def fillna( self: NDArrayBackedExtensionArrayT, value=None, method=None, limit=None ) -> NDArrayBackedExtensionArrayT: value, method = validate_fillna_kwargs( value, method, validate_scalar_dict_value=False ) mask = self.isna() # error: Argument 2 to "check_value_size" has incompatible type # "ExtensionArray"; expected "ndarray" value = missing.check_value_size( value, mask, len(self) # type: ignore[arg-type] ) if mask.any(): if method is not None: # TODO: check value is None # (for now) when self.ndim == 2, we assume axis=0 func = missing.get_fill_func(method, ndim=self.ndim) new_values, _ = func(self._ndarray.T.copy(), limit=limit, mask=mask.T) new_values = new_values.T # TODO: PandasArray didn't used to copy, need tests for this new_values = self._from_backing_data(new_values) else: # fill with value new_values = self.copy() new_values[mask] = value else: # We validate the fill_value even if there is nothing to fill if value is not None: self._validate_setitem_value(value) new_values = self.copy() return new_values
def fillna(self: NDArrayBackedExtensionArrayT, value=None, method=None, limit=None) -> NDArrayBackedExtensionArrayT: value, method = validate_fillna_kwargs(value, method) mask = self.isna() value = missing.check_value_size(value, mask, len(self)) if mask.any(): if method is not None: # TODO: check value is None # (for now) when self.ndim == 2, we assume axis=0 func = missing.get_fill_func(method, ndim=self.ndim) new_values, _ = func(self._ndarray.T.copy(), limit=limit, mask=mask.T) new_values = new_values.T # TODO: PandasArray didn't used to copy, need tests for this new_values = self._from_backing_data(new_values) else: # fill with value new_values = self.copy() new_values[mask] = value else: new_values = self.copy() return new_values
def fillna(self, value=None, method=None, limit=None): """ Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, array-like If a scalar value is passed it is used to fill all missing values. Alternatively, an array-like 'value' can be given. It's expected that the array-like have the same length as 'self'. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap. limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Returns ------- ExtensionArray With NA/NaN filled. """ value, method = validate_fillna_kwargs(value, method) mask = self.isna() # error: Argument 2 to "check_value_size" has incompatible type # "ExtensionArray"; expected "ndarray" value = missing.check_value_size( value, mask, len(self) # type: ignore[arg-type] ) if mask.any(): if method is not None: func = missing.get_fill_func(method) new_values, _ = func(self.astype(object), limit=limit, mask=mask) new_values = self._from_sequence(new_values, dtype=self.dtype) else: # fill with value new_values = self.copy() new_values[mask] = value else: new_values = self.copy() return new_values
def fillna( self: NDArrayBackedExtensionArrayT, value=None, method=None, limit=None ) -> NDArrayBackedExtensionArrayT: value, method = validate_fillna_kwargs(value, method) mask = self.isna() value = missing.check_value_size(value, mask, len(self)) if mask.any(): if method is not None: func = missing.get_fill_func(method) new_values = func(self._ndarray.copy(), limit=limit, mask=mask) # TODO: PandasArray didn't used to copy, need tests for this new_values = self._from_backing_data(new_values) else: # fill with value new_values = self.copy() new_values[mask] = value else: new_values = self.copy() return new_values