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)