ABCExtensionArray)
-
-            b = old_block.make_block_same_class(
-                old_block._holder._from_sequence(new_values),
-                placement=placement
-            )
-
         else:
             b = make_block(
                 concatenate_join_units(join_units, concat_axis, copy=copy),
@@ -5471,13 +5434,6 @@ def is_uniform_join_units(join_units):
         len(join_units) > 1)
 
 
-def is_uniform_reindexer(join_units):
-    # For when we know we can reindex without changing type
-    return (
-        all(ju.block and ju.block.is_extension for ju in join_units)
-    )
-
-
 def get_empty_dtype_and_na(join_units):
     """
     Return dtype and N/A values to use when concatenating specified units.
@@ -5505,15 +5461,12 @@ def get_empty_dtype_and_na(join_units):
 
     upcast_classes = defaultdict(list)
     null_upcast_classes = defaultdict(list)
-
     for dtype, unit in zip(dtypes, join_units):
         if dtype is None:
             continue
 
         if is_categorical_dtype(dtype):
             upcast_cls = 'category'
-        elif is_extension_array_dtype(dtype):
-            upcast_cls = 'extension'
         elif is_datetimetz(dtype):
             upcast_cls = 'datetimetz'
         elif issubclass(dtype.type, np.bool_):
@@ -5543,8 +5496,6 @@ def get_empty_dtype_and_na(join_units):
     # create the result
     if 'object' in upcast_classes:
         return np.dtype(np.object_), np.nan
-    elif 'extension' in upcast_classes:
-        return np.dtype(np.object_), None
     elif 'bool' in upcast_classes:
         if has_none_blocks:
             return np.dtype(np.object_), np.nan
@@ -5804,9 +5755,7 @@ def dtype(self):
         if self.block is None:
             raise AssertionError("Block is None, no dtype")
 
-        if not self.needs_filling or self.block.is_extension:
-            # ExtensionDtypes by definition can hold their
-            # NA value.
+        if not self.needs_filling:
             return self.block.dtype
         else:
             return _get_dtype(maybe_promote(self.block.dtype,

pandas/core/series.py

@@ -56,7 +56,7 @@
 from pandas import compat
 from pandas.io.formats.terminal import get_terminal_size
 from pandas.compat import (
-    zip, u, OrderedDict, StringIO, range, get_range_parameters, PY36, _default_fill_value)
+    zip, u, OrderedDict, StringIO, range, get_range_parameters, PY36)
 from pandas.compat.numpy import function as nv
 
 import pandas.core.ops as ops
@@ -3216,10 +3216,7 @@ def _reindex_indexer(self, new_index, indexer, copy):
                 return self.copy()
             return self
 
-        from pandas.core.dtypes.missing import na_value_for_dtype
-        fill_value = na_value_for_dtype(self.dtype, compat=False)
-        new_values = algorithms.take(self._values, indexer,
-                                     fill_value=fill_value)
+        new_values = algorithms.take_1d(self._values, indexer)
         return self._constructor(new_values, index=new_index)
 
     def _needs_reindex_multi(self, axes, method, level):
@@ -3230,7 +3227,7 @@ def _needs_reindex_multi(self, axes, method, level):
 
     @Appender(generic._shared_docs['align'] % _shared_doc_kwargs)
     def align(self, other, join='outer', axis=None, level=None, copy=True,
-              fill_value=_default_fill_value, method=None, limit=None, fill_axis=0,
+              fill_value=None, method=None, limit=None, fill_axis=0,
               broadcast_axis=None):
         return super(Series, self).align(other, join=join, axis=axis,
                                          level=level, copy=copy,

pandas/core/sparse/frame.py

@@ -6,7 +6,7 @@
 # pylint: disable=E1101,E1103,W0231,E0202
 
 import warnings
-from pandas.compat import lmap, _default_fill_value
+from pandas.compat import lmap
 from pandas import compat
 import numpy as np
 
@@ -690,7 +690,7 @@ def _reindex_columns(self, columns, method, copy, level, fill_value=None,
         if level is not None:
             raise TypeError('Reindex by level not supported for sparse')
 
-        if not (isna(fill_value) or fill_value is _default_fill_value):
+        if notna(fill_value):
             raise NotImplementedError("'fill_value' argument is not supported")
 
         if limit: