Clobber commits together to ease merge.

Author: ResidentMario

Diff for: pandas/core/dtypes/common.py

@@ -739,27 +739,3 @@ def pandas_dtype(dtype):
         return dtype
 
     return np.dtype(dtype)
-
-
-def _is_fillable_value(value):
-    pandas_ts_types = ('Timestamp', 'Period', 'Timedelta')
-    pandas_block_types = ('Series', 'DataFrame')
-
-    if any([isinstance(value, (list, dict)),
-            callable(value),
-            (not (isinstance(value, string_types) or
-                  isinstance(value, (int, float, complex, str, None.__class__)) or
-                  is_numeric_dtype(value) or
-                  is_datetime_or_timedelta_dtype(value) or
-                  is_period_dtype(value) or
-                  type(value).__name__ in pandas_ts_types) or
-                  type(value).__name__ in pandas_block_types)]):
-        return False
-    else:
-        return True
-
-
-def validate_fill_value(value):
-    if not _is_fillable_value(value):
-        raise TypeError('"value" parameter must be a scalar, but '
-                        'you passed a "{0}"'.format(type(value).__name__))

Diff for: pandas/core/dtypes/missing.py

@@ -19,7 +19,10 @@
                      is_object_dtype,
                      is_integer,
                      _TD_DTYPE,
-                     _NS_DTYPE)
+                     _NS_DTYPE,
+                     is_datetime64_any_dtype, is_float,
+                     is_numeric_dtype, is_complex, is_period_arraylike)
+from datetime import datetime, timedelta
 from .inference import is_list_like
 
 
@@ -394,3 +397,32 @@ def na_value_for_dtype(dtype):
     elif is_bool_dtype(dtype):
         return False
     return np.nan
+
+
+def is_valid_fill_value(value, dtype):
+    """
+    Makes sure the fill value is appropriate for the given dtype.
+
+    Parameters
+    ----------
+    value : scalar
+    dtype: string / dtype
+    """
+    if isinstance(value, dict):
+        return True
+    if not is_scalar(value):
+        # maybe always raise?
+        # raise TypeError('"value" parameter must be a scalar or dict, but '
+        #                 'you passed a "{0}"'.format(type(value).__name__))
+        return False
+    elif isnull(value):
+        return True
+    elif is_bool_dtype(dtype):
+        return isinstance(value, (np.bool, bool))
+    elif is_numeric_dtype(dtype):
+        return is_float(value) or is_integer(value) or is_complex(value)
+    elif is_datetime64_any_dtype(dtype):
+        return isinstance(value, (np.datetime64, datetime))
+    elif is_timedelta64_dtype(dtype):
+        return isinstance(value, (np.timedelta64, timedelta))
+    return True

Diff for: pandas/core/generic.py

@@ -26,7 +26,7 @@
     is_re_compilable)
 from pandas.core.dtypes.cast import maybe_promote, maybe_upcast_putmask
 from pandas.core.dtypes.missing import isnull, notnull
-from pandas.core.dtypes.generic import ABCSeries, ABCPanel
+from pandas.core.dtypes.generic import ABCSeries, ABCPanel, ABCDataFrame
 
 from pandas.core.common import (_values_from_object,
                                 _maybe_box_datetimelike,
@@ -3443,9 +3443,24 @@ def convert_objects(self, convert_dates=True, convert_numeric=False,
 
     @Appender(_shared_docs['fillna'] % _shared_doc_kwargs)
     def fillna(self, value=None, method=None, axis=None, inplace=False,
-               limit=None, downcast=None):
+               limit=None, downcast=None, errors=None):
         inplace = validate_bool_kwarg(inplace, 'inplace')
 
+        # if a singular fill value is provided, validate it
+        if value is not None:
+            # fill values by column, not all at once, to respect dtypes
+            if not isinstance(value, (dict, ABCSeries)) and \
+                    isinstance(self, ABCDataFrame):
+                value = {col: value for col in self.columns}
+            try:
+                missing.validate_fill_value(self, value)
+            except TypeError:
+                if errors == 'ignore':
+                    return self
+                elif errors == 'raise':
+                    raise
+                # if errors == 'coerce' continue
+
         if isinstance(value, (list, tuple)):
             raise TypeError('"value" parameter must be a scalar or dict, but '
                             'you passed a "{0}"'.format(type(value).__name__))
@@ -3464,7 +3479,8 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
             if self._is_mixed_type and axis == 1:
                 if inplace:
                     raise NotImplementedError()
-                result = self.T.fillna(method=method, limit=limit).T
+                result = self.T.fillna(method=method, limit=limit,
+                                       errors=errors).T
 
                 # need to downcast here because of all of the transposes
                 result._data = result._data.downcast()
@@ -3480,7 +3496,8 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
             elif self.ndim == 3:
 
                 # fill in 2d chunks
-                result = dict([(col, s.fillna(method=method, value=value))
+                result = dict([(col, s.fillna(method=method, value=value,
+                                              errors=errors))
                                for col, s in self.iteritems()])
                 new_obj = self._constructor.\
                     from_dict(result).__finalize__(self)
@@ -3512,7 +3529,8 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
 
                 new_data = self._data.fillna(value=value, limit=limit,
                                              inplace=inplace,
-                                             downcast=downcast)
+                                             downcast=downcast,
+                                             errors=errors)
 
             elif isinstance(value, (dict, ABCSeries)):
                 if axis == 1:
@@ -3525,12 +3543,14 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
                     if k not in result:
                         continue
                     obj = result[k]
-                    obj.fillna(v, limit=limit, inplace=True, downcast=downcast)
-                return result
+                    obj.fillna(v, limit=limit, inplace=True,
+                               downcast=downcast, errors=errors)
+                return None if inplace else result
             elif not is_list_like(value):
                 new_data = self._data.fillna(value=value, limit=limit,
                                              inplace=inplace,
-                                             downcast=downcast)
+                                             downcast=downcast,
+                                             errors=errors)
             elif isinstance(value, DataFrame) and self.ndim == 2:
                 new_data = self.where(self.notnull(), value)
             else:

Diff for: pandas/core/internals.py

@@ -362,10 +362,13 @@ def apply(self, func, mgr=None, **kwargs):
         return result
 
     def fillna(self, value, limit=None, inplace=False, downcast=None,
-               mgr=None):
+               errors=None, mgr=None):
         """ fillna on the block with the value. If we fail, then convert to
         ObjectBlock and try again
         """
+        if not errors:
+            errors = 'coerce'
+
         inplace = validate_bool_kwarg(inplace, 'inplace')
 
         if not self._can_hold_na:
@@ -399,12 +402,16 @@ def fillna(self, value, limit=None, inplace=False, downcast=None,
             if not mask.any():
                 return self if inplace else self.copy()
 
-            # we cannot coerce the underlying object, so
-            # make an ObjectBlock
-            return self.to_object_block(mgr=mgr).fillna(original_value,
-                                                        limit=limit,
-                                                        inplace=inplace,
-                                                        downcast=False)
+            if errors == 'coerce':
+                # we cannot coerce the underlying object, so
+                # make an ObjectBlock
+                return self.to_object_block(mgr=mgr).fillna(original_value,
+                                                            limit=limit,
+                                                            inplace=inplace,
+                                                            downcast=False,
+                                                            errors='ignore')
+            else:  # errors == 'ignore'
+                return self
 
     def _maybe_downcast(self, blocks, downcast=None):
 
@@ -2132,11 +2139,14 @@ def _try_coerce_result(self, result):
         return result
 
     def fillna(self, value, limit=None, inplace=False, downcast=None,
-               mgr=None):
+               errors=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")
+        if errors is not None:
+            raise NotImplementedError("specifying error handling 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,
@@ -2626,11 +2636,13 @@ def interpolate(self, method='pad', axis=0, inplace=False, limit=None,
                                           placement=self.mgr_locs)
 
     def fillna(self, value, limit=None, inplace=False, downcast=None,
-               mgr=None):
+               errors=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")
+        if errors is not None:
+            raise NotImplementedError
         values = self.values if inplace else self.values.copy()
         values = values.fillna(value, downcast=downcast)
         return [self.make_block_same_class(values=values,

Diff for: pandas/core/missing.py

@@ -20,7 +20,8 @@
     _ensure_float64)
 
 from pandas.core.dtypes.cast import infer_dtype_from_array
-from pandas.core.dtypes.missing import isnull
+from pandas.core.dtypes.missing import isnull, is_valid_fill_value
+from pandas.core.dtypes.generic import ABCSeries
 
 
 def mask_missing(arr, values_to_mask):
@@ -624,3 +625,35 @@ def fill_zeros(result, x, y, name, fill):
             result = result.reshape(shape)
 
     return result
+
+
+def validate_fill_value(obj, value):
+    """
+
+    Fillna error coercion routine.
+
+    Parameters
+    ----------
+    obj : Series of DataFrame
+        The Series or DataFrame for which a fill value is being evaluated.
+        If obj is a DataFrame this method simply returns True (e.g. the fillna
+        operation is allowed to continue) because it will be broken up and
+        parsed as a sequence of sub-Series later on.
+    value : object
+        The value to be used as a fill for the object.
+
+    Returns
+    -------
+    continue : bool
+        Whether or not, based on the values and the error mode, the fill
+        operation ought to continue.
+    """
+    """
+    fillna error coercion routine, returns whether or not to continue.
+    """
+    if isinstance(obj, ABCSeries):
+        if not is_valid_fill_value(value, obj.dtype):
+            raise TypeError('"value" parameter must be compatible '
+                            'with the {0} dtype, but you passed a '
+                            '"{1}"'.format(obj.dtype,
+                                           type(value).__name__))

Diff for: pandas/tests/dtypes/test_missing.py

@@ -2,8 +2,9 @@
 
 from warnings import catch_warnings
 import numpy as np
-from datetime import datetime
+from datetime import datetime, timedelta
 from pandas.util import testing as tm
+import pytest
 
 import pandas as pd
 from pandas.core import config as cf
@@ -14,7 +15,7 @@
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 from pandas.core.dtypes.missing import (
     array_equivalent, isnull, notnull,
-    na_value_for_dtype)
+    na_value_for_dtype, is_valid_fill_value)
 
 
 def test_notnull():
@@ -312,3 +313,35 @@ def test_na_value_for_dtype():
 
     for dtype in ['O']:
         assert np.isnan(na_value_for_dtype(np.dtype(dtype)))
+
+
+@pytest.mark.parametrize(('value', 'dtype'),
+                         [(False, bool), (np.nan, bool),
+                          (0, int), (0.0, int), (0j, int), (np.nan, int),
+                          (0, float), (0.0, float), (0j, float),
+                          (np.nan, float),
+                          (0, complex), (0.0, complex), (0j, complex),
+                          (np.nan, complex),
+                          (False, str), (0, str), (0.0, str), (0j, str),
+                          (np.nan, str), ('0', str),
+                          (datetime(1970, 1, 1), np.datetime64),
+                          (pd.Timestamp('1970-01-01'), np.datetime64),
+                          (timedelta(0), np.timedelta64),
+                          (pd.Timedelta(0), np.timedelta64)])
+def test_valid_fill_value(value, dtype):
+    assert is_valid_fill_value(value, dtype)
+
+
+@pytest.mark.parametrize(('value', 'dtype'),
+                         [(0, bool), (0.0, bool), (0j, bool), ('0', bool),
+                          ('0', int),
+                          ('0', float),
+                          ('0', complex),
+                          ('0', np.dtype('datetime64')),
+                          (timedelta(0), np.dtype('datetime64')),
+                          (pd.Period('1970-01-01'), np.dtype('datetime64')),
+                          ('0', np.dtype('timedelta64')),
+                          (datetime(1970, 1, 1), np.dtype('timedelta64')),
+                          (pd.Period('1970-01-01'), np.dtype('timedelta64'))])
+def test_invalid_fill_value(value, dtype):
+    assert not is_valid_fill_value(value, dtype)