def fillna(self, fill_value, inplace=False):
"""
Fill null values with *fill_value*
"""
result = self.copy()
fill_value_col, result = numeric_normalize_types(
columnops.as_column(fill_value, nan_as_null=False), result)
cpp_replace.replace_nulls(result, fill_value_col)
result = result.replace(mask=None)
return self._mimic_inplace(result, inplace)
def fillna(self, fill_value, inplace=False):
"""
Fill null values with *fill_value*
"""
result = self.copy()
fill_value_col = columnops.as_column(fill_value, nan_as_null=False)
if is_integer_dtype(result.dtype):
fill_value_col = safe_cast_to_int(fill_value_col, result.dtype)
else:
fill_value_col = fill_value_col.astype(result.dtype)
cpp_replace.replace_nulls(result, fill_value_col)
result = result.replace(mask=None)
return self._mimic_inplace(result, inplace)
def fillna(self, fill_value, inplace=False):
result = self.copy()
if np.isscalar(fill_value):
fill_value = np.datetime64(fill_value, 'ms')
elif pd.core.dtypes.common.is_datetime_or_timedelta_dtype(fill_value):
fill_value = pd.to_datetime(fill_value)
fill_value_col = columnops.as_column(fill_value, nan_as_null=False)
cpp_replace.replace_nulls(result, fill_value_col)
result = result.replace(mask=None)
return self._mimic_inplace(result, inplace)
def fillna(self, fill_value, inplace=False):
"""
Fill null values with *fill_value*
"""
result = self.copy()
if np.isscalar(fill_value):
if fill_value != self.default_na_value():
if (fill_value not in self.cat().categories):
raise ValueError("fill value must be in categories")
fill_value = pd.Categorical(fill_value,
categories=self.cat().categories)
fill_value_col = columnops.as_column(fill_value, nan_as_null=False)
# TODO: only required if fill_value has a subset of the categories:
fill_value_col = fill_value_col.cat()._set_categories(
self.cat().categories)
cpp_replace.replace_nulls(result, fill_value_col)
result = result.replace(mask=None)
return self._mimic_inplace(result, inplace)